# seminars

date: 2011-04-07
speaker: Dr. Massimiliano Albanese, University of Maryland, College Park
title: A Framework for Scalable Analysis of Attack Scenarios
location: Nguyen Engineering Building, Room 5117
start_time: 11:00 am
end_time: 12:00 pm
bio: TBD
abstract:
Attack graphs have been widely used for attack modeling, alert
correlation, and prediction, but traditional approaches have some
limitations with respect to scalability, impact analysis, and
attacker's behavior. In this talk, I will present a novel framework to
analyze massive amounts of security alerts in real-time, and estimate
the impact of current and future attacks, based on a better knowledge
of the attacker's behavior.

First, I will introduce the notion of generalized dependency graph,
which captures how network components depend on one another, and how
the services offered by an enterprise depend on the underlying
infrastructure. Second, I will present an extension of the classical
definition of attack graph, encoding probabilistic knowledge of the
attacker's temporal behavior. Third, I will introduce attack scenario
graphs, which combine dependency and attack graphs, bridging the gap
between known vulnerabilities and the services that could be
ultimately affected by the corresponding exploits. Finally, I will
present a data structure to index large volumes of security alerts,
and an efficient algorithm to update the index in real-time, as well
as an algorithm to predict and rank future scenarios, based on the
current situation.

In conclusion, I will show that the proposed approach scales well for
large graphs and large volumes of alerts. In practice, the proposed
framework can provide security analysts with actionable intelligence
about the current cyber situation, enabling them to make more informed
decisions.


date: 2018-02-23
speaker: Dr. Jessica Vitak, University of Maryland, College Park
title: Privacy, security, and ethical challenges in the era of ubiquitous data collection
location: Engineering Building, Room 1602
start_time: 11:00 am
end_time: 12:00 pm
abstract:
Over the past decade, the Internet of Things has pushed its way into our workplaces and homes by making regular products "smarter." We now wear watches to track our steps, heart rate, and sleep patterns. Our thermostats learn over time about our heating and cooling preferences. Our refrigerator can detect when we run out of milk. And our intelligent personal assistants passively listen for a voice cue ("Alexa!") to respond to our questions and commands. In many ways, we are living in the science fiction future we dreamed of decades ago. On the other hand, the influx of devices meant to collect constant data about your movement and location, health, and purchasing patterns raise significant questions about the privacy and security of that data. In this talk, I'll share early results from two NSF grants, one looking at privacy and surveillance on smartphones and intelligent personal assistants like Siri and Alexa, and the other collaborative project on pervasive data ethics. I'll also raise questions for researchers working in this space to consider as they work with large, public datasets to ensure they are taking adequate steps to protect the data and the users behind that data.

bio:
Jessica Vitak (PhD, Michigan State University) is an assistant professor in the College of Information Studies at the University of Maryland and associate director of the Human Computer Interaction Lab (HCIL). Her research evaluates the benefits and drawbacks of mediated communication technologies by focusing on the role that social and technical affordances shape interactions online. Specifically, she focuses on questions around data privacy and security, as well as pervasive data ethics, around the generation, collection, and analysis of large-scale user data. She is currently PI or Co-PI on three federal grants on these topics (NSF-SES-1640640, NSF-IIS-1704369, and IMLS-LG-81-16-0154-16). For more information, see https://pearl.umd.edu.


date: 2018-03-22
speaker: Dr. Noseong Park, University of North Carolina, Charlotte
title: Generative Model and Knowledge Graph Query Processing and their Applications to Cybersecurity
location: Engineering Building, Room 4801
start_time: 11:00 am
end_time: 12:00 pm
abstract:
In the big data era, sharing data with partners or releasing data to the public frequently occurs.  Privacy should be the top priority in the sharing process to protect people who were willing to share valuable information. Anonymization techniques remove identifiers (such as social security numbers) and modify quasi-identifiers (such as gender, ZIP code, age, occupation, and so forth). However, other sensitive attributes that are neither identifiers nor quasi-identifiers are often disclosed without any modification. If adversaries possess background knowledge or other information sources, then they can recover the identification of records

We present a data synthesis method based on generative adversarial networks (GANs). Our method, named table-GAN, is specialized for synthesizing tables that contain categorical, ordinal, discrete, and continuous values. Tables synthesized by table-GAN have global statistics similar to that of the original table even though they differ at the record level. The main advantages of generating synthetic tables are twofold: 1) real records are not disclosed and 2) machine learning models trained using very carefully synthesized tables show behavior similar to that of models trained using the original table; they can replace each other (i.e., model compatibility). We also show that table-GAN is strong against membership attacks to infer about the original table after observing generated tables.

Knowledge graphs are popular for various applications such as question answering, fact checking, and so forth. SPARQL is a standard knowledge graph query language. We present a method to quickly process aggregated top-k SPARQL queries that involve GROUP BY, ORDER BY, and LIMIT terms. We also present our recent efforts to construct cybersecurity knowledge graphs for cyber threat intelligence where machines can assist security analysts by answering questions such as ``What are the most vulnerable software packages installed in my network and what are the best substitutes for those packages?''.

bio:
Noseong Park is an Assistant Professor of Software and Information Systems at the University of North Carolina, Charlotte. He received his PhD degree from Computer Science at the University of Maryland, College Park. He has worked extensively at the intersection of artificial intelligence, data mining, network analysis and cybersecurity.


date: 2018-03-26
speaker: Dr. Thanh H. Nguyen, University of Michigan
title: Addressing Real-world Societal Challenges: Advanced Game-Theoretic Models and Algorithms
location: Engineering Building, Room 2901
start_time: 11:00 am
end_time: 12:00 pm
abstract:
This talk will cover my research in AI, with a focus on Multi-Agent Systems, for solving real-world societal problems, particularly in the areas of Sustainability, Public Safety and Security, Cyber-security, and Public Health. In these problems, strategic allocation of limited resources in an adversarial environment is an important challenge which involves complex human decision making, a variety of uncertainties, and exponential action spaces. In this talk, I will present my research in developing advanced
game-theoretic models and algorithms for tactical allocation decisions in these problems. In particular, I will outline three main contributions of my research: (i) learning new behavioral models of human decision-making for adversarial reasoning - I will discuss results from applying these models to both human subjects data from the lab and real-world data; (ii) developing robust game-theoretic algorithms, which handle a variety of uncertainties in security and are applied to domains in which data is not available to 
generate a prior distribution of uncertainties; and (iii) designing scalable game-theoretic algorithms, which address the challenge of exponential action and state spaces in complex cybersecurity problems. Finally, I will briefly
introduce the real-world deployed application PAWS (Protection Assistant for Wildlife Security), which incorporates my models and algorithms, for wildlife protection.

bio:
Thanh Nguyen is a Postdoctoral Researcher in the Department of Computer Science & Engineering at the University of Michigan. She received her Ph.D. from the Department of Computer Science at the University of Southern California (USC) in Summer 2016. While at USC, she was part of the USC Center for Articial Intelligence in Society. Her work in the area of Articial Intelligence is motivated by real-world societal problems, particularly in the areas of Sustainability, Public Safety and Security, Cybersecurity, and Public Health. Her recent awards include the Deployed Application Award (IAAI 2016) and Runner-up of the Best Innovative Application Paper Award (AAMAS 2016). Thanh has published extensively in several leading conferences in Articial Intelligence, including IJCAI, AAAI, AAMAS, and GameSec. She has contributed to build the real-world wildlife-protection application PAWS (Protection Assistant for Wildlife Security),which has been extensively used by NGOs in conservation areas in multiple countries.


date: 2018-03-29
speaker: Professor Xiaoyang Sean Wang
title: Supporting Smart Exploratory Data Analysis
location: Engineering Building, Room 4801
start_time: 11:00 am
end_time: 12:00 pm
abstract:
Data analysis is experiencing two changes: (1) The data used in analysis is changing from mostly domain-specific data to data from multiple, often unfamiliar, sources; (2) Data analysis practitioners are changing from only computer scientists or statisticians and other technical experts to experts in the application domains. Therefore, how to provide tools that will help user in their analysis tasks has become an important research area. This talk will discuss the possibility in providing such tools that are collectively called an "exploratory data analysis system”, and report several preliminary attempts and related research questions.

bio:
Xiaoyang Sean Wang is Professor at the School of Compute Science of Fudan University. He earned his PhD degree in Computer Science from the University of Southern California in 1992. Before joining Fudan University in 2011, he was the Dorothean Chair Professor in Computer Science at the University of Vermont between 2003-2011 and Assistant/Associate Professor in the Department of Information and Software Engineering at George Mason University during 1992-2003. During 2009-2011, he served as a Program Director at the National Science Foundation in the Division of Information and Intelligent Systems. He has published widely in the general area of databases and information security, and was a recipient of the US National Science Foundation Research Initiation and CAREER awards. He is currently serving on the IEEE ICDE steering committee, as co-Editor in Chief of the Springer Data Science and Engineering journal, and on the editorial board of IEEE Transactions on Cloud Computing.


date: 2018-04-03
speaker: Prof. Ehab Al-Shaer, University of North Carolina, Charlotte
title: Towards Autonomous Cyber Defense for Effective Attack Mitigating
location: Engineering Building, Room 1602
start_time: 11:00 am
end_time: 12:00 pm
abstract:
With the increasing frequency and impact of cyber attacks, cyber warfare has become a major threat to national security. Despite the progress made in cybersecurity, the state of the art is still far from providing sufficient protection not only for enterprises but also for critical infrastructure services. Cyber risk has been growing faster than cyber defense capabilities due to many reasons including the following: (1) the exponential increase of a system’s attack surface as a result of the proliferation of connected devices, such as IoT devices; (2) the reliance on human in the cyber defense sense-making and decision-making processes creates bottleneck in security analysis; (3) the asymmetric cyberwarfare due to static cyber configurations that allows adversaries to discover, plan, launch and propagate their attacks effectively and timely; (4) the lack of metrics to assess the effectiveness of cybersecurity analytics and defenses; and (5) the lack of automated decision-support techniques and tools that can dynamically characterize cyber risk posture, and create mitigation strategies.
This talk will present an overview of our research contributions to address these challenges by developing various techniques for automated cyber defense with provable and measurable security and resiliency properties, and minimal human involvement. This talk will cover our work in cyber defense automation in four key areas of our research including: (1) optimization of risk mitigation planning for security hardening, (2) cyber mutation for attack deterrence, deception, and (3) extracting adversary actions and inferring attack patterns from unstructured text of cyber threat intelligence reports, and (4) policy specification and verification for automated course-of-actions generation. At the end of the talk, I will present my on-going work and future research vision to enable cyber to observe, understand, investigate, act, and evolve in order to mitigate cyber-attacks at real-time. 

bio:
Ehab Al-Shaer is a Professor in Computer Science, the Director of the Cyber Defense and Network Assurability (CyberDNA) Center, and the director of NSF IUCRC Center on Configuration Analytics and Automation in the University of North Carolina Charlotte. Prof. Al-Shaer's research area expertise includes formal verification and synthesis of security configuration, data-driven analytics of cyber threat intelligence, automated adaptive cyber defense, cyber agility (deterrence and deception), and resilience of cyber and cyber-physical (smart grids and IoT) systems. He was designated by the Department of Defense (DoD) as a Subject Matter Expert (SME) on cybersecurity analytics and automation in 2011, and he was awarded the IBM Faculty Award in 2012, and UNC Charlotte Faculty Research Award in 2013. He is a lead researcher in the cyber resilience of NSA Science of Security (SOS) Lablet.  Prof. Al-Shaer has edited/co-edited more than 10 books and published more than 200 refereed journals and conference papers in this area. He has a number of patents and he has lead several technology transfer projects. He is also an advisory board member for a leading company in cybersecurity automation. Prof. Al-Shaer received funding from many government agencies including NSF, NSA, ARO, AFRL, and many industry partners including Cisco, Intel, IBM, Bank of America, Duke Energy, BB&T, Depository Trust & Clearing Corporation, RTI International, CIS, and others.


date: 2018-03-27
speaker: Dr. Edoardo Serra, Boise State University
title: Lateral Smart Network Security
location: Engineering Building, Room 4801
start_time: 11:00 am
end_time: 12:00 pm
abstract:
Nowadays, network attacks are more and more sophisticated and target specialized networks such as cyber-physical systems, IoT device networks, and Enterprise networks. Signature and anomaly-based Intrusion Detection Systems (IDSs) are commonly used to protect such networks. However, IDSs do not represent an effective solution. In fact, IDSs are not always able to recognize threats and generate a huge amount of false alarms. This happens because of sophisticated attacks such as Zero-Day attacks, Advanced Persistent Threats (APTs), Insider Threats, etc., or because non-common benign behaviors are recognized as malicious.  Moreover, teams of security analysts in bigger enterprise networks are not enough in number to keep up with these huge flows of generated alerts.  

In this talk, I will present non-conventional data-driven techniques to mitigate this problem such as:

1. How to measure computer security skills by using (in a collaborative way)
historical alert data from different computers. As this measure is proved to
correlate with the likelihood of being a victim of particular malware
infections, it can be used to prioritize alerts for further investigation.

2. How to model the behavior of an attacker and using the network structure
and the assets of the network to produce better and distributed ways to
assign alerts to security analysts over the time.

3. How honey-networks can be automatically deployed and used to observe
network intrusions, and how to use reinforcement learning to model the
learning abilities of an attacker trapped in a honey-network.

bio:
Dr. Serra received his Ph.D. degree in Computer Science Engineering from the
University of Calabria, Italy, in 2012. During his Ph.D. he was a Visiting Researcher at the Computer Science Department of the University of California - Los Angeles.  After the Ph.D. degree, Dr. Serra was a postdoc at the University of Calabria and a Research Associate at the University of Maryland (2013-2015). From Aug. 2015, he is Assistant Professor in the Computer Science Department at Boise State University (BSU). His research interests are in the field of Data Science with applications in Cyber-security. His background also includes machine learning, optimization, databases, and artificial intelligence. His research is published in prestigious venues such as IEEE an ACM Transactions Journals and top-tier conferences, e.g., ESORICS and WSDM.


date: 2018-04-04
speaker: Prof. Ehab Al-Shaer, University of North Carolina, Charlotte
title: Formal- and Data-driven Analytics for High-Assurance Decision-Making in Cyber Defense
location: Engineering Building, Dean's Conference Room
start_time: 1:30 pm
end_time: 2:30 pm
abstract:
With the expositional growth of cyber complexity and connectivity, the potential of cyber misconfigurations, and novel/unknown attacks has been tremendously increasing. The operation of cyber and cyber-physical systems depends on thousands of highly inter-dependent configurations (e.g., rules and variables) to execute mission and enforce security policies. Moreover, the amount of vulnerability reports and cyber threat intelligence received every day is overwhelming for any team of experts. These challenges make real-time understanding of the threat space, identifying potential risk, and assessing the cyber security and resiliency capabilities based on its configuration extremely challenging and daunting tasks even for human experts.

In the first part, this talk will present automated configuration verification and synthesis to plan and enforce security and resiliency properties for Cyber, Energy Management Systems, and IoT using advanced formal methods based on SMT and bounded model checking. Then, we will present our recent data-driven approach (TTPDrill) using NLP and machine learning to automatically extract attack actions and infer attack patterns from unstructured text of Cyber Threat Intelligence (CTI) with high accuracy and without human assistant. TTPDrill provides great support for threat predictive/proactive analytics, and cyber hunting and investigation in cyber diagnosis and forensics. In the second part, this talk will present future research directions leveraging Mason strengths and capabilities to establish world-class research programs in cyber security and resiliency.

bio:
Ehab Al-Shaer is a Professor in Computer Science, the Director of the Cyber Defense and Network Assurability (CyberDNA) Center, and the director of NSF IUCRC Center on Configuration Analytics and Automation in the University of North Carolina Charlotte. Prof. Al-Shaer's research area expertise includes formal verification and synthesis of security configuration, data-driven analytics of cyber threat intelligence, automated adaptive cyber defense, cyber agility (deterrence and deception), and resilience of cyber and cyber-physical (smart grids and IoT) systems. He was designated by the Department of Defense (DoD) as a Subject Matter Expert (SME) on cybersecurity analytics and automation in 2011, and he was awarded the IBM Faculty Award in 2012, and UNC Charlotte Faculty Research Award in 2013. He is a lead researcher in the cyber resilience of NSA Science of Security (SOS) Lablet.  Prof. Al-Shaer has edited/co-edited more than 10 books and published more than 200 refereed journals and conference papers in this area. He has a number of patents and he has lead several technology transfer projects. He is also an advisory board member for a leading company in cybersecurity automation. Prof. Al-Shaer received funding from many government agencies including NSF, NSA, ARO, AFRL, and many industry partners including Cisco, Intel, IBM, Bank of America, Duke Energy, BB&T, Depository Trust & Clearing Corporation, RTI International, CIS, and others. 


date: 2018-04-12
speaker: Xiang Li, University of Florida
title: Mining in Big Networks with Incomplete Information
location: Engineering Building, Room 4801
start_time: 11:00 am
end_time: 12:00 pm
abstract:
Modern networked systems such as Internet of Things or Online Social Networks have growth to billion-scale with billions of nodes and edges. At the same time, most network analyses are conducted on incomplete information due to 1) collecting network data is expensive and time-consuming; and 2)
security and privacy concerns have made users limit the information being exposed publicly. With such big and incomplete data, it becomes very challenging to mine the billion-scale networks.

In this talk, I address the above problems via two primary approaches: 1) Develop novel dynamic sampling techniques with the performance bound guarantee to exactly perform the big data mining in billion-scale
networks; and 2) Develop active learning methods to support more accurate decisions where decisions are made based on the outcomes of previous decisions. To confirm the practical uses of these mathematical techniques, I develop the first almost exact solution to an NP-complete problem, Maximum-Influence,which can run on Twitter within 3 hours. The results of active learnings are used to not only unveil the near-optimal attack strategies of APTO, one of the most persistent attacks in OSNs, but also advance the
research front of adaptive stochastic optimization, including: active learning with batch, adaptive theory under the matroids intersection, and providing the first tool to analyze greedy algorithms for optimization
problems where the objective function is non-submodular. Towards the end of the talk, I will summarize my other research results in the field of Cyber Physical Systems and discuss about my future research agenda.

bio:
Xiang Li is currently a UF Informatics Institute Fellow and a Ph.D. candidate in the Computer and Information Science and Engineering department, University of Florida, expected to graduate in May 2018.
Her research interests at the intersection of cyber-security, data science, and highly scalable algorithms with applications in complex networking systems. More specifically, the focus of her research is to develop models, theories, and approximation algorithms for many computationally hard
problems in Online Social Networks, Device-to-Device Networks, Internet of Things, and Smart Grids. She has published 21 articles in various prestigious journals and conferences such as IEEE Transactions on
Mobile Computing, IEEE Transactions on Smart Grids, IEEE INFOCOM, IEEE ICDM, including one Best Paper Award at IEEE MSN 2014, and Best Paper Nominee at IEEE ICDCS 2017.

Xiang has served as a reviewer for several journals such as Journal of Combinatorial Optimization, and IEEE Transactions on Networks Science and Engineering. She is a recipient of many awards, including UF Graduate School Fellowship, UF Informatics Institute Fellowship, UF Outstanding Merits Award, Travel Grant Awards at IEEE INFOCOM, IEEE ICDCS, IEEE ICDM, and IEEE INFOCOM Best-in-Section-Presentation Award.


date: 2018-04-23
speaker: Prem Devanbu, Computer Science Department University of California at Davis
title: Studying and Exploiting the "naturalness" of code
location: Research Hall, Room 163
start_time: 10:00 am
end_time: 11:00 am
abstract:
While natural languages are rich in vocabulary  and grammatical flexibility, most human <utterances> are mundane and repetitive. This simple, repetitive structure has led to great advances in statistical NLP methods. 

At UC Davis, we have discovered that, despite the considerable power and  flexibility of programming languages, large software corpora are actually even more repetitive, than natural language. We were the first to show  that this “naturalness” of code could be captured in statistical models, and exploited for code prediction tasks and also for defect finding; we have also developed very fast, “lazy” language models that exploit the nested structure of code to yield entropy rates significantly below those achieved even by advanced deep  learning models. Ongoing projects are exploring code-deobfuscation, and other applications. In this talk, I will present some large-scale empirical studies of this phenomenon, and some recent results.

bio:
Prem Devanbu is a Professor at UC Davis. He was formerly a Research staff member at Bell Labs. He received B. Tech from IIT Madras before you were born, and his PhD from Rutgers University in 1994.  For the work described in this talk, he gratefully acknowledges support from NSF (#141472 CISE: LARGE: Collaborative grant "Exploiting the Naturalness of Software").


date: 2018-06-08
title: The Importance of Protecting Critical Transportation Infrastructure
location: Engineering Building, Room 4801
start_time: 11:00 am
end_time: 12:00 pm
speaker:
Michael Lowder, Retd. Director of Office of Intelligence, Security, & Emergency Response at  U.S. DOT; Principal, MWL Global Associates

abstract:
In today’s world, transportation is an essential element in everyday life. We all depend on it, in ways that we might not think about, until something goes wrong. Transportation is literally a life line in our society, in our way of life. Our survival, our well-being depend on it. How you say? Think about the food that we eat, how is it produced and get from the field to the processor, to the wholesaler, to the market, and ultimately to us, and our table? Think about what would happen if that transportation system was interrupted? The degree of that impact can vary depending upon a number of factors. What can cause that interruption? We often times think about a terrorist attack on our transportation systems. However, terrorism is not the only threat or risk to our transportation systems and the infrastructure they rely on. We face threats from accidents, natural disasters, man-made incidents, etc. Each of these threats can be just as catastrophic as terrorist attack. So, instead of focusing only on one threat, our protection must be ‘all hazard’ focused. An important concept I would like to talk about is called “Left of Boom”. This means before something happens, not after it happens. It means that we address something before it occurs instead of afterwards. Critical Infrastructure protection is not a “one size fit all” approach in this context. There are many tools that we can utilize to help us better protect our infrastructure, but to be really effective, we must address it “Left of Boom”.

bio:
Michael Lowder is an internationally recognized expert, with expertise in the Security, Intelligence, Crisis & Emergency Management, Critical Transportation Infrastructure Protection & Resilience, Counterterrorism and Business Continuity fields. In the past, he served as the Director of the Office of Intelligence, Security, & Emergency Response for DOT and earlier also served at FEMA. He is also a recipient of the Presidential Rank Award - Distinguished Executive. He has worked with government and private sector organizations in over 40 countries and brings a wealth of Senior Executive level leadership experience, expertise and guidance in the different areas of Crisis Management. He has provided crisis management leadership at the highest levels of government, and has guided many organizations through the development of highly effective business continuity programs. He has successfully lead efforts to expand the integration of intelligence and information sharing processes across both government and private sector organizations.


date: 2018-06-21
speaker: Themis Palpanas, Institut Universitaire de France (IUF) and Paris Descartes University
title: Data Series Management: Fulfilling the Need for Big Sequence Analytics
location: Engineering Building, Room 4801
start_time: 11:00 am
end_time: 12:00 pm
abstract:
There is an increasingly pressing need, by several applications in diverse domains, for developing techniques able to index and mine very large collections of sequences, or data series. Examples of such applications come from social media analytics and internet service providers, as well as from a multitude of scientific domains. It is not unusual for these applications to involve numbers of data series in the order of hundreds of millions to billions, which are often times not analyzed in their full detail due to their sheer size. However, no existing data management solution (such as relational databases, column stores, array databases, and time series management systems) can offer native support for sequences and the corresponding operators necessary for complex analytics. In this talk, we argue for the need to study the theory and foundations for sequence management of big data sequences, and to build corresponding systems that will enable scalable management and analysis of very large sequence collections. We describe recent efforts in designing techniques for indexing and mining truly massive collections of data series that will enable scientists to easily analyze their data. We discuss novel techniques that adaptively create data series indexes, allowing users to correctly answer queries before the indexing task is finished. Finally, we present our vision for the future in big sequence management research, including the promising directions in terms of storage, distributed processing, and query benchmarks.

bio:
Themis Palpanas is Senior Member of the Institut Universitaire de France (IUF), a distinction that recognizes excellence across all academic disciplines, and professor of computer science at the Paris Descartes University (France), where he is director of diNo, the data management group. He received the BS degree from the National Technical University of Athens, Greece, and the MSc and PhD degrees from the University of Toronto, Canada. He has previously held positions at the University of Trento, and at IBM T.J. Watson Research Center, and visited Microsoft Research, and the IBM Almaden Research Center. His interests include problems related to data science (big data analytics and machine learning applications). He is the author of nine US patents, three of which have been implemented in world-leading commercial data management products. He is the recipient of three Best Paper awards, and the IBM Shared University Research (SUR) Award. He is curently serving on the VLDB Endowment Board of Trustees, as an Editor in Chief for the BDR Journal, Associate Editor for VLDB 2019, Associate Editor in the TKDE, and IDA journals, as well as on the Editorial Advisory Board of the IS journal, and the Editorial Board of the TLDKS Journal. He has served as General Chair for VLDB 2013, Associate Editor for VLDB 2017, and Workshop Chair for EDBT 2016, ADBIS 2013 and ADBIS 2014, General Chair for the PDA@IOT International Workshop (in conjunction with VLDB 2014), and General Chair for the Event Processing Symposium 2009.


date: 2018-07-09
speaker: Prof. Valerie Shalin, Wright State University
title: Limitations of Data-driven Frequency-based Approaches for Social Computing
location: Engineering Building, Room 4801
start_time: 11:00 am
end_time: 12:00 pm
abstract:
Frequency counts dominate the measurement of social media behavior analytics.  Common computer science methods for the analysis of content such as visual analytics with word clouds, subjective text mining with sentiment analysis and topic classification all depend on frequency counts.  However, such methods have two deficiencies.  One of these is little to no theory regarding the mechanisms that generate the tallied observations.  The second deficiency, related to the first, is little to no foundation for scaling the observed frequency counts.  These limit the ability of social computing methods to address important practical problems, concerning for example the degree of distress in a compromised population or the extent of engagement in a political issue.  This presentation situates frequency metrics of social media content in the context of a model of dyadic communication.  The model focuses analysis on ratios of lexical choice rather than absolute frequency counts. This focus effectively supports practical applications such as comparative analytics of public response across critical world events, and reveals a role for so-called stop words.      

bio:
Professor Valerie L. Shalin earned her Ph.D. in Learning, Developmental and Cognitive Psychology from the University of Pittsburgh.  She currently serves in the department of Psychology at Wright State University, Dayton OH, where she is the Area Leader for the Human Factors focus in the Graduate HF/IO program.  Dr. Shalin is an active participant in Wright State’s Knowledge-enabled Computing Center of Excellence (Kno.e.sis). In addition to her role as a co-editor of the seminal volume on Cognitive Task Analysis, she is co-recipient with her student of the 2016 Human Factors prize on the topic of Big Data, recipient of the NASA Ames team award for contributions to the first Mars Exploration Rover Mission and has over 70 referred journal articles and conference/symposium papers. Her current and past work has been funded by grants from the Air Force, ARI, INRIA, ONR, NASA, and NSF, encompassing task domains such as space exploration, disaster response and medicine.  She has served on the editorial board for Human Factors and Journal of the Learning Sciences, refereed for twelve professional journals spanning psychology and engineering, and reviewed grant proposals for The US Army Medical Research and Material Command, Joint Congressional Projects, NSF and NASA. She has graduated thirteen Ph.D. students, all of whom focus on the analysis of human behavior to support the design of workplace technology and collaboration. 


date: 2018-10-22
speaker: Briland Hitaj, Sapienza University, Rome, Italy
title: GAN based Privacy Attacks on Decentralized Deep Learning
location: Research Hall, Suite 417
start_time: 11:00 am
end_time: 12:00 pm
abstract:
Deep Learning (DL) is the driving energy behind numerous breakthroughs done recently in various areas of computer science. Despite that, due to its need for powerful computational resources and particularly massive amounts of data, deep learning cannot be a feasible solution for entities lacking these requirements. To benefit from DL, data can be typically pooled in centralized datasets residing in third-party servers. However, this can pose several privacy risks on how this data is used, raising many concerns. Moreover, a centralized approach makes deep learning impractical for entities dealing with sensitive information such as governmental agencies, hospitals, banks and more. As a viable solution to these problems, researchers have proposed decentralized learning techniques. These approaches allow one to benefit from the perks of deep learning while keeping their data local (i.e., private). 

In this talk, I will show that decentralized deep learning can in fact prove to be a more dangerous threat to privacy, than the centralized one. Exploiting the active nature of the learning process, we were able to develop a novel real-time active inference attack on decentralized learning mechanisms using a recent, yet extremely popular innovation in deep neural networks, namely Generative Adversarial Networks (GANs). Employing GANs, we were able to generate prototypical samples of the targeted training set that was meant to be private. Development of countermeasures against our GAN Attack remains an open problem.

bio:
Briland Hitaj is a PhD Student at Computer Science Department of Sapienza University of Rome. He is a member of the Cyber-Security (ICSecurity)  group led by Prof. Luigi V. Mancini. Currently, he is a Visiting Research Scholar at the Computer Science Department of Stevens Institute of Technology working with Prof. Giuseppe Ateniese at the intersection of cybersecurity and deep learning.

His research interests include cyber intelligent agents, deep learning, privacy and security in distributed systems, distributed privacy-preserving machine learning, application and incorporation of deep learning in cyber-security domain.


date: 2018-09-21
speaker: Adam Smith, Boston University
title: Privacy, Stability and Generalization
location: Research Hall, Room 163
start_time: 11:00 am
end_time: 12:00 pm
abstract:
Consider an agency holding a large database of sensitive personal information -- medical records, census survey answers, web search records, or genetic data, for example. The agency would like to discover and publicly release global characteristics of the data while protecting the privacy of individuals' records. I will begin by discussing what makes this problem difficult, illustrating some challenges via recent work on membership inference attacks. Motivated by this, I will present differential privacy, a rigorous definition of privacy in statistical databases that is now widely studied, and increasingly used to analyze and design deployed systems. Finally, I will explain how differential privacy is connected to a seemingly different problem: understanding statistical validity in "adaptive data analysis", the practice by which insights gathered from data are used to inform further analysis of the same data set. I'll show how limiting the information revealed about a data set during analysis allows one to control bias, and why differential privacy provides a particularly useful tool for limiting revealed information. 

bio:
Adam Smith is a professor of computer science at Boston University. From 2007 to 2017, he served on the faculty of the Computer Science and Engineering Department at Penn State. His research interests lie in data privacy and cryptography, and their connections to machine learning, statistics, information theory, and quantum computing. He obtained his Ph.D. from MIT in 2004 and has held postdoc and visiting positions at the Weizmann Institute of Science, UCLA, Boston University and Harvard. He received a Presidential Early Career Award for Scientists and Engineers (PECASE) in 2009; a Theory of Cryptography Test of Time award in 2016; and the 2017 Gadel Prize. These last two awards were joint with C. Dwork, F. McSherry, and K. Nissim.


date: 2018-10-05
speaker: Romit Roy Choudhury, University of Illinois at Urbana-Champaign
title: The Internet of Acoustic Things (IoAT)
location: Research Hall, Room 163
start_time: 11:00 am
end_time: 12:00 pm
abstract:
The convergence in sensing, computing, and communications — that perhaps started with smartphones — is now percolating at finer granularities. A lot of recent activity has centered around acoustic systems, including new kinds of ear phones, voice enabled devices (e.g., Amazon Echo), vibration based communication, acoustic localization,  etc. A new eco-system is on the horizon, popularly being called as the Internet of Acoustic Things (IoAT). This talk will explore ideas, challenges, and opportunities underlying various IoAT systems, while also paying attention to security and privacy threats that arrive with such applications.

bio:
Dr. Romit Roy Choudhury is a Jerry Sanders Scholar and Professor of ECE and CS at UIUC. He joined UIUC from Fall 2013, prior to which he was an Associate Professor at Duke University. His research interests are in wireless sensing and networking. Along with his students, he received a few research awards, including the ACM Sigmobile Rockstar Award, the ACM MobiSys 2017 Best Paper Award, the Hoffmann Krippner Innovations Award, the NSF CAREER Award, etc. Visit Romit’s Systems Networking Research Group (SyNRG) at http://synrg.csl.illinois.edu.


date: 2018-10-11
speaker: Dr. Dustin Dannenhauer, Naval Research Lab
title: Self Monitoring Goal Driven Autonomous Agents
location: Engineering Building, Room 3507
start_time: 11:00 am
end_time: 12:00 pm
abstract:
Robust autonomy remains a difficult problem for complex, partially observable, and real-time environments such as robotics (e.g. mars rovers, underwater vehicles) and complex video games including Starcraft and Minecraft. Goal reasoning is a growing research area for controlling agents operating in these kinds of environments. Goal reasoning agents reason over their goals in response to unexpected changes in the environment. By monitoring their own execution, agents can detect if a situation is unexpected and if it will prevent them from achieving their goals. My research introduced new approaches for detecting unexpected situations (including failures) in the environment and in an agent's own cognitive processes via monitoring their own cognition (i.e., meta-cognition). In this talk I will give a brief background on goal reasoning and automated planning, walk-through an example of a construction agent in the Minecraft video game, highlight my work on anomaly detection for plan execution in goal driven autonomy agents, and conclude with exciting areas for future work.

bio:
Dr. Dustin Dannenhauer is a National Research Council Post-doctoral Researcher at the Naval Research Laboratory in Washington, D.C. He received his Ph.D. in Computer Science from Lehigh University in 2017 and his B.S. in Computer Science from Indiana University Bloomington in 2012. His research investigates autonomous agents that monitor their own execution for unexpected behavior. His work includes how semantically inferred concepts can be used as expectations in large, complex domains like real-time strategy games; how expectations can be generated and used to monitor plans produced from a hierarchical task network planner; and how expectations at the metacognitive level can be used to identify failures in cognition. Dr. Dannenhauer has been one of the core developers of the Metacognitive Integrated Dual-Cycle Architecture (MIDCA): a cognitive architecture with explicit metacognitive capabilities. He has published in top venues for Artificial Intelligence research including IJCAI, AAAI, ICCBR and Advances in Cognitive Systems.


date: 2018-10-12
speaker: Dr. Nikolaos Bourbakis, Wright State University
title: The Impact of AI and IT Synergies on Healthcare and Biomedicine
location: Engineering Building, Room 4201
start_time: 11:00 am
end_time: 12:00 pm
abstract:
In the last two decades the maturity of two major scientific events has significantly impacted the Academic and Industrial evolution. The first event is the wide acceptance of Artificial Intelligence (AI) in the Applications domain. The second is the increased computational power of IT tools (Data Mining, Big Data) with application to very large scale demands. As a result of this evolution, singular methodologies have been replaced by Synergies of methodologies for robust solutions to complex and large scale problems. Thus, this talk will address how Synergies of AI and IT Methodologies may offer better solutions for problems on Healthcare & Biomedicine.

bio:
Dr. Nikolaos Bourbakis (IEEE Fellow-1996) currently is an Ohio Board of Regents Distinguished Professor of Information Technology College of Engineering, j.a. Professor of Geriatrics School of Medicine,  and the Director of the Center of Assistive Research Technologies (CART) at Wright State University, Dayton OH. He is the Founder and the EIC of the Int. Journal on AI Tools (World Scientific Pub., 1992-present) and the Int. Journal on Monitoring and Surveillance Tech Research (IGI-Global, Publ., 2013-present), the Founder and General Chair of several International IEEE Computer Society Conferences (ICTAI, BIBE, IISA), Symposia and Workshops. He pursues research in Assistive Technologies, Applied AI, Bioengineering, Cyber Security, and Parallel/Distributed Processing funded ($15M) by USA and European government and industry. He has extensively published in IEEE/International Journals and IEEE Conferences (430 pubs) and graduated 29 PhD students. His research work has been internationally recognized and won several prestigious awards. Some of them are: IEEE Computer Society Technical Research Achievement Award 1998; ASC Outstanding Scientists & Engineers Research Award-2005; Dr. F. Russ IEEE Biomedical Engineering award, Dayton Ohio, 2010; Most Cited Article in Pattern Recognition Journal, 2006-2010; IEEE ICTAI CV Ramamoorthy best paper Award 2012 & 2015; Recognition Award for his Outstanding Scholarly Achievements and Contributions in the field of Computer Science, UNIPI-GR, 2013; IEEE EMBS-GR Award of Achievements, 2013; IEEE Computer Society 28 years ICTAI Outstanding Service & Leadership Recognition, 2016, and IEEE AIKE Keynote Speaker Award 2018, others.


date: 2018-10-24
speaker: Jim Jones, George Mason University
title: Digital Artifact Persistence, Extraction, Analysis, and Manipulation
location: Research Hall, Room 163
start_time: 11:00 am
end_time: 12:00 pm
abstract:
Digital data dies an uncertain death. Delete a file today, and the content might be entirely destroyed immediately, or some of it may survive for a few seconds, hours, days, or longer. For a forensic investigator, this is good news – residual fragments of a deleted file might be recoverable days, months, even years after the file was deleted. But why do some fragments persist while others do not, what can we infer from the fragments that we can recover, and can such fragments be artificially created or modified? In this talk, I will discuss our approach, tools, and work to understand the patterns and mechanisms of deleted digital data decay, analysis and interpretation of recovered fragments, and techniques for the manipulation of digital fragments under various circumstances.

bio:
Jim Jones is an Associate Professor in the Digital Forensics and Cyber Analysis program within the ECE Department. Dr. Jones earned his Bachelor's degree from Georgia Tech (Industrial and Systems Engineering, 1989), Master's degree from Clemson University (Mathematical Sciences, 1995), and PhD from George Mason University (Computational Sciences and Informatics, 2008). He has been a cyber security practitioner, researcher, and educator for over 20 years. During that time, he has led and performed network and system vulnerability and penetration tests, led a cyber incident response team, conducted digital forensics investigations, and taught university courses in cyber security, penetration testing, digital forensics, and programming. Past and current funded research sponsors include DARPA, IARPA, DHS, NSF, and DoD. His research interests are focused on digital artifact persistence, extraction, analysis, and manipulation.


date: 2018-10-19
speaker: Avesta Sasan, George Mason University
location: Engineering Building, Room 4201
start_time: 2:00 pm
end_time: 3:00 pm
title:
Designing and Fabricating Trusted Hardware in an Untrusted Manufacturing Supply Chain, a Seminar on Hardware Level Attacks and Defenses 

abstract:
Cost of building a new semiconductor fab is estimated to be $8.0 billion in 2018, with large recurring maintenance costs, a figure that is expected to sharply increase as technology migrates to smaller nodes. To reduce the fabrication cost, and for economic feasibility, most of the manufacturing and fabrication in advanced technology nodes is pushed off U.S. shore. However, many offshore fabrication labs are untrusted. Considering that the hardware is the root of trust in a computing system, this global and untrusted supply chain model has raised concern over potential adversarial attacks with an intimate knowledge of the fabrication process, the ability to modify and expand the design prior to production, and an unavoidable access to the fabricated chips during testing.  In this talk, I discuss various trust and security challenges in the untrusted manufacturing supply chain, and provide an overview of the range of defined projects and suggested solutions that my team at GATE laboratory at GMU has been investigating.

bio:
Dr. Sasan received his B.Sc. in Computer Engineering from the University of California Irvine in 2005 with the highest honor (Summa Cum Laude). He then received his M.Sc. and his Ph.D. in Electrical and Computer Engineering from the University of California Irvine in 2006 and 2010 respectively. In 2010, Dr. Sasan joined the Office of CTO in Broadcom Co. working on the physical design and implementation of ARM processors, serving as physical designer, timing signoff specialist, and the lead of signal and power integrity signoff in this team. In 2014 Dr. Sasan was recruited by Qualcomm office of VLSI technology. In this role, Dr. Sasan developed different methodology and in-house EDAs for accurate signoff and analysis of hardened ASIC solutions. Dr. Sasan joined George Mason University in 2016, and he is currently serving as an Associate Professor in the Department of Electrical and Computer Engineering. Dr. Sasan research spans low power design and methodology, hardware security, accelerated computing, approximate computing, near threshold computing, neuromorphic computing, and the Internet of Things (IoT) solutions.


date: 2018-10-18
speaker: Wilbur Shi, Princeton University
title: Distributed Optimization and Resource Allocation: Algorithms and the Mirror Relation
location: Engineering Building, Room 2901
start_time: 11:00 am
end_time: 12:00 pm
abstract:
In this talk, we discuss the problems of consensus optimization and resource allocation, and how to solve them in a decentralized manner. These two problems are known to be closely related to empirical risk minimization in machine learning and management problems in operations research, respectively. By saying “decentralized”, we mean that the tasks are to be completed over a set of networked agents in which each agent is able to communicate with adjacent agents. For both problems, every agent in the network wants to collaboratively minimize a function that involves global information, while only a piece of information is available to each of them. Specifically, we will first introduce these two problems in the context of decentralized/distributed optimization, review the literatures for both problems, and then study the interesting “mirror relation” between them. Afterwards, we will enumerate a few state-of-the-art algorithms for solving the decentralized consensus optimization problem, and then correspondingly develop a few algorithms for solving the decentralized resource allocation problem based on the “mirror relationship”. Finally, we provide some numerical experiments to demonstrate the efficacy of the concerned algorithms and validate the methodology of using the “mirror relation”.

bio:
Wei (Wilbur) Shi is currently a postdoc at the ECE of Princeton University working with Dr. Mung Chiang and Dr. Yuxin Chen. He was a postdoc at Arizona State University, Boston University, and University of Illinois at Urbana-Champaign under the supervision of Dr. Angelia Nedich and Dr. Alex Olshevsky. His current research interests distribute in optimization, learning, and control, and applications in cyber physical systems. He received a Young Author Best Paper award from the IEEE Signal Processing Society in 2018.


date: 2018-11-05
speaker: Linton Wells II, George Mason University
title: Building Cyber Resilience in the "Age of Accelerations"
location: Engineering Building, Room 4705
start_time: 2:00 pm
end_time: 3:00 pm
abstract:
Accelerating technological and social changes are putting businesses, governments, and social compacts under siege everywhere. Interconnected shocks, some from cyberspace, challenge assumptions and best practices. Given the pace of change, cyber resilience is a better approach to countering these than cybersecurity. Security is largely about “locking up and hunkering down,” while resilience is about achieving the organization’s goals under any level of shock, fighting back, and emerging stronger.  Resilience includes two parts: operational resilience based on the readiness and agility of networks and systems, and cultural resilience—is the organization willing to fight back? This talk will address ways to become more cyber resilient amidst these accelerations.

bio:
Dr. Linton Wells II is Executive Advisor to the C4I & Cyber Center in VSE.  He focuses on links between policy, technology and decision-making, especially in building resilience and cybersecurity.  He has extensive civilian government leadership experience in national security affairs, including service as acting Assistant Secretary of Defense for Networks and Information Integration and Department of Defense (DoD) Chief Information Officer (CIO), as well as command, control, communications and intelligence (C3I) and civil-military relations. From 2010 to 2014 he led the Center for Technology and National Security Policy at the National Defense University.  He was a career naval officer.  He also advises VSE’s Community Resilience Laboratory. See also <https://en.wikipedia.org/wiki/Linton_Wells_II>


date: 2018-11-19
speaker: Ken Hintz, George Mason University
title: Information Based Sensor Management for ISR
location: Engineering Building, Room 4705
start_time: 2:00 pm
end_time: 3:00 pm
abstract:
Management of systems of heterogeneous sensors for performing situation assessment leading to actionable situation awareness is a ubiquitous and increasingly complex problem.  Of particular difficulty is the integration of cyber and social network data with conventional physics based observations.  Conventional optimization criteria are based on ad hoc measures consisting of weighted sums of non-commensurate criteria.  This talk partitions the problem of sensor management into six orthogonal components and two information measures, situation information and sensor information.  In combination with a mission value decomposition called goal lattice, the expected information value rate is used to manage sensors.

bio:
Dr. Ken Hintz has been an Associate Professor in the Department of Electrical and Computer Engineering at George Mason University since 1987.  He designed and established the Bachelor and Masters in Computer Engineering Degree Programs at GMU and teaches courses in sensor engineering, image processing, and computer engineering. 
Dr. Hintz' current research interest is in Orchestrated Resource Management (ORM) and Information Based Sensor Management (IBSM) supported by the Naval Postgraduate School.  He also developed a new method for pre-shot detection of barreled weapons based on his discovery of cavity induced modulation (CIM). 
Before joining GMU, Dr. Hintz was with the Naval Surface Warfare Center, Dahlgren, VA, working in electronic warfare and radar signal processing where he designed and built the original AN/ULQ-16 pulse analyzer.  Prior to working at NSWC, Dr. Hintz was with the U. S. Navy as a designated Naval Aviator stationed for 3 years in Rota, Spain flying Electronic Warfare Reconnaissance with Fleet Air Reconnaissance Squadron Two (VQ-2). During that time he became designated Electronic Warfare Aircraft Commander (EWAC) in both the EC-121 and EP-3E aircraft.  Dr. Hintz holds 24 patents, 9 patents pending, is a Fellow of SPIE, a Senior Life Member of IEEE, and lead author on a book on Microcontrollers.  He received his B.S. degree in Electrical Engineering from Purdue University, West Lafayette, Indiana in 1967 and his M.S. and Ph.D. degrees in Electrical Engineering from the University of Virginia in 1979 and 1981 respectively. 


date: 2018-11-09
speaker: Ricardo Baeza-Yates, NTENT & Northeastern University
title: Explainable AI
location: Research Hall, Room 163
start_time: 11:00 am
end_time: 12:00 pm
abstract:
The main problem of deep learning is that most applications of this technique are black boxes and hence is difficult to explain why a decision was taken. However, in many applications that affect people, the decision must be explained for legal (e.g., GDPR, the new privacy law of the European Union) and/or ethical reasons (gender or racial bias). We cover the main current techniques to explain machine learning models as well as the challenges involved, ending with best practices and an analysis of the future. 

bio:
Ricardo Baeza-Yates areas of expertise are web search and data mining, information retrieval, data science and algorithms. Since June 2016, he is CTO of NTENT, a semantic search technology company based in California, USA. He is also the Director of Data Science Programs at Northeastern University, Silicon Valley campus, since August 2017. Before he was VP of Research at Yahoo Labs, based in Barcelona, Spain, and later in Sunnyvale, California, from January 2006 to February 2016. He obtained a Ph.D. in CS from the University of Waterloo, Canada, in 1989. He is co-author of the best-seller Modern Information Retrieval textbook published by Addison-Wesley in 2011 (2nd ed), that won the ASIST 2012 Book of the Year award. From 2002 to 2004 he was elected to the Board of Governors of the IEEE Computer Society and between 2012 and 2016 was elected for the ACM Council. In 2009 he was named ACM Fellow and in 2011 IEEE Fellow, among other awards and distinctions.


date: 2018-11-08
speaker: Milan P. Yager, American Institute for Medical & Biological Engineering
title: Falling Out of Love with Science
location: Research Hall, Room 163
start_time: 12:00 pm
end_time: 1:00 pm
abstract:
In the halls of Congress there is widespread agreement about the role of R&D in the success of the America’s most innovative corporations. However, too often lawmakers view government models of discovery, from NASA to public university research labs, as obsolete and costly superstructures in today’s .com marketplace.  What happened to the case for public exploration and discovery and why shouldn’t the private sector be trusted to find the cure for Grandma’s dementia or Johnny’s brain tumor?  Long-time Washington political insider, former lobbyist, Administration appointee, and AIMBE’s Executive Director, Milan Yager, will reveal the hidden truth about why America is falling out of love with science and biomedical discovery.  

This presentation will highlight innovations and achievements made possible from past federal investments in basic research; such as the internet, cell phones, vaccines, and mapping the human genome.  Today, Congress seems less interested in past accomplishments as they assume new priorities to balance the budget, reduce government, and free the private sector to assume long-standing government responsibilities for innovation and discovery.  How did Congress make spending decisions to permit federal R&D spending to be flat for over a decade?  Why isn’t the public concerned about declining investments for medical research?  Discover practical steps for making a winning case for federal funding for medical and biological research.  Learn how engineers can successfully discuss their research with a Member of Congress.  Find out how to brand your research and the cure it supports in providing the pixie dust for returning the spark for America’s passion for research.  

Arming yourself with the strategies for the political warfare in the case for innovation is more than just changing public policy; it can provide the key to changing the future landscape of new biomedical materials, products or procedures.  Engagement might just save your lab and future career.  Attendees will get insight into putting passion back into American’s love affairs with biomedical research and innovation.


bio:
Dr. Milan Yager is a long-time Washington lobbyist and association executive who has over 30 years of senior government and public affairs experience in the public and private sectors. His background includes senior government position in the Administration and Congress, as well as private sector experience with national trade associations and his own business-consulting firm. He is currently the Executive Director of the American Institute for Medical and Biological Engineering, an honorific society of the most accomplished innovators in the fields of medical and biological engineering. Previously, he served as an Administration political appointee to a regulatory agency, held senior lobbyist positions for two national trade associations, testified before Congress, was Chief of Staff to a Member of Congress, a candidate for public office, and has participated in congressional and presidential campaigns. Dr. Yager is a graduate of the University of Iowa and has a Masters in Public Administration from The American University in Washington,D.C.


date: 2018-11-16
speaker: Dr. Nahid Farhady Ghalaty, Accenture Technology Labs
title: A Hierarchical Framework to Detect Targeted Attacks using Deep Neural Network
location: Engineering Building, Room 4201
start_time: 2:00 pm
end_time: 3:00 pm
abstract:
Malware is a serious threat to corporations, individuals and nation’s state since the adversaries leverage it to get unauthorized access to secret and critical information. Now a days, malware detection at large scale is a critical problem since the number and variety of malwares is increasing. One approach to address this issue is using statistical analysis and machine learning. Malware detection services can be for organizations or individuals. Malware detection as a service for organization is a manual and cumbersome process to find the source of the malware, the family it belongs to and the target of the malicious code. In this paper, we propose a hierarchical framework that accelerates the detection and classification of malwares based on priorities of an organization. We also propose the first deep neural network that can differentiate between a targeted malware, i.e. cyber-espionage, and cyber-crimes. This model has been tested and verified using a balanced data-set of both type of files. The detection rate is 97% with the false negative rate of 2.8%.

bio:
Dr. Nahid Farhady Ghalaty has obtained her PhD from the Bradley department of Electrical and Computer Engineering, Virginia Tech in 2016. Her research interest is on embedded systems security, Fault Attacks and Side Channel Attacks, and Cryptography. She is now a cybersecurity R&D Associate Manager at Accenture Technology Labs where she has focused on several cyber security related projects such as malware detection and classification, predictive analytics and adversarial ML. She is also an adjunct faculty at the department of computer science at The George Washington University. Her research has been published in several peer reviewed conferences and journals such as DATE, FDTC, and IEEE ESL.


date: 2019-01-23
speaker: Dr. Alec Koppel, Army Research Laboratory
title: Machine Learning for Time Series via Optimally Compressed Bayesian Methods
location: Engineering Building, Room 3507
start_time: 11:00 am
end_time: 12:00 pm
abstract:
Machine learning is ubiquitous across many areas of science and engineering. Today's learning pipeline involves storing big data in the cloud where it is used to train deep networks, which has seen impressive successes in vision and speech recognition. However, this framework fails to address application domains where data perpetually changes such as field robotics and econometrics. In this talk, we propose methods based on Bayesian statistics that can stably adapt in the face of changing data. Unfortunately, Bayesian methods classically suffer from the curse of dimensionality: their model complexity is proportionate to the time index, and so popular perception is they are inapplicable to streaming data scenarios. We survey our recent efforts to upend this perception through the introduction of online compression rules that nearly preserve optimality while ensuring the model complexity is at-worst finite. Specifically, we'll discuss compression rules for kernel regression/classification, their extensions in risk-aware learning and reinforcement learning, as well as Gaussian Process regression. These methods pave the way for new systems that may autonomously and accurately adapt.

bio:
Alec Koppel began as a Research Scientist at the U.S. Army Research Laboratory in the Computational and Information Sciences Directorate in September of 2017. He completed his Master's degree in Statistics and Doctorate in Electrical and Systems Engineering, both at the University of Pennsylvania (Penn) in August of 2017. He is also a participant in the Science, Mathematics, and Research for Transformation (SMART) Scholarship Program sponsored by the American Society of Engineering Education. Before coming to Penn, he completed his Master's degree in Systems Science and Mathematics and Bachelor's Degree in Mathematics, both at Washington University in St. Louis (WashU), Missouri. His research interests are in the areas of signal processing, optimization and learning theory. His current work focuses on optimization and learning methods for streaming data applications, with an emphasis on problems arising in autonomous systems. He co-authored a paper selected as a Best Paper Finalist at the 2017 IEEE Asilomar Conference on Signals, Systems, and Computers.


date: 2019-02-08
speaker: Brandon Park, George Mason University
title: Network Analysis for Time Series Data
location: The HUB, Room 3
start_time: 11:00 am
end_time: 12:00 pm
bio: TBD
abstract:
Network based approaches for analyses of time series data can provide new insights concerning causality and forecasting. However, it is challenging to construct such networks using time series data, due to correlations and autocorrelations between the nodes. Additionally, the problem gets more complicated when exogenous variables are present. In this presentation, we (i) describe theoretical guarantees for the regularization method for estimating the autoregressive parameters and the regression coefficients in an ARX Model, (ii) describe a new method for constructing an implicit network, and (iii) provide various network wide metrics (NWM) that are useful for identifying the active features of the implicit network. We also describe the asymptotic properties of NWM and utilize them to identify communities via the proposed implicit network.


date: 2019-02-07
speaker: Qiaoyan Yu, University of New Hampshire
title: Assuring the Root of Trust for Internet-of-Things (IoTs)
location: Engineering Building, Room 2901
start_time: 10:30 am
end_time: 11:30 am
abstract:
Internet-of-Things (IoT) security is being challenged by various threats, such as cyber attack, hardware tampering attack, and malicious tool attack. Rogue users, component/system designers, or third-party tool providers could implement those attacks to sabotage the integrity and security of IoT systems. This talk will introduce methods that assure IoT security by obfuscating hardware components, the root of trust. More specifically, a dynamic state-deflection method is proposed to obfuscate gate-level designs, thwarting reverse engineering and IP piracy attacks. Further, we exploit the principle of moving target defense (MTD) to address the stealthy hardware tampering attack from FPGA design suites. Three defense lines in the proposed FPGA-oriented (FOMTD) generate configuration uncertainty to prevent hardware Trojan insertion via malicious FPGA software.

bio:
Dr. Qiaoyan Yu is an Associate Professor of Electrical and Computer Engineering at the University of New Hampshire, where she also directs the Reliable & Secure VLSI Systems Laboratory. Dr. Yu’s research expertise includes hardware Trojan detection, side-channel attack mitigation, three-dimensional integrated circuits, FPGA, and embedded system security, cybersecurity, and VLSI fault tolerance. She received her B.S. from Xidian University (2002), M.S. in Communication and Information Engineering from Zhejiang University (2005), and the Ph.D. in Electrical and Computer Engineering from the University of Rochester (2011).

Dr. Yu received the NSF CAREER Award, the Air Force Research Lab Faculty Fellowship, a Semiconductor Research Corporation grant. She received the Best Poster Award at Texas Analog Center of Excellence 2018, Best Poster Award at ISVLSI’16, Best Paper Award Finalist in MWSCAS’15, Best Paper Award Finalist in NOCS’11, and the Best ECE Ph.D. Dissertation Award at the University of Rochester in 2011. Her student teams have won three consecutive (2015-2017) Top-3 awards in the Embedded Security Challenge at Cyber Security Awareness Week. She received the Excellence in Teaching Award at UNH in 2015. Dr. Yu was the general chair for DFT’17, track-chair for ASP-DAC’17, ISVLSI’18, ISVLSI’19, and MWSCAS’18. She serves on the technical program committees of HOST, DAC, Asian HOST, ASP-DAC, GLSVLSI, ISVLSI, DFT, ISCAS, and ICCD.


date: 2019-02-15
speaker: Rina Dechter, University of California, Irvine
title: Probabilistic Reasoning Meets Heuristic Search
location: Johnson Center, Gold Room
start_time: 11:00 am
end_time: 12:00 pm
abstract:
Graphical models, including constraint networks, Bayesian networks, Markov random fields and influence diagrams, have become a central paradigm for knowledge representation and reasoning in artificial intelligence, and provide powerful tools for solving problems in numerous application areas. Reasoning over probabilistic graphical models typically involves answering inference queries, such as computing the most likely configuration (maximum a posteriori or MAP) or evaluating the marginals or the normalizing constant of a distribution (the partition function).  A task called marginal MAP generalizes these two by maximizing over a subset of variables while marginalizing over the rest, which is similar to sequential decision making for maximizing expected utility.

Exact computation of such queries is known to be intractable in general, leading to the development of many approximate schemes, the major categories of which are variational methods, search algorithms, and Monte Carlo sampling.  The key is to leverage ideas and techniques from the three inference paradigms, and integrating them to provide hybrid solutions that inherit their respective strengths. In the past decade, my research group at UCI has developed state-of-the art algorithms based on such integration, winning a few solver competitions.

In this talk I will review the main principles behind the AND/OR search for graphical models and show how it can be  guided by heuristics based on variational inference (e.g., decomposition bounds such as weighted mini-bucket and cost-shifting schemes) for solving probabilistic and deterministic graphical models queries. The emerging solvers allow flexible trading off memory for time and time for accuracy and aim for anytime behavior that generates not only an approximation that improves with time, but also confidence bounds which become tighter with more time.

bio:
Rina Dechter’s research centers on computational aspects of automated reasoning and knowledge representation including search, constraint processing, and probabilistic reasoning. She is a Chancellor's Professor of Computer Science at the University of California, Irvine. She holds a Ph.D. from UCLA, an M.S. degree in applied mathematics from the Weizmann Institute, and a B.S. in mathematics and statistics from the Hebrew University in Jerusalem. She is an author of Constraint Processing published by Morgan Kaufmann (2003), and Reasoning with Probabilistic and Deterministic Graphical Models: Exact Algorithms by Morgan and Claypool publishers, 2013, has co-authored close to 200 research papers, and has served on the editorial boards of: Artificial Intelligence, the Constraint Journal, Journal of Artificial Intelligence Research (JAIR), and Journal of Machine Learning Research (JMLR). She is a Fellow of the American Association of Artificial Intelligence 1994, was a Radcliffe Fellow 2005–2006, received the 2007 Association of Constraint Programming (ACP) Research Excellence Award, and she is a 2013 ACM Fellow. She was a Co-Editor-in-Chief of Artificial Intelligence 2011-2018 and she was elected as the conference chair of IJCAI-2022. 


date: 2019-02-22
speaker: Dr. Les Servi, The MITRE Corporation
title: Mission Dependency Analysis with Physical Network Relationships*
location: Johnson Center, Gold Room
start_time: 11:00 am
end_time: 12:00 pm
abstract:
Combining functional and physical relationships is a natural means to identify the key areas a network is most vulnerable in an attack. Towards that end this talk examines four variations of the general combinatoric decision making problem of identifying the physical nodes of a computer network to enter, the physical nodes to traverse and then the functional nodes to attack to most impact the network's mission all subject to a budget constraint. This talk ends with numerical experiments which illustrate by example, the size and complexity of networks which can be analyzed using the methods described.

bio:
Dr. Servi received his PhD in engineering from Harvard University, worked at Bell Laboratories, and GTE (now Verizon) Laboratories pursuing telecommunication research, served 1 year as a visiting scientist at Harvard University and MIT, and worked at MIT Lincoln Lab in the areas of communication and ISR.  He currently is a Group Leader at The MITRE Corporation and the Project Leader of an internal Cyber Analytics project, an internal Health Analytics project, and an Air Force Logistics- related project.  At MITRE he has contributed to a diversity of areas including the analysis of social media, portfolio analysis, robust optimization applied to systems of interest, and cyber security.
 
Dr. Servi has an extensive publication record and 10 patents derived from his mathematical analysis.  He has been very active in INFORMS and served on its Board of Directors for 6 years.  He has also served as Chair of the Boston Chapter of INFORMS, Chair of the INFORMS Telecommunication Society, Chair of the INFORMS Applied Probability Society, and was the founding Chair of the INFORMS Social Media Analytics section. In 2004, he was named Fellow of INFORMS.  He has been an editor of Operations Research, Management Science, INFORMS Journal of Computing, and has been invited to serve on PhD thesis committees at Harvard University, MIT, Boston University, and George Washington University. In 2010-11, he served on a Defense Science Board Task Force and again in 2016. In 2016, he was an invited keynote speaker at the MORS Special Meeting on Emerging Techniques.
 
*This is joint work with Kael Stilp (The MITRE Corporation).



date: 2019-03-01
speaker: Liang Zhao, George Mason University
title: Adversarial learning on deep graph generative models and applications
location: Research Hall, Suite 417
start_time: 2:00 pm
end_time: 3:00 pm
abstract:
Inspired by the tremendous success of deep generative models on generating continuous data like image and audio, in the most recent year, deep graph generative learning is becoming a promising domain which focuses on generating discrete data such as graphs. They are typically unconditioned generative models which has no control on modes of the graphs being generated. Going beyond that, in this presentation, we will talk about a new problem named Deep Graph Translation: given an input graph, we want to infer a target graph based on their underlying (both global and local) translation
mapping. Moreover, by automatically interpreting such translation mapping, we may be able to discover new rules and patterns of the graph translation mechanism. Graph translation could be highly desirable in many applications on network synthesis, such as learning to synthesize the functional connectome from a structural connectome of the brain voxel networks and applications in other domains on network synthesis. To achieve this, we propose a novel Graph-Translation-Generative Adversarial Networks (GT-GAN) which will generate a graph translator from input to target graphs based on
adversarial learning. GT-GAN consists of a graph translator where we propose new graph convolution and deconvolution layers to learn the global and local translation mapping. A new conditional graph discriminator has also been proposed to classify target graphs by conditioning on input graphs.

bio:
Dr. Liang Zhao is an assistant professor at Information Science and Technology Department at George Mason University. He got his PhD degree from Computer Science Department at Virginia Tech in the United States. His research interests include big data mining, artificial intelligence, and machine learning, with particular emphasis on sparse feature learning, social event forecasting, text mining, heterogeneous network modeling, and deep learning on graphs. He got the CRII award from National Science Foundation of United States in 2018. He is named as the one of the “Top 20 Data mining Rising Star in the world” by Microsoft Academic Search in 2016. He has published numerous papers in top venues in data mining and artificial intelligence such as ACM KDD, Proceedings of the IEEE, IEEE TKDE, ACM TKDD, AAAI, IJCAI, IEEE ICDM, ACM CIKM, WWW, and DAC. He has served as publication chair of SSTD 2017, co-chair of LENS workshop at SIGSPATIAL 2018, program committee of ACM KDD 2018, 2019, AAAI 2019, SDM 2019, IEEE ICDM 2017, 2018. He has been serving as reviewer for top conferences and journals such as ACM KDD, ACM TKDD,IEEE TKDE, and IEEE ICDM.


date: 2019-03-08
speaker: Prof. Noseong Park, George Mason University
title: Robust Phishing URL Detection via Generative Models and Graphical Inference
location: Research Hall, Suite 417
start_time: 2:00 pm
end_time: 3:00 pm
abstract:
Phishing is a power tool of the attacker. Many cyber-attacks start with sending out phishing emails to potential victims. Two topics related to phishing will be presented in this talk. First, I will talk about how generative models can be used to detect (or even hunt) unknown phishing URLs. State-of-the-art generative models can learn about string patterns of phishing URLs and write fake phishing URLs, which we use as additional training samples to improve the detection accuracy. Second, I will talk about a phishing URL detection method robust against evasion. The attacker can design a phishing
campaign on top of benign domains, IP addresses, etc. to evade existing phishing URL detection methods. I will discuss a graphical, transductive inference method robust against such evasion attempts.

bio:
Dr. Noseong Park is an assistant professor at the Center for Secure Information Systems of George Mason University. His research interests include AI and Cybersecurity. He has published papers in KDD, IJCAI, VLDB, WWW, WSDM, ACM TIST, IEEE TIFS, etc.



date: 2019-03-22
speaker: Prof. Il-Chul Moon, Korea Advanced Institute of Science and Technology
title: Computational Social Science with Latents
location: Engineering Building, Room 2302
start_time: 11:00 am
end_time: 12:00 pm
abstract:
Computational social science has grown to answer diverse challenges with the support from data science, machine learning, and generative models. These challenges seems to have a specific problem to solve, but the true solution only achieved when we understand the latent profiles and dynamics. This talk will provides a number of computational methodologies and case studies on such understanding. For example, we understand the population latent health-care profile with a probabilistic modeling; we look into why our politicians vote on a certain bill with a deep generative model; and we regenerate a housing market agent based model to automatically calibrate the simulation to match the real world. Along these studies, we observe how the generative models from the probabilistic modeling, the deep generative neural networks, and the agent-based model can be fused to investigate the challenges that we see in our society.

bio:
Il-Chul Moon received Ph.D. in Societal Computing from Institute of Software Research, School of Computer Science, Carnegie Mellon University in 2008. He is currently an Associate Professor at the Department of Industrial and Systems Engineering, Korea Advanced Institute of Science and Technology. His research interests include the overlapping area of computer science, management, sociology, and operations research, and also command and control analyses, health-care analyses, and disaster management.


date: 2019-04-03
speaker: Jonathan Katz, University of Maryland
location: Engineering Building, Room 4201
start_time: 2:00 pm
end_time: 3:00 pm
title:
From Theory to Practice: From Zero-Knowledge via Secure Computation to a Next-Generation Signature Scheme

abstract:
Recent progress in cryptography has shown that many ideas previously viewed as purely theoretical can sometimes lead to surprisingly efficient implementations. As a particular example, we show how to instantiate the "secure computation in the head" approach of Ishai et al. with a new secure-computation protocol designed in the preprocessing model. After several optimizations, this leads to a communication- and computation-efficient zero-knowledge proof for any "moderately sized" boolean circuit.

Our zero-knowledge proof can, in turn, be used to construct digital signature schemes based only on symmetric-key primitives and hence with viable "post-quantum" security. We report on the performance of the resulting schemes, which are currently under consideration by NIST as part of their post-quantum cryptography standardization effort.

bio:
Jonathan Katz is director of the Maryland Cybersecurity Center and a professor in the Department of Computer Science. He also has a joint appointment in the University of Maryland Institute for Advanced Computer Studies.

Katz's research interests lie broadly in the areas of cryptography, computer and network security and complexity theory, with his most recent work focusing on secure multi-party computation, database privacy and the science of cybersecurity. He has co-authored the textbook Introduction to Modern Cryptography (2007), which has been used by colleges and universities throughout the world and published more than 100 scientific articles. He has also done extensive consulting work for U.S. government agencies and private corporations, mostly involving cryptographic protocols and algorithms.


date: 2019-04-05
speaker: Pierre Baldi, University of California Irvine
title: Deep Learning in the Biomedical Sciences
location: Research Hall, Room 163
start_time: 11:00 am
end_time: 12:00 pm
abstract:
We will first provide a brief historical and conceptual overview of deep learning.  We will then demonstrate several applications of deep learning to problems in the biomedical sciences, ranging from predicting protein structural features, to detecting circadian patterns of gene expression, to detecting polyps in colonoscopy videos. Finally, we will conclude with some of the future technical and societal challenges.

bio:
Pierre Baldi earned MS degrees in Mathematics and Psychology from the University of Paris, and a PhD in Mathematics from the California Institute of Technology.  He is currently Distinguished Professor in the Department of Computer Science, Director of the Institute for Genomics and Bioinformatics, 
and Associate Director of the Center for Machine Learning and Intelligent Systems at the University of California Irvine. The long term focus of his research is on understanding intelligence in brains and machines.  He has made several contributions to the theory of deep learning, and  developed and applied deep learning methods for problems in the natural sciences such as the detection of exotic particles in physics, the prediction of reactions in chemistry, and the prediction of protein secondary and tertiary structure in biology. He has written four books and over 300 peer-reviewed articles. He is the recipient of the 1993 Lew Allen Award at JPL, the 2010 E. R. Caianiello Prize for research in machine learning, and a 2014 Google Faculty Research Award. He is and Elected Fellow of the AAAS, AAAI, IEEE, ACM, and ISCB.


date: 2019-04-12
speaker: Tanmoy Chowdhury, George Mason University
title: FingerPIN: A Novel Authentication Scheme Combining Fingerprints and Numeric PINs
location: Research Hall, Suite 417
start_time: 11:00 am
end_time: 12:00 pm
bio: Tanmoy Chowdhury is a PhD student working with Prof. Emanuela Marasco.
abstract:
Biometric authentication is being increasingly adopted in a wide range of applications such as law enforcement and immigration due its numerous advantages over traditional password-based authentication. In particular for fingerprint-based systems, the ease of use and low error rates have been contributing to their success. However, some significant concerns still exist, which can potentially hinder further adoption of this technology. A subject's fingerprint is permanently associated with an individual and, once stolen or compromised, it cannot be replaced. To address this concern, we propose a novel authentication scheme that integrates type 1 and type 3 authentication factors into a fingerprint-based PIN, thus offering a new approach to multi-factor authentication. Furthermore, we carry out an experimental vulnerability analysis of the proposed scheme and discuss a solution to mitigate the discovered vulnerabilities. 


date: 2019-04-22
speaker: Shouhuai Xu, University of Texas at San Antonio
title: Cybersecurity Dynamics: Framework, Some Results, and Future Research Directions
location: Research Hall, Suite 417
start_time: 11:00 am
end_time: 12:00 pm
abstract:
For decades, computer and information security research has been driven by fundamental concepts such as confidentiality, integrity, and availability. Despite the tremendous accomplishments, cybersecurity is still largely an Art. What concepts will drive the research to elevate this Art to Science of cybersecurity? Our research has led to a unique approach, dubbed Cybersecurity Dynamics, by which we aim to build a systematic foundation for modeling, analyzing, and quantifying cybersecurity from a holistic perspective. Under the umbrella of this framework, our research has been focusing on three thrusts, which we call a systematic “x-y-z-t coordinate system” as a metaphor. At a high level, the x-axis represents first-principle modeling (analogous to Theoretical Physics research for understanding the Universe), the y-axis represents data analytics (analogous to Experimental Physics research), the z-axis represents metrics (analogous to defining notions such as Mass and Energy), and the t-axis represents time (meaning that “things” in cyberspace evolve over time). The framework is inherently multidisciplinary because it cuts across many disciplines, including Computer Science, Applied Mathematics (e.g., Dynamical Systems, Control Theory, and Game Theory), Statistics, Machine Learning, Artificial Intelligence, Statistical Physics, Complexity Science, Systems Science, Network Science, and Social Sciences. In this talk, I will review the Cybersecurity Dynamics framework, highlight some results, and discuss future research directions. 

bio:
Shouhuai Xu is a Full Professor in the Department of Computer Science, University of Texas at San Antonio (UTSA). He is the founding Director of the Laboratory for Cybersecurity Dynamics at UTSA. He is interested in both fundamental and practical cybersecurity research. His research has been funded by AFOSR (including MURI), AFRL, ARL, ARO, NSF (including Large) and ONR. He co-initiated the International Conference on Science of Cyber Security a nd the ACM Scalable Trusted Computing Workshop (ACM STC). He is/was a Program Committee co-chair of SciSec’19, SciSec’18, ICICS’18, NSS’15 and Inscrypt’13. He has served on the Program Committees of numerous international conferences. He is/was an Associate Editor of IEEE Transactions on Dependable and Secure Computing (IEEE TDSC), IEEE Transactions on Information Forensics and Security (IEEE T-IFS), and IEEE Transactions on Network Science and Engineering (IEEE TNSE). He received his PhD in Computer Science from Fudan University.


date: 2019-04-17
speaker: Dr. Nael Abu-Ghazaleh, University of California, Riverside
title: Is my GPU Secure? Covert and Side Channel Attacks on GPUs
location: Engineering Building, Room 4201
start_time: 11:00 am
end_time: 12:00 pm
abstract:
Modern computing systems are under attack by increasingly motivated and sophisticated attackers. Recently, the Meltdown and Spectre attacks demonstrated that security is not only a software problem, but that the system hardware components can expose software-exploitable vulnerabilities. With the expanding role of GPUs within computing systems, not only for graphics and multi-media workloads but also as computational accelerators, it is important to understand their security properties. In this talk, I will overview our recent research in understanding covert and side-channel attacks present in modern GPUs. We show that it is possible to construct high bandwidth covert channels, superior in bandwidth and quality to those on GPUs. Furthermore, we show that GPU sharing between multiple workloads also offers opportunities for side channel attacks, and demonstrate several variants of these attacks targeting both graphics and computational workloads. The talk will briefly discuss potential mitigations, and how this type of vulnerabilities is likely to continue to manifest as future computing systems continue to evolve.

bio:
Nael Abu-Ghazaleh is a Professor in the Computer Science and Engineering as well as the Electrical and Computer Engineering Departments at the University of California, Riverside. He also serves as the chair for the Computer Engineering Program. His research is in architecture support for computer system security, high performance computing, and networked and distributed computing. He has published over 150 papers in these areas, several of which have been recognized with best paper awards or nominations. His hardware security research has resulted in the discovery of several new attacks that have been disclosed to companies including Intel, AMD, ARM, Apple, Microsoft and Nvidia, and received wide coverage from technical news outlets.


date: 2019-04-29
speaker: Kenzo FUJISUE, Ph.D, Member of Japanese House of Councillors (Upper House)
title: Cyber Resilience in Japan and US-Japan Cybersecurity Relationships
location: Engineering Building, Room 4705
start_time: 1:00 pm
end_time: 2:00 pm
abstract:
Japanese-US collaboration on cybersecurity and cyber resilience is becoming increasingly important, not only due to the size and technological sophistication of our economies, but also because of international competition in cyberspace. This is a rare opportunity to interact directly with an influential member of Japan's Upper House who is passionately interested in cyber resilience issues and technically qualified to speak on them. Dr. Fujisue will discuss why he feels that cyber resilience (the ability to take damage and keep on operating) is a stronger approach in today's environment than cybersecurity (trying to prevent damage from happening) and how he's trying to change Japanese thinking in this area. This will be particularly important as Japan transitions to "Society 5.0," which the Japanese government defines as "the advanced fusion of cyberspace and physical space to balance economic advancement with the resolution of social problems and to bring about a human-centered society." Japan's cybersecurity policy has been evolving in preparation for "Society 5.0," to include a number of important laws, regulations and organizations. However, there still is room for improvement, and especially for more effective implementation of existing guidance and increased awareness of the importance of the topic. He also will address US-Japan security cooperation in cyberspace and the cyber defense of Japan. The 2020 Tokyo Olympics can provide a powerful incentive to (1) increase the cyber security awareness of the Japanese people, (2) establish a stronger culture of cyber security, and (3) shift Japan's policy from cyber security to cyber resilience (which he has discussed with representatives from GMU).

bio:
Japanese Member of the House of Councillors (Upper House), Kenzo FUJISUE, was born in 1964 in Kumamoto Prefecture. He received a degree in Computer Science from the Tokyo Institute of Technology in 1986, an MS in Management of Technology from MIT in 1995, a Masters in Public Administration from Harvard in 1996, a PhD in Industrial Management from the Tokyo Institute of Technology in 1999, and a PhD in International Relations from Waseda University in 2013. From 1986 to 1999 he served in what is now the Ministry of Economy, Trade and Industry (METI), and was Associate Professor at the University of Tokyo in 1999.  Elected to the Diet in 2004, 2010 and 2016, he is a member of the Liberal Democratic Party and Voice of The People. His current responsibilities in the House of Councilors (as of February 16,2019), include: Committee on Financial Affairs, Committee on Audit, Special Committee on North Korean Abduction Issue and Related Matters, and the Commission on the Constitution. He is passionately interested in cyber issues and would like to become Japan's Cyber Minister some day. His English is excellent.


date: 2019-05-23
speaker: Howie Huang, George Washington University
title: Machine Learning through Graph Algorithms, Systems and Cybersecurity Applications
location: Engineering Building, Room 4201
start_time: 1:30 pm
end_time: 2:30 pm
abstract:
Today we live in a highly connected world. Our society, commerce, and national security all depend on a myriad of networks that range from transportation networks to power grids, from biological networks to personalized healthcare, from wearable devices to autonomous vehicles, and from computer networks to social networks. In Graph Computing Lab at the George Washington University, we are developing novel graph-based machine learning algorithms and systems to manage big data generated by these networks, to understand the contextual and causal relationships within entities and events, and to deliver actionable knowledge to stakeholders in real time. In this talk, I will share our experiences in designing and developing high-performance graph systems, and discuss our techniques for addressing the algorithmic, computational, and I/O challenges in graph computing. In addition, I will present our ongoing work on utilizing these graph systems for understanding and analyzing complex network data for cyber threat hunting.

bio:
Dr. Howie Huang is a Full Professor in Department of Electrical and Computer Engineering, with a courtesy appointment in Department of Computer Science, and Director of the Graph Computing Lab at the George Washington University. Motivated by the needs of big data and cybersecurity applications, he works at the intersection of algorithms, computer architecture and systems, with focus on developing high-performance computing and machine learning techniques tailored for large-scale graph datasets. Dr. Huang is a recipient of the National Science Foundation CAREER Award, NVIDIA Academic Partnership Award, Comcast Technology Research and Development Fund Award, and IBM Real Time Innovation Faculty Award. His work on big graph traversal has ranked highly on both the Graph500 and Green Graph500 benchmarks, which measure the performance and energy efficiency of the most powerful data-intensive supercomputers in the world. His research won a Champion Award and a Student Innovation Award at the 2018 Graph Challenge of IEEE High-Performance Extreme Computing conference, two awards (Finalist and Honorable Mention) at the DARPA Graph Challenge 2017, the Best Paper Award Nomination at NVMSA'17, the ACM Undergraduate Student Research Competition Winner at SC'12, a Best Student Paper Finalist at SC'11, the Best Poster Award at PACT'11, and a High-Performance Storage Challenge Finalist at SC'09. Dr. Huang is an Associate Editor for IEEE Transactions on Parallel and Distributed Systems and IEEE Transactions on Cloud Computing. He was the Associate Chair and Interim Chair in Department of Electrical and Computer Engineering in 2015-2016. He received a PhD in Computer Science from the University of Virginia.


date: 2019-11-01
speaker: Adam Smith, Boston University
title: Data Privacy and Statistical Inference
location: Research Hall, Room 163
start_time: 11:00 am
end_time: 12:00 pm
abstract:
Consider an agency holding a large database of sensitive personal information—say,  medical records, census survey answers, web searches, or genetic data. The agency would like to discover and publicly release global characteristics of the data while protecting the privacy of individuals' records.

I will begin by discussing what makes this problem difficult, illustrating some challenges via recent attacks. Motivated by this, I will present differential privacy, a rigorous definition of privacy in statistical databases that is now widely studied, and increasingly used to analyze and design deployed systems.
  
I’ll conclude by explaining recent research on statistical inference from differentials private outputs, along the way discussing some of the challenges and promises of deployments in government and industry.

bio:
Adam Smith is a professor of computer science at Boston University. From 2007 to 2017, he served on the faculty of the Computer Science and Engineering Department at Penn State. His research interests lie in data privacy and cryptography, and their connections to machine learning, statistics, information theory, and quantum computing. He obtained his Ph.D. from MIT in 2004 and has held postdoc and visiting positions at the Weizmann Institute of Science, UCLA, Boston University and Harvard. He received a Presidential Early Career Award for Scientists and Engineers (PECASE) in 2009; a Theory of Cryptography Test of Time award in 2016; and the 2017 Gödel Prize. These last two awards were joint with C. Dwork, F. McSherry, and K. Nissim.


date: 2019-10-30
speaker: Jean Camp, Indiana University
title: Why Don't People ... Use Security, Protect Their Data, and Adopt the Available Solutions?
location: Engineering Building, Room 4201
start_time: 2:00 pm
end_time: 4:00 pm
abstract:
Is it that people don't care? Or people don't understand security and privacy? Is it a question of usability? Or is it a combination of all three? Individuals may rationally choose not to invest in security to benefit others, may underestimate their own risks, and may simultaneously find solutions to be unusable. 

The solution to the lack of adoption of security (and the corresponding privacy paradox) depends upon research thread one follows. For a classic economist, privacy is means a less efficient market. Given that market efficiency is contingent on more information, individuals are rationally unconcerned; the value from information sharing outweighs the costs of privacy loss. Thus, the solution is to ensure that the value of the information being transacted is realized by the individual. From a behavioral perspective, the problem is misperceptions: the perceived risk and perceived benefit of information sharing are incorrect. Risk perceptions can then be informed by appropriate risk communication. A final explanation is that current security and privacy enhancing technologies are not usable. The solution then is to design technology that is usable and aligns with user's needs, e.g. users must be provided risk mitigating options and not just informed of the risk. 

In this presentation I examine these explanations and their relative merits, referencing research that addresses each of these. I conclude that the emerging threat landscape of IoT and cyberphysical systems requires a comprehensive solution that addresses all three. 

bio:
Jean Camp is a Professor at the School of Informatics and Computing at Indiana University. She joined Indiana after eight years at Harvard’s Kennedy School where her courses were also listed in Harvard Law, Harvard Business, and the Engineering Systems Division of MIT. She spent the year after earning her doctorate from Carnegie Mellon as a Senior Member of the Technical Staff at Sandia National Laboratories. She began her career as an engineer at Catawba Nuclear Station and with a MSEE at University of North Carolina at Charlotte. Her research focuses on the intersection of human and technical trust, levering economic models and human-centered design to create safe, secure systems. She has authored more than two hundred publications. She has peer-reviewed publications on security and privacy at every layer of the OSI model. She has alumni in the private, public, and nonprofit sectors. She is a Fellow of the Institute of Electrical and Electronic Engineers, as well as a Fellow of the American Association for the Advancement of Science.


date: 2020-02-06
speaker: Matthew Lentz, University of Maryland
title: (Em)powering Users and Their Devices
location: Engineering Building, Room 4201
start_time: 2:00 pm
end_time: 3:00 pm
abstract:
Contemporary devices are continuously sensing and always connected, enabling powerful services for end users. These services often operate over personal data: collecting and processing input from sensors (e.g., audio, location), or rendering output to the user (e.g., financial information). This not only manifests in problems for users in terms of security and privacy but also power management due to energy constraints on portable devices.

Understandably, this data is the target of many attacks, ranging from malicious applications to compromises of platform software. Users are increasingly required to rely on these devices but have few ways to control and reason about how their sensitive data is protected, processed, and shared. In this talk, I will discuss my work that leverages hardware security extensions to construct a software enforcement layer that helps users regain control over their data. I will introduce my work on SeCloak, a minimal enforcement layer to provide users with simple, yet powerful on/off control for hardware devices (such as their microphone or camera), and then discuss my work on Accountable Paths, which enables more expressive policies and supports a wider range of assurances over the data.

In order to support continuously sensing applications, energy-constrained devices rely on aggressive power management techniques such as entering a "sleep" state whenever possible, wherein tasks are frozen and hardware resources are placed in low-power modes. However, for application-driven wakeup events (e.g., sampling sensors), transitioning between states often consumes more energy than handling the event itself. I will present my work on a new power management state "Drowsy" which tracks dependencies at the OS-level to determine exactly which tasks and hardware need to be woken up to handle an event, improving the energy efficiency of handling these events by up to 5x. I will conclude with a discussion of my future research directions.

bio:
Matthew Lentz is a Ph.D. candidate in Computer Science at the University of Maryland. He received his B.S. in Computer Engineering from the University of Maryland in 2010. He is broadly interested in research at the intersection of systems, networking, and security.


date: 2020-02-28
speaker: Aastha Mehta, Max Planck Institute for Software Systems (MPI-SWS)
title: Policy Compliance in Online Services
location: Engineering Building, Room 4201
start_time: 2:00 pm
end_time: 3:00 pm
abstract:
In response to incidents of unintended disclosure and misuse of user data by online services, modern data protection regulations require service providers to restrict their collection, processing, sharing and storage of sensitive user data. However, ensuring compliance with such regulation in today's complex and rapidly evolving systems is technically challenging. In my research, I have developed practical systems to prevent unintended disclosures and misuse of data in the face of two broad classes of threats: software bugs and misconfiguration, and side channels.

In this talk, I will describe Pacer, a compliance system designed to prevent indirect inference of sensitive data via side channels in shared network links in the Cloud. Pacer shapes the outbound traffic of a Cloud tenant to make it independent of the tenant's secrets. At the same time, Pacer does allow variations in the traffic shape based only on public (non-secret) aspects of the tenants' workloads, thus enabling efficient sharing of network resources and incurring moderate overhead. Implementing Pacer requires modest changes to the Cloud hypervisor and the guest OS, and minimal changes to the guest application.

bio:
Aastha Mehta is a Ph.D. student at the Max Planck Institute for Software Systems (MPI-SWS) in Germany. She is expecting to graduate by mid 2020. Prior to joining MPI-SWS, she worked at NetApp. She received her Bachelors degree from BITS Pilani in India. She is interested in building practical systems that solve security problems.  Most recently, she has been focusing on systems for mitigating side channels in Cloud environments. She was invited to attend the 4th Heidelberg Laureate Forum in 2015 and the Rising Stars in EECS Workshop in 2018.


date: 2021-01-25
speaker: Manu Fontaine, CEO, Hushmesh Inc.
title: Decentralized cryptography eliminates identity and transaction fraud for everyone
location: Zoom
start_time: 1:00 pm
end_time: 2:00 pm
abstract:
For the past 50+ years, humans have had to identify and authenticate themselves to computers. While the Internet grew ever more essential to our everyday lives, this domain-centric approach to security has perpetuated inherent vulnerabilities such as human errors, social engineering and insider attacks. In 2020, new groundbreaking technologies became commercially available that enable a completely new person-centric approach to cryptographic identity, authentication and data security. The talk will introduce this new opportunity, and will show how it eliminates identity and transaction fraud for everyone.

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bio:
Manu is the Founder and CEO of Hushmesh, a cybersecurity startup that eliminates both identity and transaction fraud. The "mesh" enables tamperproof identity, globally trustworthy authentication and end-to-end cryptographic transactions for everyone, everything, everywhere. Before founding Hushmesh, Manu led Brightspot's marketing and business strategy, a next-generation Digital Experience Platform. Earlier at Brightspot, he secured new digital product and platform business with The Coca-Cola Company, U.S. News, Univision, Hanley Wood and more. Prior to Brightspot, Manu joined EMC to spearhead the company’s entry in the consumer space. He launched EMC LifeLine, a new consumer-friendly operating system for Network Attached Storage devices, and brought it to market with partners such as Intel and Iomega. He worked closely with Mozy, Iomega and EMC’s CTO Office to craft innovative storage solutions for consumers. Manu won the 2008 EMC Innovation Showcase Award for Constellation Computing, an aptly named and trademarked distributed approach to Cloud computing. Prior to EMC, Manu led the Safety, Security and Privacy Center of Excellence at AOL, and oversaw a portfolio of over 100 products, including AOL Parental Control and AOL Security Center. He personally led AOL PassCode and AOL Key, two subscription-based products intended to bring two-factor authentication to consumers. Prior to that, he led AOL’s Home Networking and Digital Home technology initiatives, which evolved into a strategic alliance with Sony. Prior to AOL, Manu started and led Alcatel's WebTouch consumer screenphone business in the US. In his earlier years with the company, Manu developed multimedia broadband devices for endoscopic surgeons as a member of the Alcatel Corporate Research Center in Strasbourg, France.
Manu holds two Masters in Electrical Engineering from the University of Louvain, Belgium. He is fluent in English and French.


date: 2021-01-22
speaker: Evgenios Kornaropoulos, Postdoctoral Researcher, University of California, Berkeley
title: Demystifying Leakage in Encrypted Systems
location: Zoom
start_time: 11:00 am
end_time: 12:00 pm
abstract:
The growing area of encrypted systems combines cryptographic advancements with system designs so that the end product can compute directly on ciphertexts without decrypting. One of the more prominent and practical approaches is using cryptographic primitives that reveal some formally-defined information, known as leakage, during computations on encrypted data.

 In this talk, I present two key findings from my research that show a foundational approach to understanding leakage in practical encrypted databases. First, I present a new family of leakage-abuse attacks against various encrypted queries under realistic scenarios. Second, I use the insights from this cryptanalysis towards a new framework of principled and practical defenses with provable guarantees. Overall, these results contribute to a holistic understanding of encrypted databases, one that bridges the gap between attacks and defenses. Finally, I will talk about my long-term vision for building encrypted systems by taking a multi-angle approach that combines cryptography, algorithms, and systems.

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bio:
Evgenios Kornaropoulos is a Postdoctoral Scholar at UC Berkeley working in the area of computer security with a focus on analysis and design of systems that use encryption. He obtained his Ph.D. from the Department of Computer Science at Brown University in 2019 where he received the Joukowsky Outstanding Dissertation Award. Before graduate school, he received his M.Sc. and B.Sc. in Computer Science at the University of Crete in Greece. He has published in conferences and journals spanning the areas of applied cryptography and security, data mining, visualization, and graph algorithms.


date: 2021-02-03
speaker: Xiaokuan Zhang, Ohio State University
title: Side-Channel Threats on Modern Platforms
location: Zoom
start_time: 9:00 am
end_time: 10:00 am
abstract:
In side-channel attacks, the adversary may learn secrets of a system or an application that are otherwise well protected, by observing traces (e.g., timing, power, or resource usage) of its execution. Recent studies have shown that attackers can learn sensitive information (e.g., cryptographic keys) through side channels, which jeopardizes the user's security and privacy. In this talk, I will talk about my research on exploring side-channel attack surfaces on smartphones (iOS), and applying differential privacy mechanisms to thwart traffic analysis attacks.

The first part of this talk covers our exploration of OS-level side channels on iOS, which are side channels exposed by public APIs. I will present the public APIs we found that caused cross-app side-channel leakages on iOS, and explain how they can be used to extract private user information (e.g., inferring foreground apps). I will also talk about our proposed countermeasures, which were integrated into iOS 11.1 and MacOS 10.13.1. In the second part of my talk, I will present our efforts on applying differential privacy to defeating streaming traffic analysis attackers. I will talk about how to adapt differential privacy mechanisms to add noise to the video streams. Next, I will present our implementation of a Chrome extension, which adds differentially private noise to Youtube streaming traffic. I will discuss future research directions at the end of my talk.

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bio:
Xiaokuan Zhang is a Ph.D. candidate at the Ohio State University. He graduated from Shanghai Jiao Tong University with the bachelor's degree in Computer Science in 2015. His research interest lies in the broad area of system security and privacy, including side channels, mobile security, IoT security, etc. He is one of the three winners worldwide to receive the 2020 NortonLifeLock (formerly Symantec) Graduate Fellowship. His papers were selected as the top 10 finalists of NYU Cyber Security Awareness Week (CSAW) applied research paper competition in 2016 and 2018, respectively, and his research has helped Apple mitigate side-channel attack surfaces in iOS. He has interned at Google and Microsoft Research.


date: 2021-02-05
speaker: Peng Gao, Postdoctoral Researcher in Computer Science at UC Berkeley
title: Building Trustworthy Systems for Fighting Modern Threats
location: Zoom
start_time: 11:00 am
end_time: 12:00 pm
abstract:
The increased opaqueness in computing systems makes them vulnerable to sophisticated cyber attacks (e.g., Advanced Persistent Threats). Consequently, many businesses have been plagued with huge losses (e.g., Equifax, Target, Yahoo). In addition, the success of emerging technologies such as online social networks and blockchain has come at the cost of new threats to security. How can we gain visibility into currently opaque computing systems and defend them from sophisticated attacks? How can we re-architect our computing for fighting new threats?

 

In this talk, I will cover multiple systems I have built for addressing these pressing issues. I will primarily discuss the advanced threat protection (ATP) stack, which brings transparency into computing systems (via ubiquitous system auditing) and connects the dots across system activities in various ways (forensically vs. real-time, manually vs. semi-automatically vs. automatically) to fight advanced threats. The ATP stack is multi-disciplinary, using techniques from system security, operating system, database, compiler, and machine learning. It includes multiple research efforts and has had real-world impact. I will also cover other systems on using AI for automated threat intelligence gathering and management and using network science and program analysis for securing online social networks and blockchain. I will conclude the talk by describing my future research agenda and long-term vision towards a secure and trustworthy cyberspace.

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Meeting ID: 999 4786 9960
Passcode: 407646

bio:
Peng Gao is a Postdoctoral Researcher in Computer Science at UC Berkeley. He received his Ph.D. in Electrical Engineering from Princeton University in 2019. His research interest lies in security and privacy issues in systems and networks. His work centers on creating scalable, secure, and trustworthy systems to solve real-world problems, with publications at premier academic venues (e.g., ACM CCS, USENIX Security, USENIX ATC, ICSE). His work on advanced threat protection has been successfully deployed in the real-world enterprise and commercialized, and has been selected as the Top-10 Finalist in the 2018 CSAW Best Applied Research Paper Competition. His proposal on threat intelligence has been selected as the winner of the 2020 Microsoft Security AI Research Award. He also has rich industry experience through multiple internships at leading companies and research labs (e.g., Microsoft Research, Facebook, etc.).


date: 2021-02-11
speaker: Brian McInnis, UC San Diego
title: Opportunities and Barriers to Facilitating Evidence-Based Discussion about Civic Issues
location: Zoom
start_time: 11:00 am
end_time: 12:00 pm
abstract:
News websites have the potential to facilitate large scale evidence-based discussion about civic issues, like climate change, but the online discussions are often overrun by toxicity and misinformation.  Even when people are not shouting at each other, online discussions about civic issues rarely focus on evidence, such as data and visualizations.  In this talk, I will share our analysis of how people comment about climate change data in the discussion threads at three news websites (i.e., Breitbart news, the Guardian, the New York Times).  The findings illustrate that data-centered talk, while rare, can provide valuable insights that could help center an online discussion around evidence—collection, analysis, and visual representation—to engage with an article’s narrative.  My research explores how techniques from crowdsourcing offer a potential way to promote and encourage evidence-based discussion.  I will draw examples from my own work studying news websites, collaboration platforms, and in-the-wild civic initiatives to review critical system design opportunities and barriers.

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Meeting ID: 927 6216 1280
Passcode: 742156

bio:
Dr. Brian McInnis is a postdoctoral scholar at UC San Diego and a member of the Design Lab.  Brian explores how to help people collaboratively build insights around policy concerns.  Brian earned his PhD in Information Science from Cornell University in 2019 where his thesis investigated how people build insights around policy concern through a series of studies that involved crowd workers in online discussions related to the AMT participation agreement.  Prior to joining Cornell, Brian worked at the RAND Corporation, where he studied a range of public policy issues—from the design of youth summer learning programs to predictive policing techniques.  Brian earned his Masters of Public Policy from Vanderbilt University's Peabody College of Education as well as a dual Bachelors in Economics and History from the University of California at Davis.


date: 2021-02-15
speaker: Arlei Silva, University of California, Santa Barbara
title: Mining and Learning from Graph Processes
location: Zoom
start_time: 11:00 am
end_time: 12:00 pm
abstract:
The digital transformation has given rise to a new form of science driven by data. Graphs (or networks) are a powerful framework for the solution of data science problems, especially when the goal is to extract knowledge from and make predictions about the dynamics of complex systems such as those arising from epidemiology, social media, and infrastructure. However, this representation power comes at a cost, as graphs are highly combinatorial structures, leading to challenges in search, optimization, and learning tasks that are relevant to modern real-world applications.

In this talk, I will overview my recent work on new algorithms and models for mining and learning from graph data. First, I will show how the interplay between a graph structure and its dynamics can be exploited for pattern mining and inference in networked processes, such as improving the effectiveness of testing during an epidemic. Then, I will focus on machine learning on graphs, where novel deep learning and optimization approaches for predicting graph data, such as traffic forecasting, will be described. As the last topic, I will introduce combinatorial algorithms for optimization on graphs that enable us to attack/defend their core structure, among other applications. I will end by briefly contextualizing my ongoing work as part of a broader research agenda with new related problems that I plan to address in the next few years.

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Meeting ID: 919 5924 6582
Passcode: 587177
 

bio:
Arlei Silva is a postdoctoral researcher at the University of California, Santa Barbara, where he also completed his Ph.D. Earlier, he received bachelor's and master's degrees from Universidade Federal de Minas Gerais, in Brazil. His research focuses on new algorithms and models for mining and learning from complex datasets, especially graphs. His work has been published at several venues including ICLR, WebConf (previously WWW), SIGKDD, AAAI, IJCAI, VLDB, AAMAS, SDM, ICDM, ICDE, and TKDE. He has also been the chair of the UCSB Graduate Student Workshop on Computing, the president of the UCSB CS Department Graduate Student Council, and a PC member for WebConf, AAAI, SDM, AAMAS, IJCAI, DSAA, and COMAD.


date: 2021-03-02
speaker: Alberto Dainotti, University of California San Diego
title: Detecting and Visualizing Internet Failures: From Measurements to Streaming Analytics
location: Zoom
start_time: 11:00 am
end_time: 12:00 pm
abstract:
Our society's dependence on the Internet is unquestioned. Yet our ability to manage the risks associated with relying on the proper functioning of the Internet's core infrastructure is hindered by a poor understanding of how it can fail. For example, we lack a thorough understanding of when/where/how/why large connectivity failures happen; or we rely on a global routing system that is extremely vulnerable and we do not know to what extent it is exploited, by whom, and for what purposes.  My research tries to address these questions by acquiring empirical evidence (e.g., on network status and misuse) on a global scale and longitudinally.  

In this talk I will first describe an approach I developed to gather unprecedented insight into episodes of connectivity disruption, which is based on two intuitions: the opportunity to extract (connectivity liveness) "signals" from so-called "Internet background noise" traffic and the benefit of combining different types of control-plane and data-plane measurements.  I then discuss how this inspired me to build a system (IODA, ioda.caida.org) to monitor the Internet for large-scale connectivity outages. IODA can detect Internet outages worldwide within minutes, continuously updating and monitoring hundreds of thousands of time series. IODA serves both as a research testbed and as a public service that provides automatically generated alerts and visualizations. This effort has enabled further research in multiple directions, facilitated interdisciplinary collaborations, and resulted in the engagement of industry, government, and civil society.  

I will conclude my talk by briefly discussing my other ongoing and future research efforts to understand and remediate Internet infrastructural issues and weaknesses.

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Meeting ID: 991 6892 8944
Passcode: 855711
 

bio:
Alberto Dainotti is an Associate Research Scientist at CAIDA, the Center for Applied Internet Data Analysis at University of California San Diego, USA. His research is at the intersection of Internet measurement, data science, and Internet security, with a focus on understanding and improving Internet infrastructure security and reliability. He has received the ACM SIGCOMM Internet Measurement Conference Distinguished Paper Award, two IRTF Applied Networking Research Prizes, and the Best of ACM SIGCOMM Computer Communication Review award. Dr. Dainotti received his Ph.D. in Computer Engineering and Systems at University of Napoli "Federico II", Italy. 


date: 2021-03-04
speaker: Chenshu Wu, University of Maryland, College Park
title: Wireless AI for the Internet of Things
location: Zoom
start_time: 11:00 am
end_time: 12:00 pm
abstract:
We are rapidly moving towards a ubiquitously connected, interactive world of AIoT, where pervasive sensing is highly demanded to bridge the cyber and physical space. Today's computers, despite their powerful computing and networking capabilities, still lack effective sensing of the physical world to make possible revolutionary applications for smart homes, digital health, autonomous vehicles, cybersecurity.
This talk will focus on wireless AI for seamless, high-precision sensing and tracking for smart homes, healthcare, cars, and beyond. I will introduce how we unleash the incredible potentials of pervasive wireless signals (such as WiFi) for a wide range of sensing applications, including home security, sleep monitoring, fall detection, gait recognition, all in a contactless and sensorless way without cameras or wearables, which have been commercialized as award-winning products. I will then present a large-scale, decimeter-level indoor tracking system using a single commodity WiFi Access Point, which is easy to install and can support an unlimited number of clients just like GPS does. The prototype system has been demoed at big companies like Apple, Qualcomm, HP, etc. I will conclude the talk with future directions towards advanced wireless AI for intelligent cyber-physical perception and interaction.

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Meeting ID: 955 0745 4738
Passcode: 213400

bio:
Chenshu Wu is an Assistant Research Scientist at the University of Maryland, College Park. He is also the Chief Scientist at Origin Wireless Inc., a spotlight AIoT company. His research focuses on wireless AIoT systems at the intersection of wireless sensing, ubiquitous computing, and the Internet of Things. He has published two books, 60+ papers in prestigious conferences and journals (such as SIGCOMM, NSDI, MobiCom, MobiSys, UbiComp, TMC, JSAC, TPDS, etc.), and 40+ filed/granted patents. His research has been commercialized as products, including LinkSys Aware that received the CES 2020 Innovation Award, HEX Home that won CES 2021 Innovation Award, and Origin Health Remote Patient Monitoring that won CES 2021 Best of Innovation Award. He received his B.S. and Ph.D. degree in Computer Science both from Tsinghua University.


date: 2021-03-05
speaker: Binghui Wang, Duke University
title: Machine Learning, Security, and Privacy: Challenges and Opportunities
location: Zoom
start_time: 11:00 am
end_time: 12:00 pm
abstract:
Machine learning provides more and more powerful tools for data analytics. On the other hand, security and privacy attacks increasingly involve data. Therefore, machine learning and security & privacy naturally intersect with each other as they both involve data, and there are many interesting questions at the intersections: i) How machine learning impacts security and privacy analytics design? ii) How security and privacy impact the deployment of machine learning systems?  
In this talk, I will first talk about machine learning for security and privacy in social networks, particularly, graph-based collective classification to detect fake accounts in social networks. A long-standing challenge in collective classification is that existing methods cannot learn accurate edge weights, thus resulting in limited detection performance in practice. I address this challenge by developing a general weight learning framework. Our framework is applicable to many collective classification methods and significantly enhances detection performance for state-of-the-art methods, while with an acceptable computational overhead. 
Second, I will present the work on stealing hyperparameters in machine learning. Hyperparameters are critical in machine learning and they are also deemed confidential because of their commercial value. I design a framework to accurately steal hyperparameters that are used to balance between the loss function and the regularization terms in the objective function of machine learning algorithms, and evaluate the attack theoretically. I also show that the hyperparameter stealing attack can be used by users to save economic costs when they use machine-learning-as-a-service platforms, e.g., Amazon machine learning,  to train machine learning models. I further study countermeasures. The results highlight the need for new defenses against the hyperparameter stealing attack. 
Finally, I will describe my future research plan on building machine learning systems that are provably secure and privacy-preserving and extending my research from cyber security and privacy to cyber-physical security and privacy.

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Meeting ID: 989 4126 2728
Passcode: 217375

bio:
Binghui Wang is currently a Postdoctoral Researcher in Electrical and Computer Engineering at Duke University. He obtained his Ph.D. from the Electrical and Computer Engineering Department at Iowa State University in July 2019. His research interests are trustworthy machine learning, data-driven security and privacy, and machine learning. His research has been published in top-tier security conferences such as IEEE S & P, CCS, NDSS, and AI/data mining/networking conferences such as NeurIPS, ICLR, WWW, AAAI, INFOCOM, and ICDM. His work has received several honors and awards, such as 2020 DeepMind Best Extended Abstract Award, 2019 NDSS Distinguished Paper Award Honorable Mention, 2017 INFOCOM Paper for Fast Tracking, and has been also deployed by JD, the second-largest e-commerce company headquartered in China. 


date: 2021-03-23
speaker: Tianhao Wang, Purdue University
title: Sensitive Data Analytics with Local Differential Privacy
location: Zoom
start_time: 11:00 am
end_time: 12:00 pm
abstract:
When collecting sensitive information, local differential privacy (LDP) can relieve users' privacy concerns, as it allows users to add noise to their private information before sending data to the server. LDP has been adopted by big companies such as Google and Apple for data collection and analytics. My research focuses on improving the ecosystem of LDP. In this talk, I will first share my research on the fundamental tools in LDP, namely the frequency oracles (FOs), which estimate the frequency of each private value held by users. We proposed a framework that unifies different FOs and optimizes them. Our optimized FOs improve the estimation accuracy of Google's and Apple's implementations by 50% and 90%, respectively, and serve as the state-of-the-art tools for handling more advanced tasks. In the second part of my talk, I will present our work on extending the functionality of LDP, namely, how to make a database system that satisfies LDP while still supporting a variety of analytical queries.

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Meeting ID: 970 0267 9622
Passcode: 942948

bio:
Tianhao Wang is a Ph.D. candidate in the department of computer science, Purdue University, advised by Prof. Ninghui Li. He received his B.Eng. degree from software school, Fudan University in 2015. His research area is security and privacy, with a focus on differential privacy and applied cryptography. He is a member of DPSyn, which won several international differential privacy competitions. He is a recipient of the Bilsland Dissertation Fellowship and the Emil Stefanov Memorial Fellowship.


date: 2021-03-25
speaker: Rajesh Kumar, Syracuse University
title: Toward a Securer, Smarter, and Healthier World!
location: Zoom
start_time: 9:00 am
end_time: 10:00 am
abstract:
The rapid evolution and acceptance of smart and wearable devices have enabled us to record almost every aspect of our lives. For example, we can precisely record behavioral activities such as walking, swiping, typing, browsing, drone flying; and health indicators such as blood–pressure, temperature, pulse, stress level, seizure, Parkinson, and fall detection in the elderly. The ability to record such life patterns and the advancement in machine intelligence has opened several doors for research, development, and innovation. The first half of the talk will discuss some interesting possibilities and challenges in the dimensions of security, privacy, and health. While in the second half, will focus on the vulnerability of behavioral biometric systems to sophisticated attacks.  

Zoom Link: https://gmu.zoom.us/j/97503725557

bio:
Rajesh Kumar is currently a Ph.D. Candidate at Syracuse University, NY, and a visiting faculty at Haverford College, PA. He holds an MS in Computer Applications from Jawaharlal Nehru University (JNU), New Delhi, and another MS in Mathematics from Louisiana Tech University, LA. His research focuses on harnessing the power of ever-evolving smart devices, wearables, and machine intelligence. He has 2.5 years of experience as an IT executive, 1.2 years of experience as a software development engineer, seven years of experience in graduate-level research, and five semesters of teaching experience.


date: 2021-03-26
speaker: Linghan Zhang, Florida State University
title: CYSE Tenure-Track Faculty Candidate Seminar #1
location: Zoom
start_time: 9:00 am
end_time: 10:00 am
bio: Linghan Zhang is a Ph.D. candidate in Computer Science at Florida State University.
abstract:
Research is in Mobile and IoT security with an emphasis on discovering new human biometrics, inventing biometric sensing methods, designing practical systems for user authentication and identification, and liveness detection for improved mobile and IoT security.

Zoom Link:  https://gmu.zoom.us/j/94719042947
 


date: 2021-03-29
speaker: Bin Hu, Old Dominion University
title: CYSE Tenure-Track Faculty Candidate Seminar #2: Toward Safe and Secure Vehicular Networked Systems
location: Zoom
start_time: 9:00 am
end_time: 10:00 am
abstract:
Vehicular Networked Systems (VNS) are cyber-physical systems composed of  autonomous vehicles exchanging information over vehicular communication networks. A safe and secure VNS is capable of maintaining active situational awareness of surrounding threats, and recovering from adversarial attacks with safety guarantee. Building a safe and secure VNS, however, is challenging because the vehicular networks are vulnerable to malicious attacks such as jamming attacks, and are subject to deep fading where characteristics of communication channels stochastically change as a function of the vehicle’s physical states. To address these cybersecurity and safety challenges in VNS, I will present a novel probabilistic approach that characterizes both jamming attacks and state-dependent channel fading in a unified framework. This probabilistic framework generalizes the state-of-art models for jamming attacks and channel fading. Building upon this probabilistic model, I will then show the development of a distributed switching control strategy to address the safety and security issues in VNS where both jamming attacks and channel fading are present. Moreover, I will introduce a novel event-based adaptive power control scheme to ensure efficient use of communication resources while preserving system security against jamming attacks. Other on-going work and future research directions will be discussed at the end of this talk.

Zoom Link:  https://gmu.zoom.us/j/98284315473

bio:
Dr. Bin Hu is currently an Assistant Professor in the Department of Engineering Technology at Old Dominion University, where he has been a faculty member since 2017.  He received his Ph.D. degree from the Department of Electrical Engineering at the University of Notre Dame in 2016. Prior to joining ODU, he was a research intern at Mitsubishi Electric Research Lab (MERL) from May 2015 to August 2015.
 


date: 2021-03-31
speaker: Vijay Shah, Virginia Tech
location: Zoom
start_time: 9:00 am
end_time: 10:00 am
title:
CYSE Tenure-Track Faculty Candidate Seminar #3: Artificial Intelligence-Driven 5G and Beyond Radio Access Networks (RANs)

abstract:
On contrary to previous generation mobile wireless networks, 5G and Beyond (B5G) wireless networks are expected to undergo an unprecedented transformation and revolutionize wireless evolution to support a wide variety of heterogeneous quality of service (QoS) provisioning, such as, mobile broadband, ultra-reliable low-latency communications, and massive machine connectivity etc. These multi-dimensional QoS requirements stem from exponential data traffic growth due to massive internet of things, autonomous vehicles, unmanned aerial systems, telehealth, AR/VR application, and many other, yet unknown use cases, such as, internet of everything. Consequently, B5G wireless networks, particularly, radio access networks (RANs) are becoming increasingly smart bringing in automation with artificial intelligence (AI) and machine learning technologies. In this talk, we discuss a few recent works where we designed AI techniques to solve key challenges in B5G RANs (especially, PHY and MAC layers). Following this, we briefly present an emerging industry-driven open radio access network (O-RAN) architecture that provides sufficient openness, flexibility, and well-defined interfaces to incorporate intelligence in RANs. We conclude this talk with an overview on the future outlook of the exciting area of AI-driven B5G RANs.

Zoom Link:  https://gmu.zoom.us/j/93131934658

bio:
Vijay K. Shah is a Research Assistant Professor in the Bradley Department of Electrical and Computer Engineering at Virginia Tech, and affiliated with Wireless@Virginia Tech. He is also a faculty member of Commonwealth Cybersecurity Initiative (CCI), a state-wide initiative to foster 5G wireless, autonomous systems, and cybersecurity research. He received his Ph.D. degree in Computer Science from University of Kentucky in 2019. His research interests include Artificial Intelligence (AI) for Wireless Networks, 5G and Beyond Cellular Networks, UAV Communications and Networking, mmWave Communications, Internet of Things (IoT), Age of Information (AoI), Vehicle-to-Everything (V2X), Networked Music Collaboration, Multi-band Spectrum Sharing and Cybersecurity. He leads the development efforts for 5G Open Radio Access Network (O-RAN) enabled wireless network testbed at Wireless@Virginia Tech. His research has been supported by several federal, state and industry grants, including NSF, DoD, ARL, CCI, IAI, and AT&T Labs.


date: 2021-03-30
speaker: Bo Liu, Auburn University
title: Towards Trustworthy Decision-Making and AI: Explainability and Safety
location: Zoom
start_time: 11:00 am
end_time: 12:00 pm
abstract:
In this talk, I will discuss my recent work towards trustworthy artificial intelligence, particularly trustworthy decision-making. Many companies are now building self-driving vehicles and medical robots, and the development of advanced autonomous systems is already a billion-dollar industry. These new technologies offer oversight, advanced automation, and autonomous instruments, and they are adaptable to changing situations, knowledge, and constraints. However, introducing new technologies into our technical and social infrastructures has profound implications and requires establishing confidence in their behavior to avoid potential harm. Therefore, the effectiveness and broader acceptability of autonomous smart systems rely on these systems' ability to explain their decisions. Building trust in artificial intelligence (AI) systems is a critical requirement in human-robot interaction and essential for realizing the full spectrum of AI's societal and industrial benefits.

This talk identifies two critical factors for establishing the trustworthiness of autonomous systems: explainability and safety. First, to achieve human-level interpretability, I propose new algorithms leveraging symbolic AI and data-driven ML to enable real-world applications. Particularly, I investigate an explainable and data-efficient hierarchical sequential decision-making framework based on symbolic planning and deep reinforcement learning, termed Symbolic Deep Reinforcement Learning (SDRL, IJCAI'2018, AAAI'2019, ICLP'2019). This approach achieves state-of-the-art results on the most challenging Atari games, Montezuma's Revenge, and outperforms other methods by a large margin. Second, to enhance safety and risk-awareness in decision-making, I propose the Mean-Variance Policy search (MVP, NeurIPS'2018, JAIR'2018, ICML'2020, AAAI'2021) algorithm family. Instead of merely maximizing the expected mean of cumulative rewards in sequential decision-making, the MVP algorithm enables a trade-off between the mean and variance by utilizing the Legendre-Fenchel duality. Unlike conventional mean-variance optimization, which often has multi-timescale stepsizes to tune, this algorithm is single-time-scale and can thus scale up easily. Third, I will discuss a “magic” meta-algorithm framework that can “robustify” any off-the-shelf risk-oblivious decision-making algorithms. This line of work has a wide range of practical applications such as control, robotics, e-commerce, autonomous driving, and medical treatment.

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Meeting ID: 990 5151 7102
Passcode: 915413
 

bio:
Bo Liu is a tenure-track assistant professor in the Dept. of Computer Science and Software Engineering at Auburn University. He obtained his Ph.D. from Autonomous Learning Lab at the University of Massachusetts Amherst, 2015, co-led by Drs. Sridhar Mahadevan and Andrew Barto. His primary research area covers decision-making under uncertainty, human-aided machine learning, symbolic AI, trustworthiness and interpretability in machine learning, and their numerous applications to BIGDATA, autonomous driving, and healthcare informatics. In his current research, he has more than 30 publications on several notable venues, such as NIPS/NeurIPS, ICML, UAI, AAAI, IJCAI, AAMAS, JAIR, IEEE-TNN, etc. His research is funded by NSF, Amazon, Tencent (China), Adobe, and ETRI (South Korea). He is the recipient of the Conference on Uncertainty in Artificial Intelligence (UAI)'2015 Facebook best student paper award and the Amazon research award in 2018. His research results have been covered by many prestigious venues, including the classical textbook "Reinforcement Learning: An Introduction" (2nd edition), NIPS'2015/IJCAI'2016/AAAI'2019 tutorials. He is an Associate Editor of IEEE Transactions on Neural Networks and Learning Systems (IEEE-TNN), a senior member of IEEE, and a member of AAAI, ACM, and INFORMS.


date: 2021-04-01
speaker: Shangqing Zhao, University of South Florida
location: Zoom
start_time: 9:00 am
end_time: 10:00 am
title:
CYSE Tenure-Track Faculty Candidate Seminar #4: Measurement Integrity in Network Tomography and Resource Allocation: Attacks and Defenses 

abstract:
Network measurement is an essential way to gain the understanding on how well a network operates and allocates its resources. In this talk, two new attack strategies against network measurement and resource allocation will be discussed. The first attack is called measurement integrity attack targeting the network tomography, which is an important tool to estimate link metrics from end-to-end network measurements. Unlike conventional data integrity problems that are usually protected by standard methods (e.g., encryption and authentication), a key challenge associated with measurement integrity attacks is that the facts (e.g., packet transmission/delivery timings) during network measurement cannot be protected by such standard methods, but can be easily manipulated by malicious attackers. The basic idea of this attack is to intentionally delay or drop packets at malicious nodes to manipulate end-to-end measurements in a way such that a legitimate node is incorrectly identified by network tomography as the root cause of the problem, thereby becoming a scapegoat. The second attack is the Learning-Evaluation-Beating attack against the resource allocation in the cooperative spectrum sensing system. Based on the black-box nature of the fusion center in cooperative spectrum sensing, our new perspective is to make the adversarial use of machine learning to construct a surrogate model of the fusion center’s decision model. We propose a generic algorithm to create malicious sensing measurements using this surrogate model. The defenses against the two types of attacks will also be discussed.  

Zoom Link: https://gmu.zoom.us/j/96242010304 

bio:
Shangqing Zhao is currently a Ph.D. candidate of Systems and Security in the Department of Electrical Engineering and Florida Center for Cybersecurity at the University of South Florida. In his Ph. D. study, his research primarily focuses on the network and mobile system design and security. His research results have been published in top-tier conferences (e.g., MobiCom, NSDI, INFOCOM, CCS, ICDCS) and journals (e.g., IEEE Transactions on Mobile Computing, IEEE Transactions on Dependable and Secure Computing, IEEE Transactions on Wireless Communications). He also served as a reviewer for multiple top academic journals and conferences. He is a student member of IEEE and ACM. 


date: 2021-04-02
speaker: Dr. Julian Rrushi, Oakland University, Michigan
location: Zoom
start_time: 9:00 am
end_time: 10:00 am
title:
CYSE Tenure-Track Faculty Candidate Seminar: Malware in the Electrical Power Grid: Attacks and Defenses

abstract:
In addition to exploits and command & control modules, malware that are crafted to attack the electrical power grid have a physics-centric nucleus. These malware understand physics, and hence use their knowledge to guide how they initiate physical damage to power grid equipment on a compromised industrial computer. This sample class delivers methods of attack and defense. The class begins with background on some of the data and the algorithms that malware work with when they operate in the electrical power grid. The class continues with a description of attacks that malware implement to cause physical destruction of a power transformer in an electrical substation. The class then provides concepts of cyber deception engineering that detect malware and disrupt their decision making. More specifically, I explain a physics-driven page fault handler in the seL4 microkernel, which, in addition to reducing the page fault rate, differentiates active physics in main memory from passive physics in the backing store. Active physics is leveraged to develop cyber deception that is customized to attack the physics-centric nucleus of malware. This sample class implements student-centered learning, and includes in-class student activities.

Zoom Link: https://gmu.zoom.us/j/94884075445

bio:
Dr. Julian Rrushi is a tenure-track assistant professor of engineering at Oakland University, Michigan. He carries out research at the intersection of operating systems, computer architectures, data science and artificial intelligence to break new ground in cybersecurity. His research projects are supported by the U.S. Department of Defense, the U.S. Department of Energy, and industry. Prior to commencing his academic career, Dr. Rrushi was a vulnerability researcher with industry for several years. He received a B.S. in Computer Science, an M.S. in Information Technology, and a Ph.D. in Computer Science, all from the University of Milan. His Ph.D. work focused on detection of malware and exploits in cyber-physical systems. Dr. Rrushi is a recipient of the Young Faculty Award from DARPA, class of 2020, for research on the cyber physics of computing machines and networks.


date: 2021-04-05
speaker: Dr. Cong Wang, Old Dominion University
title: CYSE Faculty Candidate Seminar: Explore Security and Privacy in Deep Image Retrieval Systems
location: Zoom
start_time: 9:00 am
end_time: 10:00 am
abstract:
The rapid growth of large image and video collection has made content-based image retrieval possible at a large scale, e.g., Google, Pinterest, Bing, TinEye and Alibaba. Powered by deep learning, these techniques have been increasingly built into social networks, e-commerce and fashion design to capture semantic similarities from visual queries for finer results. Unfortunately, by inheriting the backend from deep learning, these systems are also vulnerable to the well-known adversarial examples.
 
In today’s talk, we will look at the security and privacy side in hash-based image retrieval systems. For search accuracy, service providers typically maintain a growing database via automated crawling and indexing of images online. When private or copyrighted images are collected into the database, an adversary can extract those images by querying similar images from the target categories. We will develop a privacy-preserving mechanism based on adversarial examples to “stash” the private images in the deep hash space while maintaining perceptual similarity. Then we will take a look at the security problem when attackers embed malicious images into the database and point them to specific categories to be retrieved by the user. We will develop a practical method to enhance black-box transferability for targeted attacks.
We will conclude this talk with a discussion of the ongoing challenges and connections between security and computation, along with the directions for future works across the hardware and software stacks.  

Zoom Link: https://gmu.zoom.us/j/99398956647

bio:
Dr. Cong Wang is an Assistant Professor from the Computer Science Department at the Old Dominion University in Norfolk, VA. He is also affiliated with the Center of Cybersecurity Research and Education. Cong’s research focuses on addressing security and performance challenges in Mobile/Cloud Computing, IoT, Machine Learning and Systems. He has published in networking, distributed/mobile computing, AI and security conferences and journals including INFOCOM, IPDPS, ICDCS, CVPR, IJCAI, CCS, ACM MM, PERCOM, IEEE Trans. On Mobile Computing, IEEE Trans. On Computers and IEEE Trans. On Distributed and Paralleled Systems. He received his Ph.D from the Department of Electrical and Computer Engineering, Stony Brook University(16’), B. Eng in Information Engineering from the Chinese University of Hong Kong (08’) and M. Sc in Electrical Engineering from Columbia University (09’). He is the recipient of IEEE PERCOM Mark Weiser Best Paper Award in 2018, COVA-CCI Cybersecurity Research and Innovation Award, ODU’s Richard Cheng Innovative Research Award in 2020, and NSF CAREER Award in 2021.


date: 2021-04-06
speaker: Dr. Yanhai Xiong, Dartmouth College
title: CYSE Faculty Candidate Seminar: AI for Cyber-Physical Systems: Cyber Security and Efficiency
location: Zoom
start_time: 9:00 am
end_time: 10:00 am
abstract:
Cyber-physical systems are transforming the way people interact with physical systems by combining a cyber network. For the successful development of cyber-physical systems, there are multiple important factors, among which I focus on two key factors: cyber security and efficiency. In this talk, I mainly introduce a research work on improving the cyber security of a specific platform, the Android platform. The malware performs malicious behavior on Android devices and puts user privacy and property at risk. To improve the performance and robustness of malware detection, three new techniques for feature transformation are innovated. I will demonstrate how the new features efficiently aid the standard machine learning detectors and improve the robustness. In the rest of the talk, I will briefly present some other studies on cyber-physical systems' cyber security and efficiency and my research plan. 

Zoom Link: https://gmu.zoom.us/j/94046376366

bio:
Dr. Yanhai Xiong is a postdoc working at Dartmouth College with Professor VS Subrahmanian. She obtained her Ph.D. degree from Nanyang Technological University, Singapore, under the supervision of Prof. Bo An. Her research interests include Game Theory, optimization, and applied machine learning.  Her current research focuses on applying AI techniques to cyber-physical systems to enhance cyber security and improve efficiency.


date: 2021-05-28
speaker: Abubakr Abdulgadir, The Volgenau School of Engineering, George Mason University
location: Zoom
start_time: 12:00 pm
end_time: 1:00 pm
title:
Secure Hardware Implementations of Lightweight and Post-Quantum Cryptography; Trade-offs, Applicability, and Tools 

abstract:
Lightweight authenticated ciphers are crucial in many resource-constrained applications, including online payment, edge computing, and the Internet of Things. However, the current standard AES-GCM is not adequate for many applications. Recent developments in Post-Quantum computing increased the urgency of deploying Post-Quantum cryptographic standards that withstand quantum and classic attacks. As a response to these needs, the US National Institute of Standards (NIST) coordinates two standardization processes to evaluate and ratify suitable algorithms. Although resistance to mathematical cryptanalysis is paramount, cost, performance, and resistance to side-channel attacks are among the most critical selection criteria.  
 
In this research, we study the effect of applying side-channel countermeasures to the NIST LWC candidates Xoodyak and TinyJAMBU and the NIST PQC candidate Saber with a concentration on hardware implementations. Specifically, we employ the Domain-Oriented masking countermeasure to produce two first-order protected implementations of Xoodyak. For TinyJAMBU, we present a flexible implementation that is synthesizable for arbitrary order of protection. For Saber, we first develop a lightweight design and then apply countermeasures to algorithm building blocks.  
 
In parallel, we developed the tools necessary for this research to improve on the existing FOBOS platform. We significantly improved the efficiency and flexibility of the system while using affordable hardware components. Our upgrades resulted in an efficient system capable of performing leakage assessment and attacks on FPGA cryptographic implementations. We also improved the usability of the system to address research and education needs. Our work aims at establishing methodologies and providing tools to develop and evaluate cryptographic candidates. Also, our results provide a more realistic look at the practical cost and performance of the algorithms studied. 

Zoom Meeting Link: https://gmu.zoom.us/j/96947328909 (CORRECTED START TIME)

bio:
Abubakr Abdulgadir is a doctoral candidate in Electrical and Computer Engineering. He holds a BS degree and an MS degree from University of Gezira.


date: 2021-06-01
speaker: Kimia Zamiri Azar, The Volgenau School of Engineering, George Mason University
location: Zoom
start_time: 10:00 am
end_time: 11:00 am
title:
Vulnerability Assessment of Logic Locking Techniques: Towards Next Generation Attacks on Logic Locking

abstract:
The increasing cost of integrated circuits (IC) manufacturing has forced many design houses to become fabless. Outsourcing the stages of the manufacturing supply chain to the third-party facilities has introduced multiple forms of security threats. To combat these threats, logic locking introduces the process of hiding the correct functionality of a circuit using key-programmable logic gates. However, the introduction of the satisfiability (SAT) attack has undermined the effectiveness of many existing logic locking solutions.  

This dissertation, after showing the shortcomings and limitations of the SAT attack, introduces the newer and stronger attack approaches with much more capabilities and performance compared to the existing ones. It introduces the satisfiability modulo theory (SMT) attack, in which the adversary has the capability of modeling non-Boolean logic locking mechanisms using theory solvers. SMT attack is the first of its kind that can model non-Boolean characteristics of the circuit. Then, we introduce the neural network guided SAT (NNgSAT) attack that exploits the benefit of a message passing neural network (MPNN) to reduce the complexity of the de-obfuscation model, especially when complex structures, such as routing modules and multipliers, are parts of the logic locked circuits. 

In general, this thesis aims to provide an assessment of the capabilities and limitations of the existing studies on logic locking, with more concentration on attack mechanisms. By introducing newer and stronger approaches, this thesis also opens new directions for the designers to evaluate the security of the designs using more appropriate and well-formulated mechanisms, leading to stronger and more reliable design and implementation with enhanced security.   

Zoom Meeting Link: https://gmu.zoom.us/j/97080445863

bio:
Kimia Zamiri Azar is a doctoral candidate in the Electrical and Computer Engineering PhD program.. She holds a BS degree from K.N.Toosi University of Technology and an MS degree from Shahid Beheshti University.


date: 2021-06-04
speaker: Amir Alipour-Fanid, The Volgenau School of Engineering, George Mason University
title: Machine Learning for Wireless Cyber-Physical Systems Security
location: Zoom
start_time: 3:00 pm
end_time: 4:00 pm
abstract:
Wireless cyber-physical systems (CPS) have been progressively adopted in many applications such as smart industrial control systems, intelligent vehicular transportation, unmanned aerial vehicles (UAV), etc. Despite the CPS huge potential benefits, the open and broadcast nature of the wireless communication medium has made these systems vulnerable to cyber-attacks. In this dissertation, we discuss four research projects related to the security of CPS applications summarized as follows. 1) We study security of remote state estimation in wireless CPS where a sensor sends its measurements to the remote state estimator over a multi-channel wireless link in presence of a jamming attacker. We propose a novel online learning-based policy which can be employed by the sensor to jointly choose the transmission channel and power to defend against the attack. We theoretically prove that the proposed algorithm achieves a sublinear order-optimal learning regret bound in time. 2) We focus on the security of multi-channel wireless communication systems with a scenario in which the jammer always successfully attacks on the acknowledgment link and the transmitter loses throughput due to dynamic channel switching latency. We model this problem as a self-unaware bandit with arm switching costs problem and propose two novel online learning algorithms with theoretical performance guarantees. We prove sublinear regret upper bound for both algorithms and bound the switching costs such that it can improve the regret bound. 3) We study the security of cooperative adaptive cruise control (CACC) system under jamming attacks. We propose a time domain approach to analyze the mean string stability and impact of the jammer's location on the string stability. As a defense strategy, we derive the mean string stability condition with respect to the minimum packet loss probability and number of channels, when both the vehicles and jammer employ online learning-based channel access policies for data transmission and attack, respectively. 4) Finally, we study detecting and identifying intruding consumer UAVs as an urgent need for both invasion detection and forensics purposes. We propose a machine learning-based framework for fast UAV identification over encrypted Wi-Fi traffic. The framework jointly optimizes feature selection and prediction performance in a unified objective function. Furthermore, we identify the UAVs' operation mode through data traffic analysis which implies that there is a strong correlation or coupling between cyber information (data traffic) and physical information (operation mode) of UAVs. This finding is expected to motivate new cyber-physical defense and forensics mechanisms that leverage this cyber-physical coupling.

Zoom Meeting Link: https://gmu.zoom.us/j/95027287209
Meeting ID: 950 2728 7209

bio:
Amir Alipour-Fanid is a doctoral candidate in Electrical and Computer Engineering. He holds a BS degree from Islamic Azad University of Ardabil and an MS degree from University of Tabriz.


date: 2021-07-22
speaker: Zili Zha, Volgenau School of Engineering, George Mason University
title: Towards Efficient and Scalable SDN-based Data Center Monitoring and Management
location: Zoom
start_time: 10:00 am
end_time: 12:00 pm
abstract:
Recent years have witnessed the great success of cloud computing that is powered by data center systems. As an essential building block for many of our cyber infrastructures, the monitoring, security, and management of data center traffic is of ultimate importance. However, the ever-increasing scale and speed of data center networks (DCNs) pose various challenges to data center traffic monitoring and management. The traditional monitoring approaches are neither flexible nor efficient to meet the security and performance needs of DCNs. With the flexibility and programmability of Software-Defined Networking (SDN) technology, this dissertation presents efficient solutions to address DCNs monitoring and management challenges.
 
First, we investigate the feasibility and efficiency of building software-defined monitoring (SDM) functionalities into network edges. For this purpose, we design and implement four measurement schemes in Open vSwitch (OVS), by either integrating forwarding and measurement functions into a pipeline, or decoupling them into parallel operations. We further include another eBPF-based approach in our comparative study. Then, we quantitatively show the various trade-offs among network performance and system overhead that different schemes strike to balance, and demonstrate the feasibility of instrumenting OVS with monitoring capabilities. Second, we leverage the flexibility and efficiency offered by SDM to enhance the security of DCNs. Accordingly, we design BotSifter, an SDN-based scalable, accurate and runtime bot detection framework for DCNs. To improve the detection scalability, BotSifter utilizes centralized learning with distributed detection by distributing detection tasks across the network edges. Furthermore, it employs multiple novel mechanisms for parallel detection of Command and Control (C&C) channels and botnet activities, which greatly enhances the detection robustness. Third, the traffic surges in DCNs are common, due to the attacking (e.g., via botnets) traffic or the increase of legitimate traffic. They can easily degrade the performance of containerized clouds these days. To address this challenge, we further propose to adaptively offload the DCNs traffic and optimize the network virtualization performance in DCNs. For this purpose, we propose EZPath, a novel system that can seamlessly expedite container traffic by leveraging the programmable Top-of-Rack (ToR) switches in clouds. EZPath dynamically offloads selected (e.g., performance-critical) network flows to the in-network programmable hardware, which not only optimizes the network performance, but also effectively mitigates the negative impact of attack traffic.

Zoom Link: https://gmu.zoom.us/j/91279610162?pwd=emY1OUlrN0VYM1hWMTkvVXVqMVhvUT09

Meeting ID: 912 7961 0162
Passcode: 098858

bio:
Zili Zha is a doctoral candidate in Computer Science at George Mason University.  She holds a Bachelor of Science degree from the University of Science and Technology of China (2009) and a Master of Science degree from the College of William and Mary (2013).


date: 2021-07-26
speaker: Shervin Roshanisefat, Volgenau School of Engineering, George Mason University
title: Implementation and Assessment of Strong Logic Encryption Techniques
location: Zoom
start_time: 4:00 pm
end_time: 5:30 pm
abstract:
The cost of building a new semiconductor fab was estimated to be $5.0 billion in 2015, with large recurring maintenance costs, and sharply increases as technology migrates to smaller nodes. Thus, to reduce the fabrication cost, and for economic feasibility, most of the manufacturing and fabrication is pushed offshore. However, many offshore fabrication facilities are considered to be untrusted, which has raised concern over potential attacks in the manufacturing supply chain. Hence, fabrication in untrusted fabs has introduced multiple forms of security threats from supply chain including that of overproduction, Trojan insertion, reverse engineering, intellectual property theft, and counterfeiting.
 
To prevent the adversaries from such attacks, various countermeasures for hiding and/or locking the functionality of a netlist are proposed. Logic encryption is one of these methods that hides the functionality of an IP by building ambiguity or by implementing post-manufacturing means of control and programmability into a netlist. In this dissertation, I will present cycle-based logic locking for combinational circuits and deep faults locking for encrypting sequential circuits. Then, I will discuss RANE, a framework that we developed for research community to evaluate robustness of their encryption techniques against the state-of-the-art attacks.

Zoom Meeting Link: https://gmu.zoom.us/j/97807070569

bio:
Shervin Roshanisefat is a doctoral candidate in the Electrical and Computer Engineering Department at George Mason University.  He holds a Bachelors of Science degree from Azad University and a Master of Science degree from the University of Tehran.


date: 2021-10-21
speaker: Dr. Kun Sun, George Mason University
title: Cache-based Attacks Against ARM TrustZone
location: Research 163 or Zoom
start_time: 10:00 am
end_time: 11:00 am
abstract:
ARM processors provide a hardware security extension called TrustZone to protect security sensitive code and data running in a trusted execution environment. Our research points out that it is critical to protect the CPU caches when developing TrustZone-based security systems. First, we observe an ARM TrustZone cache incoherence behavior, which results in the cache contents of the two worlds, secure world and normal world, potentially being different even when they are mapped to the same physical address. Based on this observation, we develop a new cache-based rootkit called CacheKit that hides in the cache of the normal world and is able to evade memory introspection from the secure world. Second, researchers propose to create Isolated Execution Environments (IEEs) in the normal world to protect the security sensitive applications. However, we discover three cache-based attacks called CITM that can be leveraged to manipulate the sensitive data protected in IEE systems. Specifically, due to the inefficient and incoherent security measures on the cache that maps to the IEE memory, attackers in the normal world may compromise the security of IEE data by manipulating the IEE memory during concurrent execution, bypassing the security measures enforced when a security sensitive application is suspended or finished, or misusing the incomplete security measures during IEE’s context switching processes.

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bio:
Dr. Kun Sun is an associate professor in the Department of Information Sciences and Technology at George Mason University. He is also the director of Sun Security Laboratory and the associate director of the Center for Secure Information Systems. He received his Ph.D. in Computer Science from North Carolina State University in 2006. Before joining GMU, he was an assistant professor in College of William and Mary. Dr. Sun has more than 15 years working experience in both academia and industry, and his research work has been funded by government agencies including the National Science Foundation, the Office of Naval Research, the Army Research Office, the Army Research Office, the Air Force Research Laboratory, the Department of Homeland Security, and the National Institute of Standards and Technology. His research focuses on systems and network security. He has publishing over 100 conference and journal papers, and two papers won the Best Paper Award. His current research focuses on trustworthy computing environment, moving target defense, smart phone security, network security, AI/ML security, and software security. He has been serving as a faculty senator since 2018.


date: 2021-11-12
speaker: Suresh Venkatasubramanian, PhD
title: Algorithmic Fairness: Collisions and Collaborations Between Technology and Society
location: Research 163 and Zoom
start_time: 11:00 am
end_time: 12:00 pm
abstract:
The field of "algorithmic fairness" studies the collisions, and potential collaborations, between a tech-drenched society and issues of justice and equitability. My research over the past 8 years has been about establishing a field whose objective is to understand the societal impact of automation, and especially automation that arises from the data-rich area of machine learning (ML).

In this talk I will take you through two vignettes of my research that illustrate the deep and complex interplay between formalisms, algorithms, and broader societal concerns. Firstly, I will discuss the transparency question of knowing why an algorithm makes a particular prediction -- the problem of explanations. I'll outline what we currently know about feature attribution - identifying how much a feature influences a prediction and present my research on the limits of these approaches, applying both mathematical and a broader societal lens. I will also link these insights to a legal consideration of explanations and our ability to foresee harms from ML.

Secondly, I'll talk about what it might mean to have a fair clustering. I'll describe definitions and algorithms, and introduce the idea of fair ACCESS --- whether it be about voting rights, access to testing and vaccines, or even access to information in a network - and how we should think about what equity means in this context.

I'll conclude with a  vision for where  research in this area is going next, centered around how we might use technology to support justice and more broadly aid in opportunities for human-flourishing.

.................................................................................................................................................................
For in-person attendance: we are following University guidelines so if you are attending the on-campus Distinguished Lecture Series talk, you need to RSVP through the below link:

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Participants must complete Mason COVID Health✓™ and receive a “green light” status on the day of the event.

Masks are also required for attendance.

ZOOM Link:
https://gmu.zoom.us/j/92545949435?pwd=cUVwSjJ0UCsxR1VCWk5KMm9Wcjg3QT09
Meeting ID: 925 4594 9435
Passcode: 007407

bio:
Suresh Venkatasubramanian is a faculty member in computer science and data science at Brown University, currently on loan to the White House in the Office of Science and Technology Policy. His background is in theoretical computer science, and he’s taken a long and winding path through many areas of data science. For almost the past decade, he’s been interested in algorithmic fairness, and more broadly the impact of automated decision-making systems in society.

All views expressed in this talk are solely his own and do not represent the perspectives of any of the institutions with which he is affiliated.
______________________________________________________


date: 2022-01-25
speaker: Joshua Loyal, University of Illinois at Urbana-Champaign
title: An Eigenmodel for Dynamic Multilayer Networks
location: Zoom
start_time: 12:00 pm
end_time: 1:00 pm
abstract:
Network (or graph) data is at the heart of many modern data science problems: disease transmission, community dynamics on social media, international relations, and others. In this talk, I will elaborate on my research in statistical inference for complex time-varying networks. I will focus on dynamic multilayer networks, which frequently represent the structure of multiple co-evolving relations. Despite their prevalence, statistical models are not well-developed for this network type. Here, I propose a new latent space model for dynamic multilayer networks. The key feature of this model is its ability to identify common time-varying structures shared by all layers while also accounting for layer-wise variation and degree heterogeneity. I establish the identifiability of the model's parameters and develop a structured mean-field variational inference approach to estimate the model's posterior, which scales to networks previously intractable to dynamic latent space models. I apply the model to two real-world problems: discerning regional conflicts in a data set of international relations and quantifying infectious disease spread throughout a school based on the student's daily contact patterns. 

bio:
Joshua Loyal is a Ph.D. candidate in the Department of Statistics at the University of Illinois at Urbana-Champaign. He received an M.S. in Physics from Yale University and a B.S. in Physics and Mathematics from Duke University. In addition, from 2015 to 2018, he was a Data Scientist at DataRobot, a start-up in Boston aimed at building an automated machine learning platform. His research interests include statistical network analysis, Bayesian inference, machine learning, data science, and statistical computing. 
 
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date: 2022-02-11
speaker: Dr. En-hui Yang, University of Waterloo, Waterloo, Ontario, Canada
location: Zoom
start_time: 11:00 am
end_time: 12:00 pm
title:
Watermarking for Protecting Deep Image Classifiers against Adversarial Attacks: A Framework and Algorithms 

abstract:
Deep neural networks (DNNs) are vulnerable to adversarial examples, inputs with imperceptible, but subtly crafted perturbation that lead DNNs to produce incorrect outputs. The existence and easy construction of adversarial examples pose significant security risks to DNNs, especially in safety-critical applications, including visual object recognition and autonomous driving. After reviewing attack strategies and failed defense strategies, in this talk, we will present a novel watermarking-based framework for protecting deep image classifiers by detecting adversarial examples. The proposed framework consists of a watermark encoder, a possible adversary, and a detector followed by a deep image classifier to be protected. Specific methods of watermarking and detection will also be discussed. It is shown by experiment on a subset of ImageNet validation dataset that our framework along with the presented methods of watermarking and detection is effective against a wide range of advanced attacks (static and adaptive), achieving a near zero (effective) false negative rate for FGSM and PGD attacks (static and adaptive) with the guaranteed zero false positive rate. In addition, for all tested deep image classifiers (ResNet50V2, MobileNetV2, InceptionV3), the impact of watermarking on classification accuracy is insignificant with, on average, 0.63\% and 0.49\%  degradation in top 1 and top 5 accuracy, respectively. 

Zoom Meeting Link: https://gmu.zoom.us/j/91322937979 

bio:
En-Hui Yang is an Electrical and Computer Engineering Professor and founding director of the Leitch-University of Waterloo Multimedia Communications lab. He is also the co-founder of SlipStream Data Inc. (now a subsidiary of BlackBerry Inc. formerly known as Research In Motion), and a past associate editor for IEEE Transactions on Information Theory. Professor Yang holds a Canada Research Chair (Tier 1) in Information Theory and Multimedia Data Compression.

Dr. Yang received a Ph.D. in Electrical Engineering from the University of Southern California in 1996, a Ph.D. in Probability and Statistics from Nankai University in 1991, and a Bachelor's in Applied Mathematics from HuaQiao University in 1986.


date: 2022-02-15
speaker: Dr. Damon L. Woodard, University of Florida
title: Artificial Intelligence in Biometrics: Past, Present, and Future Challenges
location: Zoom
start_time: 12:00 pm
end_time: 1:30 pm
abstract:
Biometrics as a means of establishing identity has been widely adopted primarily due to the increased performance of these systems during the last few decades. A main reason for the improved performance has been the result of utilizing advanced artificial intelligence (AI) technologies in biometric systems. Despite the recent advances in AI, many open challenges remain in terms of AI-enabled biometrics. Dr. Damon Woodard will provide an overview of the application of AI in biometric systems and provide an in-depth discussion of open research challenges.

Zoom Meeting: https://metsci.zoomgov.com/j/1600095156?pwd=TEFSZndKWVltbmt3ZStreFB4QlVzZz09

 
Meeting ID: 160 009 5156          Passcode: 024752

bio:
Dr. Damon L. Woodard currently serves as the Director of AI Partnerships for the University of Florida’s Artificial Intelligence Initiative. He is an Associate Professor within the Electrical and Computer Engineering Department at the University of Florida, where he is a member of the Florida Institute for Cybersecurity (FICS) Research. His research focuses on the application of artificial intelligence to various problems in the cyber security and national security domains. Dr. Woodard is an IEEE Senior Member, an ACM Senior Member, a National Academy of Science Kavli Frontiers Fellow, and a member of the Association for the Advancement of Artificial Intelligence (AAAI). Dr. Woodard received his Ph.D. in Computer Science and Engineering from the University of Notre Dame, his M.E. in Computer Science and Engineering from Penn State University, and his B.S. in Computer Science and Computer Information Systems from Tulane University. Before becoming a faculty member, Dr. Woodard was a Director of Central Intelligence postdoctoral fellow.


date: 2022-02-23
speaker: Farahnaz Soleimani, Ph.D., Georgia Insititute of Technology
title: Toward Resilience of Civil Infrastructures: Machine Learning-based Probabilistic Assessment
location: ENGR 3507 (and Zoom)
start_time: 12:00 pm
end_time: 1:00 pm
abstract:
Civil infrastructures serve as the critical lifeline to connect communities with essential services for public health, economic productivity, and the operation of daily life. Disruption of critical infrastructure can cause devastating social and economic consequences, particularly in a catastrophic event such as an earthquake. With the increasing demand for improved infrastructure services in modern societies, establishing resilient infrastructure is essential for the long-term sustainable development of post-disaster recovery. This seminar presents the application of state-of-the-art machine learning algorithms in earthquake engineering, focusing on the performance-based seismic vulnerability and resilience assessment of civil systems, particularly bridges. This includes improving fragility curves, probabilistic seismic demand models, seismic resilience assessment, and analytical models of seismically-induced slope displacements. The overall outcome of this research empowers risk-informed mitigation strategies by developing physics-guided machine learning frameworks to tackle challenges towards a natural hazard resilient society.

Zoom Meeting ID: 964 6812 4632; Passcode: 442246.

 
We encourage everyone to attend and participate in person.  For those attending in person, make sure you received a green status on your daily Mason COVID Health Check <Mason COVID Health ✓™ >   and please remember to for RSVP this event. 

bio:
Farahnaz Soleimani is currently a Research Scientist at the Georgia Institute of Technology in the field of Structural Engineering and working in the research arm of the provost office. Prior to her current position, she was a post-doctoral scholar in the Machine Learning and Data Science Lab at Georgia Tech and Emory University. She received her BSc from Sharif University of Technology, and between 2013-2017 completed her MSc and PhD at Georgia Tech in Civil and Structural engineering, with a minor in Statistics. She also received an MEng degree in Civil engineering from Tufts University prior to joining Georgia Tech. Her research interest is mainly in the multi-hazard resilience assessment of structures and critical infrastructure systems. She develops computationally effective tools to better understand the seismic performance of structural systems by establishing predictive models and conducting sensitivity analysis. During her PhD study, in the Natural Hazards Mitigation Research Group at Georgia Tech, Dr. Soleimani developed an efficient approach to account for the irregularities existing in the bridge geometries towards developing a new generation of accurate fragility models for existing highway bridges in California. She has multiple leadership experiences such as serving as the Academic Relation Chair of the Society of Women in Engineering (SWE), and the Vice President of the Earthquake Engineering Research Institute (EERI). She has been the recipient of awards from Purdue Prospective Faculty program, SWE, and EERI, Georgia Tech Research and Innovative Conference, Student Government Association of Georgia Tech, and Doctoral Student Professional Development of the College of Engineering.  


date: 2022-02-14
speaker: Michael Coblenz, University of Maryland, College Park
title: Squashing Bugs and Empowering Programmers with User-Centered Programming Language Design
location: Zoom
start_time: 11:00 am
end_time: 12:00 pm
abstract:
Programming languages are simultaneously formal systems and user interfaces with which programmers work. Unfortunately, programmers have a hard time writing safe software: serious bugs and security vulnerabilities are common. In many cases, however, languages with strong safety guarantees have been hard to use. In this talk, I’ll discuss user-centered design methods I developed to help language designers create languages that are easier to use. I’ll show how I created and evaluated Obsidian, a new smart contract language that uses a linear type system, and Bronze, a new garbage collector for Rust. I found that programmers are more effective when using the resulting languages than when using prior languages.

ZOOM: https://gmu.zoom.us/j/92337272429?pwd=OGF1YTB1emtRWDRDSmxncVNPNlUwZz09
Meeting ID: 923 3727 2429
Passcode: 411343

bio:
Michael Coblenz is a Basili Postdoctoral Fellow at the University of Maryland. Michael developed the PLIERS method, which integrates user-centered methods into the design process for safe programming languages. He created Glacier, an immutability system for Java; Obsidian, a strongly-typed language for smart contracts; and Bronze, a garbage collector that improves usability of Rust. He holds a Ph.D. in computer science from Carnegie Mellon University. Previously, he was a Senior Software Engineer at Apple. His work, which is at the intersection of programming languages, software engineering, and human-computer interaction research, has been published at OOPSLA, TOPLAS, ICSE, and TOCHI.


date: 2022-02-28
speaker: Garrett S. Rose, Ph.D., University of Tennessee, Knoxville
title: Memristive Nanoelectronics in Neuromorphic Computing and Considerations for Hardware Security
location: ENGR 4201 and Zoom
start_time: 11:00 am
end_time: 12:00 pm
abstract:
Although CMOS technology scaling has been remarkable, it is also true that a significant slow-down in such scaling has occurred in recent years. This slower pace of technology advancement also means that modern computer systems can no longer rely exclusively on scaling for performance enhancements. Now is the time for circuit and system designers to consider alternative approaches to computer system architectures. One such approach is neuromorphic computing which uses models for biological neural systems to construct brain-inspired computer systems. Two basic components are required for a typical neuromorphic system: neurons that fire based on spike patterns received and synapses that provide weighted connections between neurons. Here we consider spiking neuromorphic systems where neurons are implemented using analog/mixed-signal CMOS and synapses are built from memristors (e.g. ReRAM). The specific neuromorphic framework presented also allows for recurrent pathways in the networks constructed, a feature that is particularly useful for stateful neuromorphic processing at the edge. We urther consider emerging memristive technology and novel computer architectures, such as neuromorphic, through the lens of hardware security. Specifically, memristor-based hardware security primitives are discussed in the context of lightweight security applications for embedded systems. While the emphasis is on what we can do with memristive nanoelectronics from a circuits and systems design perspective, early implementations of fabricated hybrid CMOS/memristive chips will also be presented and discussed.

Note for people joining in-person only:
Participants must complete Mason COVID Health✓™ and receive a “green light” status on the day of the event.
Please send your RSVP request to ece@gmu.edu with the info of seminar date, seminar title, your name.

Zoom link: https://gmu.zoom.us/j/94920236381

bio:
Garrett S. Rose received the B.S. degree in computer engineering from Virginia Polytechnic Institute and State University (Virginia Tech), Blacksburg, in 2001 and the M.S. and Ph.D. degrees in electrical engineering from the University of Virginia, Charlottesville, in 2003 and 2006, respectively. His Ph.D. dissertation was on the topic of circuit design methodologies for molecular electronic circuits and computing architectures. 
Presently, he is a Professor in the Min H. Kao Department of Electrical Engineering and Computer Science at the University of Tennessee, Knoxville where his work is focused on research in the areas of nanoelectronic circuit design, neuromorphic computing and hardware security. Prior to that, from June 2011 to July 2014, he was with the Air Force Research Laboratory, Information Directorate, Rome, NY. From August 2006 to May 2011, he was an Assistant Professor in the Department of Electrical and Computer Engineering at the Polytechnic Institute of New York University, Brooklyn, NY. From May 2004 to August 2005 he was with the MITRE Corporation, McLean, VA, involved in the design and simulation of nanoscale circuits and systems. His research interests include low-power circuits, system-on-chip design, trusted hardware, and developing VLSI design methodologies for novel nanoelectronic technologies. 


date: 2022-02-24
speaker: Kevin Bock, Ph.D. Candidate, University of Maryland
title: Automating the Protection of Internet Freedoms
location: Zoom
start_time: 2:00 pm
end_time: 3:00 pm
abstract:
Users on the Internet face threats from powerful, often politically motivated entities: nation-states deploy increasingly complex network infrastructure to regulate what content citizens can access and can launch powerful attacks. I will present my agenda towards automating the discovery of and protection against threats to internet freedoms, in two broad contexts: network censorship and denial of service attacks. First, I will present Geneva, a genetic algorithm I developed that trains directly against nation-state censors and automatically discovers new ways to evade nation-state censors. I will discuss the novel evasion strategies I found using Geneva, including the first not to require any extra client software whatsoever. Second, I will describe how I used automated methods to discover new denial of service amplification attacks, the nature of which were long thought to be impossible. Collectively, my work shows the power of combining automated techniques with large-scale network measurements. I will close with a discussion of my future agenda towards protecting internet freedoms in the next generation of the censorship arms race.

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Meeting ID: 995 4321 3775
Passcode: 860713

bio:
Kevin Bock is a PhD candidate at the University of Maryland, advised by Dave Levin. His work focuses on enabling open communication, improving network security, and evading censorship. Kevin is passionate about scaling up undergraduate research, and has advised 26 undergraduate students to date on his anti-censorship team. Kevin is also adjunct faculty for the University of Maryland, where he created and teaches an upper-level undergraduate security course on Penetration Testing every Spring. Kevin's website is available at https://ter.ps/kevinbock.


date: 2022-03-03
speaker: Wenbo Guo, Penn State
title: Strengthening and Enriching Machine Learning for Cybersecurity
location: Zoom
start_time: 2:00 pm
end_time: 3:00 pm
abstract:
Nowadays, security researchers are increasingly using AI to automate and facilitate security analysis. Although making some meaningful progress, AI has not maximized its capability in security yet, mainly due to two challenges. First, existing ML techniques have not reached security professionals' requirements in critical properties, such as interpretability and adversary-resistancy. Second, Security data imposes many new technical challenges, and these challenges break the assumptions of existing ML models and thus jeopardize their efficacy. 

 In this talk, I will describe my research efforts to address the above challenges, with a primary focus on strengthening the interpretability of ML-based security systems and enriching ML to handle low-quality labels in security data. I will describe our technique to robustify existing explanation methods against attacks and a novel explanation method for deep learning-based security systems. I will also demonstrate how security analysts could benefit from explanations to discover new knowledge and patch ML model vulnerabilities. Then, I will introduce a novel ML system to enable high accurate categorizations of low-quality attack data and demonstrate its utility in a real-world industrial-level application. Finally, I will conclude by highlighting my plan towards maximizing the capability of advanced ML in cybersecurity.

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Meeting ID: 950 5107 3318

Passcode: 196639

bio:
Wenbo Guo is a Ph.D. Candidate at Penn State and a visiting student at Northwestern. His research interests are machine learning and cybersecurity. His work includes strengthening the fundamental properties of machine learning models and designing customized machine learning models to handle security-unique challenges. He is a recipient of the IBM Ph.D. Fellowship (2020-2022), Facebook/Baidu Ph.D. Fellowship Finalist (2020), and ACM CCS Outstanding Paper Award (2018). His research has been featured by multiple mainstream media and has appeared in a diverse set of top-tier venues in security and machine learning. Going beyond academic research, he also actively participates in many world-class cybersecurity competitions and has won the 2018  DEFCON/GeekPwn AI challenge finalist award. 


date: 2022-03-07
speaker: Yueqi Chen, Penn State University
title: Securing Operating System Kernels with Fewer Shots
location: Zoom
start_time: 11:00 am
end_time: 12:00 pm
abstract:
Despite significant efforts on cybersecurity, we are observing an increasing number of attacks in recent years. The reason for this harsh reality is all our efforts aim at individual incidents and there is no deep understanding of attack surfaces in software systems. As a result, software systems are integrated with too many individual patches and ad-hoc mitigations, which slows down systems significantly without introducing substantial security benefits. 
In this talk, I will present a systematic approach to understanding attack surfaces. This approach provides security analysts and developers with the ability to quantify the impact of attack surfaces and facilitate the development of universal and effective defense solutions. Technically, this approach consists of two steps - induction and deduction. The induction step abstracts the essential attack surface behind the incident and the deduction step generalizes it to the exploitation of different error types in different systems. In this talk, I will illustrate this research approach starting from a security incident in the Linux kernel. I will then present a universal and effective defense that mitigates the generalized attack surface and is widely adopted in various commodity Operating System kernels. 

In the future, I plan to further advance this systematic approach and make it a fundamental part of the entire software development lifecycles. More specifically, I will: 1) enrich induction and deduction techniques for more attack forms under new contexts, 2) formalize the description of attack surfaces, and 3) quantify the security of systems to optimize and re-construct existing defenses architecture.

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Meeting ID: 965 7586 2853
Passcode: 323119

bio:
Yueqi Chen received his B.Sc degree from Nanjing University. He is currently a fifth-year Ph.D. student at Pennsylvania State University and a predoctoral fellow with Dr. Xinyu Xing at Northwestern University. He was awarded the IBM Ph.D. Fellowship 2020-2022. In general, his research focuses on system security and software security. He is particularly interested in developing systematic approaches to inducing, deducting, and mitigating attack surfaces. Along this thread, he has published 11 papers in top-tier academic conferences, including IEEE S&P, USENIX Security, ACM CCS, NDSS, OOPSLA, ACM/IEEE ICSE, IEEE/ACM ASE as leading authors and co-authors over the past 4 years. In addition, he presented his works at CLK 2019, BlackHat Europe 2019, BlueHat IL 2020, LSS Europe 2020, BlackHat Aisa 2021, LSS North America 2021, BlackHat Europe 2021. His research works were covered by high-profile media (e.g., Dark Reading) and have received wide recognition from the industry, including Amazon, Apple, Baidu, Google, Grsecurity, IBM, JD.com, Linux Foundation, Microsoft, and Red Hat. His work is integrated into the internal threat alerting platform of JD.com. The new defenses in AutoSlab produced by Grsecurity and iPhone 13 series from Apple can find their prototypes in his works.  As a team member of r3kapig and Nu1L, he participated in DEF CON 26 CTF Final and DECONF 29 CTF Final, and ranked 16th and 7th, respectively. He ranked 5th in NSA codebreaker 2017.


date: 2022-03-08
speaker: Chenguang Wang, University of California, Berkeley
title: Towards Trustworthy Knowledge Sharing of Language Models
location: Zoom
start_time: 2:00 pm
end_time: 3:00 pm
abstract:
Pretrained language models such as BERT and GPT-3 have revolutionized text understanding over the last few years. These models share knowledge present in the training data via parameter sharing with downstream tasks (e.g., question answering and code completion). However, it is difficult to deploy these models in real-world applications, since the current knowledge sharing mechanism is not trustworthy for the following reasons. First, this mechanism shares latent model knowledge that is not explainable. Second, it is not robust to distribution shifts arising in real-world tasks. Third, this also raises concerns for broader societal impacts, such as bias. In this talk, I will describe my research in trustworthy knowledge sharing of pretrained language models that solve the pressing problems. My talk will start by presenting my work in interpreting latent knowledge in pretrained language models as human-readable knowledge. I will then introduce benchmarks and algorithms that enhance the robustness of knowledge sharing from those models. The talk will also discuss the applications of our trustworthy text understanding techniques to real-world scenarios. Finally, I will conclude with a vision of future directions for trustworthy knowledge sharing.

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Meeting ID: 913 9355 6125
Passcode: 673397

bio:
Chenguang Wang is a postdoc in Computer Science at UC Berkeley. Before that, he was a Research Scientist at Amazon AI and a Research Staff Member at IBM Research-Almaden. He received his Ph.D. degree from Peking University in 2016. He was also a visiting Ph.D. student at UIUC. His research interests span the areas of data science, natural language processing, security, systems, and machine learning. His recent work is focused on trustworthy text understanding. He has created several impactful open-source systems, including GluonNLP (one of the most popular deep learning for NLP systems with 220,000 downloads) and AutoGluon (4,176 GitHub stars). He is the recipient of several academic awards such as ACM China Doctoral Dissertation Award Honorable Mention (one of the two national winners). His research has resulted in real-world impact and has been used by Amazon, Microsoft, UPMC Hillman Cancer Center.


date: 2022-03-11
speaker: Xuhao Chen, Massachusetts Institute of Technology
title: Specializing Computing Systems for Graph Algorithms
location: Zoom
start_time: 11:00 am
end_time: 12:00 pm
abstract:
Numerous AI applications in social networks, e-commerce, biomedicine and security, are driven by graph algorithms.  The graph data is massive and sparse, which poses great challenges in computing system design. 

In this talk, I will explore system design tradeoffs for an important class of graph algorithms — graph mining. I will describe experiences creating abstractions, optimization techniques and automation methodologies for graph mining, across different layers of the system stack, including both software and hardware.  As I will demonstrate, cross-layer system design can fully unlock the potential of graph computing, and should be used for enabling pervasive AI.

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Meeting ID: 951 9462 7649
Passcode: 077584

bio:
Xuhao Chen is a Research Scientist at MIT CSAIL, working with Prof. Arvind.  Dr. Chen is broadly interested in parallel systems and computer architectures for AI and big-data, with a focus on graph algorithms.  He has built multiple software and hardware systems for data mining and machine learning on graphs.  His work has been published in ISCA, MICRO, VLDB, ICS and DAC.  Before joining MIT, Dr. Chen was a Research Fellow at UT Austin.


date: 2023-01-20
speaker: Chong Yu, University of Nebraska-Lincoln
title: Cyber security over AI-empowered Internet-of-Things
location: Engineering Building, Room 2901. Zoom link: https://gmu.zoom.us/j/92947203785
start_time: 11:00 am
end_time: 12:00 pm
abstract:
Cyber security is one of the most challenging missions in the era of ubiquitous networks. With the rapid development of the Internet-of-Things (IoT) and various applications of artificial intelligence (AI) in the cyber world, the research in cyber security is expanding beyond the traditional topics focusing on information confidentiality, authentication, and integrity. Many novel vulnerabilities are threatening AI-empowered IoT. 

My research about cyber security over AI-empowered IoT mainly focuses on two aspects: 1) studying advanced threats that undermine the efficiency of AI in IoT, and 2) proposing solutions to resist these attacks. In this talk, I will introduce several attacks in AI-empowered IoT, including poisoning attacks, adversarial attacks, and credit falsification attacks. Meanwhile, I will discuss how these attacks weaken the system and the challenges to overcome them. After that, I will detailly present one of my works that proposes a secure and efficient federated learning approach from a device selection perspective against the model poisoning attack. Specifically, this work first formulates an optimization problem to minimize training costs while maximizing accuracy. Then, two model poisoning attacks are designed to degrade the performance of training. Finally, two device selection methods are proposed to eliminate the effect of the designed model poisoning attacks. In addition, the main ideas and results of the solutions to the adversarial attack and credit falsification attack also will be briefly presented.

bio:
Chong Yu is working toward the Ph.D. degree at the Department of Electrical and Computer Engineering, University of Nebraska-Lincoln. She received her B.Sc. degree in communication engineering, M.Sc. degree in communication and information system, and Ph.D. degree in computer system organization from Northeastern University, Shenyang, China. She has 14 publications at prestigious journals and highly selective conferences. Her research interests include cybersecurity, secure and privacy-preserving distributed artificial intelligence (AI), and Internet of Things (IoT).


date: 2023-01-26
speaker: Anomadarshi Barua, University of California, Irvine
title: Towards a Hardware/Software Co-Design Approach for CPS and IoT Security
location: https://gmu.zoom.us/j/99149174714?pwd=M2RwZEZITmlWOXdpc0JRSmxRZHVjZz09
start_time: 11:00 am
end_time: 12:00 pm
abstract:
Have you ever wondered how simple music can leak deadly pathogens from a bio-safety lab? Have you ever considered how an industrial robotic arm can be compromised from a cloud infrastructure? Have you ever imagined an attacker could use a magnetic field to shut down a weak microgrid?  If the above questions excite you, please attend my research seminar, where my research answers the above questions with a practical demonstration of CPS attacks on industry-used infrastructure. With this seminar, the audience will gain a deep insight into how hardware/software interaction in CPSs can open a “Pandora’s Box” of unknown threats that can come from very unconventional ways. 
 
My research sits at the intersection of hardware and software layers in CPSs and IoTs, exploring how interactions of cyber and physical components can affect the safety and controllability of closed-loop control from sensing to actuation. Most attacks on CPSs and IoTs can be propagated from the physical domain to the cyber domain or vice-versa and hence, can be termed as cross-domain attacks. To understand these cross-domain attacks, a very different set of methodologies and tools is needed. My research talks about how these tools can be made. Moreover, the seminar will show how cross-domain vulnerabilities can be handled using new hardware/software co-design approaches to detect, contain and isolate threats in CPSs and IoTs. 

bio:
Anomadarshi Barua is a Ph.D. candidate studying Computer Engineering at the University of California Irvine. He received his M.S. degree in Embedded Computing Systems jointly at the University of Southampton (UoS) and the Norwegian University of Science and Technology (NTNU) in 2016. Now, He is working under the supervision of Professor Al Faruque in the Autonomous and Intelligent Cyber-Physical Systems (AICPS) Lab. His current research focuses on the system-level design of embedded and Cyber-Physical-Systems (CPSs) with a special interest in CPS security, low-power design, and computer microarchitecture. He publishes in the security conferences like ACM CCS, USENIX, ACSAC, RAID, and CHES and system conferences, like ICCPS and ICCAD. He also has worked in several industries in North America and Europe, such as Intel Corporation (USA), Solidigm (USA), Nordic Semiconductor (Norway), and IDEAS (Norway). In his leisure time, he likes to travel to new places and go for hiking. 


date: 2023-02-03
speaker: Ms. Xinda Wang
title: AI-Enhanced Software Vulnerability and Security Patch Analysis
location: CSIS Conference Room - Research 420
start_time: 2:00 pm
end_time: 3:00 pm
abstract:
With the increasing popularity of open-source software (OSS), their embedded vulnerabilities have been widely propagating to downstream software. Although timely applying security patches is the best practice to prevent vulnerabilities, OSS users are hard to distinguish and prioritize security patches over tons of non-security patches (i.e., bug fixes, feature updates, etc.) Even worse, software vendors may silently release security patches without providing any explicit advisories. While users are unaware of security patches, attackers can still carefully inspect the patch code changes to exploit unpatched software. Therefore, automatically detecting security patches becomes imperative for software maintenance.
 
In this talk, I will describe my research efforts to address the above problems. First, I will introduce an empirical study that reveals the insecure behavior of software vendors during maintenance and discloses the existence of silent (hidden) security patches. Second, I will present PatchDB, the first large-scale real-world patch dataset, that enables the training of data-hungry AI models for patch detection and facilitates future vulnerability/patch analysis research. An unsupervised method is developed to efficiently collect security patch samples from a huge number of unlabeled Git commits. Third, I will present GraphSPD, a novel graph learning-based approach for automated security patch detection. By combining rich semantic properties of both pre-patch code and post-patch code in a joint graph structure and adopting a tailored multi-attributed graph convolution network to adapt diverse attributes in a patch graph representation, GraphSPD demonstrates state-of-the-art performance and detects 88 new silent security patches in popular real-world Git projects. Finally, I will conclude by highlighting my research plan towards maximizing AI capabilities in automating the process of software vulnerability and patch management.

If you cannot join in person, you are welcome to join via Zoom meeting:
 
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Meeting ID: 912 8623 9802
Passcode: 260455
 
 

bio:
Xinda (Cindy) Wang is a Ph.D. candidate in the Center for Secure Information Systems at George Mason University. She received her B.E. degree in Computer Science from the Harbin Institute of Technology in 2017. Her research interests are AI-enhanced cybersecurity, including software security, container security, and network anomaly detection. As the leading author, she has published papers in flagship security venues such as IEEE S&P, DSN, and CNS. She is the recipient of the IEEE CNS Best Paper Nomination (2020), Tapia Scholarship (2018, 2022), GHC Scholarship(2022), and IMC Suneeth Nayak Scholarship (2020).


date: 2023-03-22
speaker: Habiba Farukh, Purdue University
title: Leveraging Multimodal Sensing for Enhancing the Security and Privacy of Mobile Systems
location: ENGR 4201 + ZOOM
start_time: 11:00 am
end_time: 12:00 pm
abstract:
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Meeting ID: 938 5208 4679
Passcode: 727602

Mobile systems, such as smartphones, wearables (e.g., smart watches, AR/VR headsets), and IoT devices, have evolved significantly from being just a method of communication to sophisticated sensing devices that monitor and control several aspects of our lives. While these devices' advanced computing and sensing capabilities enable several useful applications, they also make them attractive targets for attackers, leading to several security threats and loss of privacy. In this talk, I explore how we can leverage multimodal sensing to design secure and usable user and device authentication systems for mobile and IoT devices through a combination of computer vision, machine learning, and cryptographic methods. In particular, I will present two systems for mobile security and privacy: a liveness detection system for protecting face authentication systems on mobile devices, and a group pairing system for IoT devices with different sensing modalities. Through this research, we develop tools and algorithms that allow developers to implement effective and user-friendly systems for user and device authentication.

bio:
Habiba Farrukh is a Ph.D. candidate in the Computer Science department at Purdue University, where she is advised by Professor Z. Berkay Celik. Habiba has conducted research on a variety of topics, including mobile and IoT security and privacy and human-centered computing. Her dissertation focuses on leveraging multimodal sensing on mobile and IoT devices to provide rigorous security and privacy guarantees for these systems. She received the Bilsland Dissertation Fellowship for her dissertation in 2021. She has led a team of 5 students to improve the usability and conformity of Android authorization APIs and WearOS permission model for the Google ASPIRE (Android Security and PrIvacy REsearch) projects in 2021 and 2022. She is expected to earn her Ph.D. in the Spring of 2023. Habiba also interned with the Machine Learning Science team at Amazon Robotics. More information available at https://habiba-farrukh.github.io/.


date: 2023-03-28
speaker: Zhengdao Wang, Iowa State University
title: Cybersecurity Research in the Era of Quantum, IoT, AI, and 6G
location: ENGR 3507
start_time: 11:00 am
end_time: 12:00 pm
abstract:
The rapid evolution of technology has led to an unprecedented expansion of our digital world, with advancements in quantum computing, IoT, AI, and the forthcoming 6G network. However, as technology continues to progress, so too do the threats posed to our cybersecurity. This talk with explore the research trend in cybersecurity research in the context of these emerging technologies. By examining the unique challenges and opportunities presented by these technologies, we can better understand the evolving threat landscape and work towards developing effective solutions to protect our digital world.

bio:
Zhengdao Wang received his Bachelor's degree in Electronic Engineering and Information Science from the University of Science and Technology of China (USTC) in 1996, the M.S. degree in Electrical and Computer Engineering from the University of Virginia in 1999, and Ph.D. degree in Electrical and Computer Engineering from the University of Minnesota in 2002. From 2002, he has been on the faculty of the Department of Electrical and Computer Engineering in the Iowa State University, where he is currently a professor. He held a visiting professorship from USTC from 2012 to 2015. He served as a Program Director at National Science Foundation in the Division of Electrical, Communications, and Cyber Systems from 2020 to 2022.

He received the Procter & Gamble Fellowship from the USTC in 1992, an Excellent Youth Award from the USTC in 1995, an Outstanding Graduate Award from the USTC in 1996, an Outstanding Graduate Award from Anhui Province of China in 1996, a Graduate Fellowship from the University of Virginia in 1997, and a Graduate School Doctoral Dissertation Fellowship from the University of Minnesota in 2001. At Iowa State University, he received an Early Career Engineering Faculty Research Award and an ECE Warren B. Boast Undergraduate Teaching Award both in 2010. He received the IEEE Signal Processing Society Best Magazine Paper Award in 2003, the IEEE Marconi Paper Prize Award in Wireless Communications in 2004, and the EURASIP Journal on Advances in Signal Processing Best Paper Award in 2008. He is an IEEE Fellow of class 2016.

He served as an Associate Editor for IEEE Transactions on Vehicular Technology, from 2004 to 2006, an Associate Editor for IEEE Signal Processing Letters from 2005 to 2008, an Associate Editor for IEEE Transactions on Signal Processing from 2013 to 2015. He served as an Associate Editor for IEEE Transactions on Wireless Communications and an Editor for The IEEE Signal Processing Society Online Video Library. He was a Technical Program co-chair for GLOBECOM 2008 Wireless Communications Symposium, a Technical Program co-chair for WUWNet 2014, a Technical Area co-Chair on Wireless Communications for Asilomar Conference 2014, and the Publications and Publicity chair for IEEE GlobalSIP 2014. He was a member and the webmaster for the IEEE Signal Processing for Communication and Networking (SPCOM) Technical Committee from 2007 to 2012. He served as the Chair of the IEEE Joint Chapter on Communications, Signal Processing, Circuit and System; Central Iowa Section, IEEE Region 4 (Northern Midwest USA) from 2004 to 2006, and is serving again as the Chair from 2016 to 2018. He has served as a Technical Committee member for many conferences throughout the years. He served as the Regional Director at Large of the IEEE Signal Processing Society for Regions 1-6 (US) in 2017 and 2018. He was a General co-chair of IEEE GLOBECOM 2018 Workshop on Machine Learning for Communications. He was as a TPC co-chair for IEEE SPAWC 2020.

His interests are in the areas of machine learning, cybersecurity, communications, signal processing, and information theory.



date: 2023-03-24
speaker: Dr. Sadegh Torabi, Assistant Professor, George Mason University
title: Internet-Scale Discovery and Security Analysis of EV Charging Management Systems
location: CSIS Conference Room, Research Hall, Room 420
start_time: 2:00 pm
end_time: 3:00 pm
abstract:
Electric Vehicle Charging Management Systems (EVCMS) are a collection of specialized software that allow users to remotely operate Electric Vehicle Charging Stations (EVCS). With the increasing number of deployed EVCS to support the growing global EV fleet, the number of EVCMS are consequently growing, which introduces a new attack surface. In this paper, we propose a multi-stage framework to discover Internet-connected EVCMS and investigate their security posture. Our approach leverages identifiers extracted from a small seed of EVCMS to extend the capabilities of device search engines through iterative fingerprinting using a combination of classification and clustering approaches. Indeed, by leveraging a small seed of 1,800 discovered hosts that deployed 9 distinct EVCMS, we leveraged our approach to identify 27,439 online EVCS instrumented by 44 unique EVCMS. Consequently, our in-depth security analysis highlighted the insecurity of the majority of the deployed EVCMS while uncovering 120 0-day vulnerabilities. Our findings shed light on the insecurity of the EVCSMS at scale while highlighting the feasibility of cyber attacks against the EV charging ecosystem, its users, and the connected power grid. Finally, while we recommend countermeasures to mitigate future threats, this work contributes to the security of the EVCS ecosystem via conducting a Coordinated Vulnerability Disclosure (CVD) effort with system developers/vendors who acknowledged and assigned the discovered vulnerabilities more than 20 CVE-IDs.

If you cannot join in person, you are welcome to join via Zoom meeting:
https://gmu.zoom.us/j/96351546909?pwd=WFVpNVF0eGRuSmtmWWEzV0tZalQ2QT09
 
Meeting ID: 963 5154 6909
Passcode: 788844
 

bio:
Sadegh Torabi is an assistant professor and a research fellow at Department of Information Science and Technology (IST), School of Computing, at George Mason University. Prior to joining the department of IST, Sadegh was a postdoctoral research fellow at the Center for Secure Information Systems (CSIS) at George Mason University. Sadegh’s research interests are in the areas of Internet measurements, network/systems security, IoT malware detection and characterization, and operational cyber security including the security of Internet of Things (IoT) and Cyber-Physical Systems (CPS). Sadegh received his Ph.D. degree in Information Systems Engineering (with Outstanding Ranking) from Concordia University, Montreal, Canada. During his Ph.D., Sadegh was a member of the Security Research Centre (SRC) at Concordia University, where he received several awards and scholarships such as the Abdul-Aziz Hariri Graduate Scholarship in Cyber Security and the Carolyn and Brian Neysmith Graduate Scholarship. Sadegh also received his M.Sc. from the Electrical and Computer Engineering Department at University of British Columbia (UBC), Vancouver, Canada. 


date: 2023-03-29
speaker: Mehdi Mirakhorli, Rochester Institute of Technology
title: Secure by Design, What does it mean? What does it take?
location: ENGR 4801
start_time: 3:30 pm
end_time: 4:30 pm
abstract:
Secure by design is an approach to developing secure software systems from the ground up.  In such an approach,  the alternate security controls and design decisions are first thought; among them, the best are selected and enforced by the architecture design, and then used as guiding principles for developers.  Thus, design flaws in the architecture of a software system mean that successful attacks could result in enormous consequences. Therefore, secure-by-design shifts the main focus of software assurance from finding security bugs to identifying architectural flaws in the design. Current research in software security has been neglecting vulnerabilities that are caused by flaws in a software architecture design and/or deteriorations of the implementation of architectural decisions. In this talk, I will discuss the role and impact of software architecture and architecture awareness in software assurance activities.  I will present the concept of Common Architectural Weakness Enumeration (CAWE), the results of empirical research on design flaws in real systems, as well as techniques to reason about security architecture and detect security architectural weaknesses.

bio:
Mehdi Mirakhorli is the Director of Research at ESL Global Cybersecurity Institute (GCI) and an associate professor of software engineering and Kodak Endowed Chair at Rochester Institute of Technology. His research interests are on the intersection of Software Engineering and Cybersecurity. He has carried out various projects of national importance in the areas of trustworthy software, software assurance, cybersecurity, resiliency, scientific software development, and software-enabled sustainable disposal. Dr. Mirakhorli has served as PI on R&D awards and contracts of over $20.8 Million. He serves as Associate Editor for IEEE Transactions in Software Engineering (TSE) and Empirical Software Engineering Journal. He is a recipient of the US National Science Foundation Faculty Early Career Development Award and multiple Distinguished/Best Paper Awards.


date: 2023-04-12
speaker: Xinda Wang, George Mason University
title: AI-Enhanced Software Vulnerability and Security Patch Analysis
location: ENGR 2302
start_time: 10:30 am
end_time: 12:30 pm
abstract:
With the increasing popularity of open-source software (OSS), their embedded vulnerabilities have been widely propagating to downstream software. Although timely applying security patches is the best practice to fix vulnerabilities, OSS users are hard to distinguish and prioritize security patches over tons of non-security patches (i.e., bug fixes, feature updates, etc.). Even worse, software vendors may silently release security patches without providing any explicit advisories. While users are unaware of security patches, attackers can still carefully inspect the patch code changes to exploit unpatched software. Therefore, automatically detecting security patches becomes imperative for software maintenance.
 
In this defense, I will describe my research efforts to address the above problems. First, I will introduce an empirical study that reveals the insecure behavior of software vendors during maintenance and discloses the existence of silent (hidden) security patches. Second, I will present PatchDB, the first large-scale real-world patch dataset, that enables the training of data-hungry AI models for patch detection and facilitates future vulnerability/patch analysis research. An unsupervised method is developed to efficiently collect security patch samples from a huge number of unlabeled GitHub commits. Third, I will present GraphSPD, a novel graph learning-based approach for automated security patch detection. By combining rich semantic properties of both pre-patch code and post-patch code in a joint graph structure and adopting a tailored multi-attributed graph convolution network to adapt diverse attributes in a patch graph representation, GraphSPD demonstrates state-of-the-art performance and detects 88 new silent security patches in popular real-world GitHub projects. Finally, I will conclude by highlighting my research plan towards maximizing AI capabilities in automating the process of software vulnerability and patch management.

bio:
Xinda Wang received her Bachelor of Engineering from Harbin Institute of Technology in 2017 and Master of Science from George Mason University in 2022.  


date: 2023-03-31
speaker: Mohammad Amiri, University of Pennsylvania
title: Large-Scale Collaborative Data Management in Untrusted Environments
location: JC GOLD ROOM
start_time: 11:00 am
end_time: 12:00 pm
abstract:
Today's large-scale data management systems need to address distributed applications' confidentiality and scalability requirements among a set of mutually distrustful collaborative enterprises. On the one hand, emerging distributed applications, e.g., supply chain management and multi-platform crowdworking, require collaboration between distributed enterprises to process a mix of public and private transactions. On the other hand, distributed applications need to scalably process a large number of transactions within or across enterprises. My research addresses these requirements by bridging large-scale data management and distributed fault-tolerant systems. In this talk, I first discuss Qanaat to address confidentiality and then present SharPer, a flattened sharding protocol to manage large-scale data. The talk concludes with several future directions on leveraging privacy-preserving, resource disaggregation and reinforcement learning techniques to address large-scale data management systems' verifiability and adaptivity requirements.

bio:
Mohammad Javad Amiri is a postdoctoral researcher in the Computer and Information Science department at the University of Pennsylvania, where he is working with Prof. Boon Thau Loo. At Penn, he is a member of the NetDB research group, distributed systems lab, and database group. Before joining Penn, he received his Ph.D. in Computer Science at the University of California, Santa Barbara, under the supervision of Prof. Divyakant Agrawal and Prof. Amr El Abbadi. His research mainly lies at the intersection of large-scale data management and distributed fault-tolerant systems, focusing on distributed transaction processing, consensus protocols, and blockchains. His work has appeared at premier conferences such as VLDB, SIGMOD, WWW, FSE, and ICDE.


date: 2023-04-17
speaker: Kun Sun, George Mason University
title: Cache-based Attacks against ARM TrustZone
location: ENGR 4201
start_time: 11:00 am
end_time: 12:00 pm
abstract:
ARM processors provide a hardware security extension called TrustZone to protect security sensitive code and data running in a trusted execution environment. Our research points out that it is critical to protect the CPU caches when developing TrustZone-based security systems. First, we observe an ARM TrustZone cache incoherence behavior, which results in the cache contents of the two worlds, secure world and normal world, potentially being different even when they are mapped to the same physical address. Based on this observation, we develop a new cache-based rootkit called CacheKit that hides in the cache of the normal world and is able to evade memory introspection from the secure world. Second, researchers propose to create Isolated Execution Environments (IEEs) in the normal world to protect the security sensitive applications. However, we discover three cache-based attacks called CITM that can be leveraged to manipulate the sensitive data protected in IEE systems. Specifically, due to the inefficient and incoherent security measures on the cache that maps to the IEE memory, attackers in the normal world may compromise the security of IEE data by manipulating the IEE memory during concurrent execution, bypassing the security measures enforced when a security sensitive application is suspended or finished, or misusing the incomplete security measures during IEE’s context switching processes.

bio:
Dr. Kun Sun is a professor in the Department of Information Sciences and Technology at George Mason University. He is also the director of Sun Security Laboratory and the associate director of the Center for Secure Information Systems. He received his Ph.D. in Computer Science from North Carolina State University. Before joining GMU, he was an assistant professor in College of William and Mary. He has more than 15 years working experience in both academia and industry; his research work has been funded by government agencies including the NSF, DOD, NSA, DHS, and NIST. His research focuses on systems and network security. He has publishing over 130 conference and journal papers, and two papers won the Best Paper Award. His current research focuses on trustworthy computing environment, software security, moving target defense, network security, smart phone security, cloud security, and AI/ML security.


date: 2023-04-19
speaker: Albert Cheu, Georgetown University
title: Differential Privacy without Centralization
location: ENGR 4201
start_time: 11:00 am
end_time: 12:00 pm
abstract:
Fields like machine learning and statistics have flourished with the accessibility of personal data. To address concerns about such analysis, computer scientists have identified special families of data analysis algorithms. One of these is the family of differentially private (DP) algorithms, which allow individuals to quantify the risk of contributing their data. A textbook instantiation of DP is centralized: it requires data owners to trust that a data collector runs the correct algorithm and does nothing else with data.

In this talk, I advocate for decentralized DP, where no single collector no longer has direct access to data and computations are done by multiple parties. My work proves that access to a shuffler---an entity that anonymizes communication---allows us to replicate some of the success in the centralized setting. I look forward to continuing my theoretical work, while also pursuing real-world implementations and exploring other benefits of decentralization.

bio:
Albert Cheu is a computer scientist working on differential privacy, with a particular interest in its intersections with cryptography and security. He is a postdoctoral fellow working in the Department of Computer Science at Georgetown University. He earned his PhD. at Northeastern University's Khoury College of Computer Science.


date: 2023-05-05
speaker: Dr. Marcos Zampieri, Assistant Professor, George Mason University
title: Offensive Language Identification in Social Media
location: CSIS Conference Room, Research 420
start_time: 2:00 pm
end_time: 3:00 pm
abstract:
Offensive language is pervasive in social media. Individuals frequently take advantage of the perceived anonymity of computer-mediated communication, using this to engage in behavior that many of them would not consider in real life. One of the most effective strategies for tackling this problem is to use computational methods to identify offense, aggression, and hate speech in user-generated content (e.g., posts, comments, microblogs, etc.). In this talk, I discuss some of the challenges of using NLP to recognize offensive content online. I present the OLID and TBO taxonomies created to annotate offensive language datasets. The challenges of collecting and annotating multilingual datasets for offensive language identification are also discussed. Finally, I present the set-up and the results of the two editions of the OffensEval competition hosted at SemEval-2019 and SemEval-2020 (https://nam11.safelinks.protection.outlook.com/?url=https%3A%2F%2Fsites.google.com%2Fsite%2Foffensevalsharedtask%2Fhome&data=05%7C01%7Cksun3%40gmu.edu%7Cb6eeba0c5fe346c5dfa708db4b636096%7C9e857255df574c47a0c00546460380cb%7C0%7C0%7C638186662410849858%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=9FkFUcyoL%2FbiJCstA1kzlr2YcX%2BAq7V3L519PchgRO8%3D&reserved=0).


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bio:
Marcos Zampieri is a tenure-track assistant professor at the School of Computing at George Mason University. He obtained his PhD degree from Saarland University in Germany with a thesis on computational approaches to language variation. Marcos published papers on many topics in Computational Linguistics and Natural Language Processing such as language acquisition and variation, offensive language identification, and machine translation. He has been one of the lead organizers of the workshop series on NLP for Similar Languages, Varieties and Dialects (VarDial) and a lead organizer of OffensEval at SemEval. He is one of the editors of Similar Languages, Varieties, and Dialects: A Computational Perspective published at the series Studies in Natural Language Processing by Cambridge University Press.


date: 2023-09-01
speaker: David Donoho, Stanford University
title: Data Science at the Singularity
location: Johnson Center, Meeting Room A
start_time: 11:00 am
end_time: 12:00 pm
abstract:
A purported “AI Singularity” has been much in the public eye recently, especially since the release of ChatGPT last November, spawning social media “AI Breakthrough” threads promoting Large Language Model (LLM) achievements.  Alongside this, mass media and national political attention focused on “AI Doom” hawked by social media influencers, with twitter personalities invited to tell congresspersons about the coming "End Times."   
 
In my opinion, “AI Singularity” is the wrong narrative; it drains time and energy with pointless speculation. We do not yet have general intelligence, we have not yet crossed the AI singularity, and the remarkable public reactions signal something else entirely. 
 
Something fundamental to science really has changed in the last ten years. In certain fields which practice Data Science according to three principles I will describe, progress is simply dramatically more rapid than in those fields that don’t yet make use of it.
 
Researchers in the adhering fields are living through a period of very profound transformation, as they make a transition to frictionless reproducibility. This transition markedly changes the rate of spread of ideas and practices, and marks a kind of singularity, because it affects mindsets and paradigms and erases memories of much that came before.  Many phenomena driven by this transition are misidentified as signs of an AI singularity. Data Scientists should understand what's really happening and their driving role in these developments.

bio:
David Donoho is one of the most accomplished statisticians in our lifetime. He is a Professor of Statistics, and serves as the Anne T. and Robert M. Bass Professor in the Humanities and Sciences at Stanford University. He is a COPSS Presidents' Awardee, an elected Fellow of the American Academy of Arts and Sciences, and an elected Member of the United States National Academy of Sciences. In 2019, he was also elected a Member of the American Philosophical Society. Among his many accomplishments, he also received the John von Neumann Prize (2001, Society for Industrial and Applied Mathematics), the Norbert Wiener Prize in Applied Mathematics (2010, from SIAM and the AMS), the Shaw Prize for Mathematics (2013), the Gauss Prize from IMU (2018), and, most recently, the IEEE Jack S. Kilby Signal Processing Medal in 2022. His research interests include large-scale covariance matrix estimation, large-scale matrix denoising, detection of rare signals, compressed sensing, and empirical deep learning. 


date: 2023-09-06
speaker: Arun Ross, Michigan State University
title: Biometrics in a Deep Learning World
location: Engineering Building, Room 4801
start_time: 2:00 pm
end_time: 3:00 pm
abstract:
Biometrics is the science of recognizing individuals based on their physical and behavioral attributes such as fingerprints, face, iris, voice and gait. The past decade has witnessed tremendous progress in this field, including the deployment of biometric solutions in diverse applications such as border security, national ID cards, amusement parks, access control, and smartphones. Further, the paradigm of deep learning using deep neural networks is rapidly changing the landscape of biometrics. Despite these advancements, biometric systems have to contend with a number of challenges related to data integrity, spoof attacks, and personal privacy. This talk will highlight some of the recent progress made in the field of biometrics; present our lab’s work on topics such as presentation attack detection, privacy-enhancing techniques, and synthetic biometrics; and discuss some of the challenges that have to be solved in order to deepen society’s trust in biometric technology. 

bio:
Arun Ross is the Martin J. Vanderploeg Endowed Professor in the Department of Computer Science and Engineering at Michigan State University, and the Site Director of NSF’s Center for Identification Technology Research (CITeR). He conducts research on the topic of biometrics, privacy, computer vision and deep learning. He is a recipient of the JK Aggarwal Prize (2014) and the Young Biometrics Investigator Award (2013) from the International Association of Pattern Recognition for his contributions to the field of Pattern Recognition and Biometrics. He was designated a Kavli Fellow by the US National Academy of Sciences by virtue of his presentation at the 2006 Kavli Frontiers of Science Symposia. Ross is a recipient of the NSF CAREER Award.
 
Ross has advocated for the responsible use of biometrics in multiple forums including the NATO Advanced Research Workshop on Identity and Security in Switzerland in 2018. He testified as an expert panelist in an event organized by the United Nations Counter-Terrorism Committee at the UN Headquarters in 2013. In June 2022, he testified at the US House Science, Space, and Technology Committee on the topic of Biometrics and Personal Privacy. He is a co-author of the monograph “Handbook of Multibiometrics” and the textbook “Introduction to Biometrics”.

Web: https://iprobe.cse.msu.edu/


date: 2023-09-22
speaker: Srini Devadas, Massachusetts Institute of Technology
title: PAC Privacy: Automatic Privacy Measurement and Control of Data Processing
location: Nguyen Engineering Bldg., Conference Room 4201
start_time: 1:00 pm
end_time: 2:00 pm
abstract:
We propose and study a new privacy definition, termed Probably Approximately Correct (PAC) Privacy. PAC Privacy characterizes the information-theoretic hardness to recover sensitive data given arbitrary information disclosure/leakage during/after any processing. Unlike the classic cryptographic definition and Differential Privacy (DP), which consider the adversarial (input-independent) worst case, PAC Privacy is a simulatable metric that quantifies the instance-based impossibility of inference. A fully automatic analysis and proof generation framework is proposed: security parameters can be produced with arbitrarily high confidence via Monte-Carlo simulation for any black-box data processing oracle. On the utility side, the magnitude of (necessary) perturbation required in PAC Privacy is not lower bounded by Θ(√d) for a d-dimensional release but could be O(1) for many practical data processing tasks, which is in contrast to the input-independent worst-case information-theoretic lower bound.  We discuss applications of PAC Privacy to statistical data processing tasks. Joint work with Hanshen Xiao.

bio:
Srini Devadas is the Webster Professor of EECS at the Massachusetts Institute of Technology, where he has been on the faculty since 1988. Devadas's current research interests are in computer architecture, computer security, and applied cryptography.  In 2021, he received the IEEE Cybersecurity Award for Practice, and the ACM SIGSAC Award for Outstanding Innovation for his work on secure hardware. Devadas is a MacVicar Faculty Fellow and an Everett Moore Baker teaching award recipient, considered MIT's two highest undergraduate teaching honors.


date: 2023-10-23
speaker: Dan Roth, University of Pennsylvania
title: The Science of Generative AI: Focus on Reasoning
location: Nguyen Engineering Bldg., Conference Room 4201
start_time: 11:00 am
end_time: 12:00 pm
abstract:
The rapid progress made over the last few years in generating linguistically coherent natural language has blurred, in the mind of many, the difference between natural language generation, understanding, and the ability to reason with respect to world. Nevertheless, robust support of high-level decisions that depend on natural language understanding, and one that requires dealing with “truthfulness” are still beyond our capabilities, partly since most of these tasks are very sparse, often require grounding, and may depend on new types of supervision signals.  I will discuss some of the challenges underlying reasoning – and present some of our work in this space, focusing on supporting task decomposition and planning.   

bio:
Dan Roth is the Eduardo D. Glandt Distinguished Professor at the Department of Computer and Information Science, University of Pennsylvania, a VP/Distinguished Scientist at AWS AI Labs, and a Fellow of the AAAS, the ACM, AAAI, and the ACL.
 
In 2017 Roth was awarded the John McCarthy Award, the highest award the AI community gives to mid-career AI researchers. Roth was recognized “for major conceptual and theoretical advances in the modeling of natural language understanding, machine learning, and reasoning.” 

Roth has published broadly in machine learning, natural language processing, knowledge representation and reasoning, and learning theory. He was the Editor-in-Chief of the Journal of Artificial Intelligence Research (JAIR), has served as the Program Chair for AAAI, ACL and CoNLL, and as a Conference Chair for a few top conferences. Roth has been involved in several startups; most recently he was a co-founder and chief scientist of NexLP, a startup that leverages the latest advances in Natural Language Processing (NLP), Cognitive Analytics, and Machine Learning in the legal and compliance domains. NexLP was acquired by Reveal in 2020. Prof. Roth received his B.A Summa cum laude in Mathematics from the Technion, Israel, and his Ph.D. in Computer Science from Harvard University in 1995.



date: 2023-11-17
speaker: Stephanie Forrest, Arizona State University,
title: The Biodesign of Computing
location: Nguyen Engineering Bldg., Conference Room 4201
start_time: 2:00 pm
end_time: 3:00 pm
abstract:
Software today is an evolving adaptive system. Although we think of computer programs as the products of intelligent design, they also evolve inadvertently through the actions of many individual programmers, often leading to unanticipated consequences. Similarly, economic and political incentives produce arms races between competitors and adversaries, which in turn have shaped the cyber 
landscape.  Because software is subject to constraints similar to those faced by evolving biological systems, we have much to gain by viewing computing through the lens of biology.  The talk will highlight research applying the mechanisms of evolution quite directly to software, including repairing bugs, closing vulnerabilities, and optimizing GPU codes.  The results have implications for how we think more generally about engineering complex systems that are subject to
evolutionary pressures and engineering constraints.

bio:
Stephanie Forrest is Professor of Computer Science at Arizona State University, where she directs the Biodesign Center for Biocomputation, Security and Society.  Her interdisciplinary research focuses on the intersection of biology and computation, including cybersecurity, software engineering, evolutionary computation, and biological modeling.

Prior to joining ASU in 2017, she was a Distinguished Professor at the University of New Mexico and served for 5 years as Dept. Chair. She is a member of the Santa Fe Institute External Faculty, where she has also served as co-Chair of its Science Board and Interim VP for Academic Affairs.  She spent 2013-2014 at the U.S. Dept. of State as a Senior Science Advisor for cyberpolicy.  She was educated at St.John's College (B.A.) and the University of Michigan (M.S. and Ph.D. in Computer Science).

Some of her awards include: The 2023 IEEE Computational Intelligence Pioneer Award; The 2020 Test of Time Award from the IEEE Security and Privacy Symposium; The 2019 Most Influential Paper Award from the International Conference on Software Engineering, the Santa Fe Institute Stanislaw Ulam Memorial Lectures, the ACM/AAAI Allen Newell Award, and the NSF Presidential Young Investigator Award.  She is a Fellow of the IEEE and a member of the Computing Research Association Board of Directors.



date: 2023-11-30
speaker: Ramesh Karri, New York University
title: High-Level Approaches to Hardware and Embedded Security
location: Fenwick 1040, Fenwick Library
start_time: 11:00 am
end_time: 12:00 pm
abstract:
As designers increasingly rely on third-party intellectual property (IP) cores and outsourcing in the integrated circuit (IC) design and manufacturing process, security vulnerabilities have been on the rise. This has forced both IC designers and end users to re-evaluate their trust in ICs, as unprotected ICs are susceptible to reverse engineering, IP piracy, and the insertion of malicious circuits and backdoors. In this talk, I will present High-Level Design for Trust techniques that my group has developed to address these threats: Locking of Designs and Secure Sourcing of IPs for High-Level Integration. Implementing built-in locking mechanisms aims to prevent reverse engineering. Secure Sourcing thwarts Trojan insertion in third-party IPs. I will wrap up the presentation by emphasizing the importance of hardware security from economic, security, and safety standpoints, and share my vision for the exciting field of hardware cybersecurity

bio:
Ramesh Karri is a Professor of Electrical and Computer Engineering at New York University. He co-directs the NYU Center for Cyber Security (http://cyber.nyu.edu). He co-founded the Trust-Hub (http://trust-hub.org) and founded/organizes the Embedded Systems Challenge (https://csaw.engineering.nyu.edu/esc), the annual red-blue team event.

Ramesh Karri holds a Ph.D. in Computer Science and Engineering from the University of California at San Diego, and a B.E in ECE from Andhra University. With a focus on hardware cybersecurity, his research and educational endeavors encompass trustworthy ICs, processors, and cyber-physical systems; security-aware computer-aided design, test, verification, validation, and reliability; nano meets security; hardware security competitions, benchmarks, and metrics; biochip security; and additive manufacturing security. Ramesh has published over 300 articles in prestigious journals and conferences.

A Fellow of IEEE, Ramesh's work on hardware cybersecurity has earned numerous best paper award nominations (ICCD 2015 and DFTS 2015) and awards (ITC 2014, CCS 2013, DFTS 2013, VLSI Design 2012, ACM Student Research Competition at DAC 2012, ICCAD 2013, DAC 2014, ACM Grand Finals 2013, Kaspersky Challenge, and Embedded Security Challenge). He has also received the Humboldt Fellowship and the National Science Foundation CAREER Award. Ramesh is the Editor in Chief of the ACM Journal of Emerging Computing Technologies and an Associate Editor for IEEE and ACM journals. He's had leadership roles in various IEEE conferences like ICCD, HOST, DFTS, and others. He served as an IEEE Computer Society Distinguished Visitor from 2013-2015 and was on the Executive Committee for Security@DAC from 2014-2017. Additionally, he's been on multiple PCs and delivered keynotes on Hardware Security and Trust at events like ESRF, DAC, MICRO.



date: 2024-02-09
speaker: Alex Wong, Yale University
title: Unsupervised Learning of Depth Perception and Beyond
location: ENGR 4801
start_time: 10:00 am
end_time: 11:00 am
abstract:
Deep neural networks have seen empirical successes across computer vision tasks, but training them requires tens of thousands to millions of examples, which typically come in the form of an image or images, and human annotated ground truth. Curating vision datasets, in general, amounts to numerous man-hours; tasks like depth estimation require an even more massive effort. I will introduce an alternative form of supervision that leverages multi-sensor validation as an unsupervised (or self-supervised) training objective for depth estimation. I will demonstrate how one can leverage synthetic data and the abundance of publicly available pretrained models, which has largely relied on expensive manual labeling, to learn or distill the regularities of our visual world. In doing so, I show that one can design smaller and faster models that can operate in real-time with state-of-the-art performance. Not only that, these models can be adapted online to novel environments in which they are deployed. Additionally, I will discuss the current limitations of data augmentation procedures used during unsupervised training, which involves reconstructing the inputs as the supervision signal, and detail a method that allows one to scale up and introduce previously inviable augmentations to boost performance. Finally, I will show that unsupervised depth training can serve as a feasible form of large-scale pretraining to produce backbones suitable for semantic tasks.

bio:
Alex Wong is an Assistant Professor in the department of Computer Science and the director of the Vision Laboratory at Yale University. He received his Ph.D. in Computer Science from the University of California, Los Angeles (UCLA) in 2019 and was co-advised by Stefano Soatto and Alan Yuille. He was previously a post-doctoral research scholar at UCLA under the guidance of Stefano Soatto. His research lies in the intersection of machine learning, computer vision, and robotics and largely focuses on multi-sensor fusion for 3D reconstruction, robust vision under adverse conditions, unsupervised learning, and medical image analysis. His work has received the outstanding student paper award at the Conference on Neural Information Processing Systems (NeurIPS) 2011 and the best paper award in robot vision at the International Conference on Robotics and Automation (ICRA) 2019.


date: 2024-02-16
speaker: Enrique Mallada, Johns Hopkins University
title: Reinforcement Learning for Safety-Critical Systems
location: ENGR 4201
start_time: 11:00 am
end_time: 12:00 pm
abstract:
Integrating Reinforcement Learning (RL) in safety-critical applications, such as autonomous vehicles, healthcare, and industrial automation, necessitates an increased focus on safety and reliability. In this talk, we consider two complementary mechanisms to augment RL's suitability for safety-critical systems.
 
Firstly, we consider a constrained reinforcement learning (C-RL) setting, wherein agents aim to maximize rewards while adhering to required constraints on secondary specifications. Several algorithms rooted in sampled-based primal-dual methods have been recently proposed to solve this problem in policy space. However, such methods exhibit a discrepancy between the behavioral and optimal policies due to their reliance on stochastic gradient descent-ascent algorithms. We propose a novel algorithm for constrained RL that does not suffer from these limitations. Leveraging recent results on regularized saddle-flow dynamics, we develop a novel stochastic gradient descent-ascent algorithm whose trajectories almost surely converge to the optimal policy.
 
Secondly, we study the problem of incorporating safety-critical constraints to RL that allow an agent to avoid (unsafe) regions of the state space. Though such a safety goal can be captured by an action-value-like function, a.k.a. safety critics, the associated operator lacks the desired contraction and uniqueness properties that the classical Bellman operator enjoys. In this work, we overcome the non-contractiveness of safety critic operators by leveraging that safety is a binary property. To that end, we study the properties of the binary safety critic associated with a deterministic dynamical system that seeks to avoid reaching an unsafe region. We formulate the corresponding binary Bellman equation (B2E) for safety and study its properties. While the resulting operator is still non-contractive, we fully characterize its fixed points representing--except for a spurious solution--maximal persistently safe regions of the state space that can always avoid failure. We provide an algorithm that, by design, leverages axiomatic knowledge of safe data to avoid spurious fixed points.

bio:
Enrique Mallada has been an associate professor of electrical and computer engineering at Johns Hopkins University since 2022. Before joining Hopkins in 2016 as an assistant professor, he was a post-doctoral fellow at the Center for the Mathematics of Information at the California Institute of Technology from 2014 to 2016. He received his telecommunications engineering degree from Universidad ORT, Uruguay, in 2005 and his Ph.D. degree in electrical and computer engineering with a minor in applied mathematics from Cornell University in 2014. Dr. Mallada was awarded the Johns Hopkins Alumni Association Excellence in Teaching award in 2021, the NSF CAREER award in 2018, the ECE Director's Ph.D. Thesis Research Award for his dissertation in 2014, the Cornell University's Jacobs Fellowship in 2011 and the Organization of American States scholarship from 2008 to 2010. His research interests lie in the areas of control, dynamical systems, optimization, and machine learning, with applications to infrastructure networks and autonomous systems.


date: 2024-03-20
speaker: Yanxi Liu, Penn State University
title: Human versus Machine Perception of Patterns - A visual Turing Test: “Are you a human or a robot?”
location: Nguyen Engineering Bldg., Conference Room 4201
start_time: 2:00 pm
end_time: 3:00 pm
abstract:
Regularities with varying form and scale pervade our natural and man-made world. From insects to mammals, the ability to sense regular patterns has a neurobiological basis and has been observed in many levels of intelligence and behavior. From Felix Kleinʼs Erlanger program, D’Arcy Thompson’s Growth-and-Form, to the Gestalt principles of perception, much of our understanding of the world is based on the perception and recognition of repetitions, generalized by the mathematical concept of symmetry and symmetry groups. Given the ubiquity of symmetry in both the physical and the digital worlds, a computational model for symmetry-based regularity perception is especially pertinent to computer vision, computer graphics, robotics and machine intelligence in general, where an intelligent being (e.g. a robot) seeks to perceive, reason and interact with the chaotic world in the most effective and efficient manner. Surprisingly, we have limited knowledge on how humans perceive regular patterns and little progress has been made in computational models for noisy, albeit near-regular patterns in real data. In this talk, I present parallels as well as differences between machine perception and human perception of visual regularity. I shall report our recent exploration on human perception of patterns using 2D crystallographic groups as a guiding principle via neuroimaging, an un-supervised learning approach for recurring patterns discovery in the wild, a deep learning framework to mimic human perception of symmetry from photos as well as a successful attempt at building a symmetry-based Turing test to tell humans and robots apart: a symmetry reCAPTCHA.

bio:
Dr. Yanxi Liu is a Professor of EECS at Penn State University (PSU), University Park, USA, trained in physics/EE, computer science and theoretical robotics/AI (B.S, China; Ph.D. USA; Postdoc, France). With an NSF (USA) research-education fellowship award, she spent one year at DIMACS (NSF center for DIscrete MAthematics and Theoretical Computer Science) before joining the faculty of the Robotics Institute, Carnegie Mellon University for ten years. Currently at PSU, she is the director of the Human Motion Capture Lab for Smart Health and co-directs the Lab for Perception, Action and Cognition (LPAC). Dr. Liu has been a visiting professor at Stanford University, ETH Zurich, Tsinghua University and Google/MSR/MSRA. She is on sabbatical 2023-2024 at CMU. A central theme of Dr. Liu's research is on group theory-based “computational regularity” for multimodality data (funded continuously by US NSF, including a prestigious multidisciplinary INSPIRE grant) with diverse applications in robotics, human/machine perception, human activity in sports and in health. Dr. Liu chaired three international competitions at CVPR, ECCV, ICCV on Computer Vision algorithms for Detecting Symmetry in the Wild, and is the lead author for the book on “Computational Symmetry in Computer Vision and Computer Graphics”. Her industrial visits to Google Mountain View and Microsoft Silicon Valley/Uber resulted in two granted US patents. She has served as a program chair for Computer Vision and Pattern Recognition (CVPR) Conference 2017 and Winter Conference on Applications of Computer Vision (WACV) 2019, area chairs for all major computer vision/graphics conferences (CVPR/ECCV/ICCV/MICCAI/ACM MM/SIGGRAPH), and as an associate editor for IEEE Transaction of Pattern Analysis and Machine Intelligence (PAMI).


date: 2024-03-13
speaker: Dr. Simon Ou, University of South Florida
title: A Preliminary Study on Using Large Language Models in Software Pentesting
location: Dean’s Conference Room, Nguyen Engineering Building
start_time: 1:00 pm
end_time: 2:00 pm
abstract:
Large Language Models (LLM) are perceived to offer promising potential for automating security tasks, such as those found in security operation centers (SOCs). As a first step towards evaluating this perceived potential, we investigate the use of LLMs in software pentesting, where the main task is to automatically identify software security vulnerabilities in source code. We hypothesize that an LLM-based AI agent can be improved over time for a specific security task as human operators interact with it. Such improvement can be made, as a first step, by engineering prompts fed to the LLM based on the responses produced, to include relevant contexts and structures so that the model provides more accurate results. Such engineering efforts become sustainable if the prompts that are engineered to produce better results on current tasks, also produce better results on future unknown tasks. To examine this hypothesis, we utilize the OWASP Benchmark Project 1.2 which contains 2,740 hand-crafted source code test cases containing various types of vulnerabilities. We divide the test cases into training and testing data, where we engineer the prompts based on the training data (only) and evaluate the final system on the testing data. We compare the AI agent’s performance on the testing data against the performance of the agent without prompt engineering. We also compare the AI agent’s results against those from SonarQube, a widely used static code analyzer for security testing. We built and tested multiple versions of the AI agent using different off-the-shelf LLMs – Google’s Gemini-pro, as well as OpenAI’s GPT-3.5-Turbo and GPT-4-Turbo (with both chat completions and assistants APIs). The results show that using LLMs is a viable approach to building an AI agent for software pentesting that can improve through repeated use and prompt engineering.

bio:
Dr. Xinming Ou is a professor of Computer Science and Engineering at the University of South Florida. Dr. Ou's research is primarily in cyber defense technologies, with a focus on computer systems, programming languages, and human-centric approaches.  He has broad interest and ongoing work in security operations, IoT/CPS security, intrusion and forensics analysis, and mobile system security. Dr. Ou's research has been funded by the National Science Foundation, the Department of Defense, the Department of Homeland Security, the Department of Energy, the National Institute of Standards and Technology, HP Labs, and Rockwell Collins. He is a recipient of the 2010 U.S. NSF Faculty Early Career Development (CAREER) Award, a three-time winner of the HP Labs Innovation Research Program (IRP) award, and the 2013 Kansas State University Frankenhoff Outstanding Research Award.


date: 2024-03-25
speaker: James Bidzos, CEO/Founder of Verisign
location: Merten Hall, Room 1204
start_time: 4:30 pm
end_time: 5:30 pm
title:
“Scrambling for Privacy” - Inside the Encryption Wars 1986-2000 and the battle over privacy, security, and the future of a secure Internet

abstract:
Public-key cryptography, or PKC, first appeared in the late 1970s, as a proposed solution to the problem of security over future open networks, where parties wishing to communicate or transact securely may have no prior relationship, and authentication, via digital signatures, would be required. Interest and research, while lively, was mostly scientific and academic until the 1980s when modest commercialization efforts began. And today, PKC is everywhere. There is broad and deep deployment in network infrastructure, protocols, operating systems, browsers, email, smartphones, smartcards, and numerous applications. The impact is considerable; PKC has enabled global Internet usage, especially e-commerce, to rapidly grow. 
 
But the path to the outcome we see today was neither smooth nor certain.
 
Technology often gets ahead of policy, especially in the Internet age. In the 1980s and 1990s, PKC collided with national security, law enforcement, civil liberties, and the security needs of the emerging digital economy. Today's speaker was at the forefront of the 15-year struggle for a balance, and will share that history from the inside, including lessons learned from building two companies along the way. And while the conflicts were mostly resolved by 2000, issues remain today.

bio:
Jim Bidzos is the Chairman, CEO, and president of Reston-headquartered Verisign, Inc., a technology services provider he, along with Ron Rivest, founded in 1995 by spinning it out of RSA Data Security, Inc.  From 1986 until 2000, he was also the CEO of RSA Data Security, Inc., a company founded by MIT Professors Ron Rivest, Adi Shamir, and Len Adleman, the inventors of the RSA public-key crypto-system. He also founded the RSA Conference in 1991, which is today the definitive annual cybersecurity event, drawing over 50,000 participants each year, to discuss technology, policy, and much more.


date: 2024-04-19
speaker: Fabrizio Ruggeri, Consiglio Nazionale delle Ricerche, Italy
title: Advances in Adversarial Risk Analysis
location: Nguyen Engineering Bldg., Room 1602
start_time: 11:00 am
end_time: 12:00 pm
abstract:
In the first part of my talk I will talk about Adversarial Classification. In multiple domains such as malware detection, automated driving systems, or fraud detection, classification algorithms are susceptible to being attacked by malicious agents willing to perturb the value of instance covariates in search of certain goals. Such problems pertain to the field of adversarial machine learning and have been mainly dealt with, perhaps implicitly, through game-theoretic ideas with strong underlying common knowledge assumptions. These are not realistic in numerous application domains in relation to security. We present an alternative statistical framework that accounts for the lack of knowledge about the attacker’s behavior using adversarial risk analysis concepts. In the second part I will discuss an adversarial risk analysis framework for the software release problem. A major issue in software engineering is the decision of when to release a software product to the market. This problem is complex due to, among other things, the uncertainty surrounding the software quality and its faults, the various costs involved, and the presence of competitors. A general adversarial risk analysis framework is proposed to support a software developer in deciding when to release a product and showcased with an example.

bio:
Fabrizio Ruggeri (B.Sc. Mathematics Milano, M.Sc. Statistics Carnegie Mellon, Ph.D. Statistics Duke) is Senior Fellow at the Istituto di Matematica Applicata e Tecnologie Informatiche (IMATI) in Milano of CNR (Consiglio Nazionale delle Ricerche) where he had been a researcher from 1988 to May 2023 (as Research Director since 2001). He is member of the Faculty of the Ph.D. programme in Mathematics at the universities of Milano-Bicocca and Pavia. He had various international appointments, including Adjunct Professor at Queensland University of Technology (Brisbane, Australia), International Professor Affiliate at Polytechnic Institute (New York University, USA), Chair of Excellence at Universidad Carlos III and ICMAT-CSIC (Madrid, Spain) and Faculty of the Ph.D. programme in Statistics at the Universidad de Valparaiso (Chile). He is the President-Elect of the International Statistical Institute (ISI) for the 2023-2025 term, after which he will serve as President from 2025 to 2027. He has been President of ENBIS (European Network for Business and Industrial Statistics), ISBA (International Society for Bayesian Analysis) and ISBIS (International Society for Business and Industrial Statistics), and ISI Vice President. He is a Fellow of IMS (Institute of Mathematical Statistics), ASA (American Statistical Association) and ISBA (which also awarded him the first Zellner Medal), and ENBIS Honorary Member. He is Editor-in-Chief of Applied Stochastic Models in Business and Industry and Wiley StatsRef, an online encyclopedia. He is the Director of the Applied Bayesian Statistics summer school organized by CNR-IMATI since 2004 and Chair of the series of workshops on Bayesian Inference in Stochastic Processes. He is a member of the Advisory Scientific Committee of the Institute of Statistics of Academia Sinica, Taiwan. He is author of over 200 articles (including 120 in refereed journals) and author/editor of 6 books. His interests are mostly in Bayesian Statistics and Decision Analysis, especially about robustness, stochastic processes and industrial applications, mainly in reliability. His interests also cover other areas, in particular concentration functions, wavelets and, more recently, healthcare frauds and Adversarial Risk Analysis.


