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March 2017
Free

CESG Seminar: Exo-Core — Software-Defined Hardware-Security

March 24 @ 4:10 am - 5:10 pm
WEB, Room 236-C,
Wisenbaker Engineering Building

Mohit Tiwari – University of Texas at Austin Abstract: Confinement is a fundamental security primitive. The ability to put private data in a box and ship the box to run untrusted code in an untrusted data center can transform systems security and expand the use of cloud services to regulated data. However, untrusted applications are hard to confine — we show that using only meta-data about the computation, a malicious process can leak secrets at hundreds of kilo-bits per second on machines today. Closing such leaks in the past has followed a piece-meal approach of closing individual channels. In this talk, we propose that exposing the micro-architecture to software can enable flexible defenses to a large class of vulnerabilities, and show that software solutions can implement efficient and verifiable solutions to hardware-security problems.   Bio: Mohit Tiwari received his PhD from UCSB (2011) and joined UT Austin as an Assistant Professor in Fall 2013. His research enables privacy for end-users through information leak-free containers — such containers can be used to create trustworthy computing services using untrusted data centers and vulnerable applications — and through anomaly detection across the computing stack. Professor Tiwari’s research has received the NSF Career Award (2015), Best Paper Awards (ASPLOS’15, PACT’09), IEEE Micro Top Picks (2010, 2014 Honorable Mention), and industry research awards from Google and Qualcomm.

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Free

CESG Teleseminar: From Brain Biometrics to Brain Hacking: Convergence of Neuroscience and Cyber Technology

March 23 @ 3:00 pm - 4:00 pm

Zhanpeng Jin Department of ECE, Binghamton University Room 333 Wisenbaker Engineering Building (fishbowl)   Abstract: Cryptographic systems often rely on the secrecy of cryptographic credentials; however, these are vulnerable to eavesdropping and can resist neither a user’s intentional disclosure nor coercion attacks where the user is forced to reveal the credentials. Conventional biometric keys (e.g., fingerprint, iris, etc.), unfortunately, can still be surreptitiously duplicated or adversely revealed. To this end, we argue that the most secure cryptographic credentials are ones of which the users aren’t even aware. On the basis of this argument, our research seeks to investigate a new psychophysiological approach for secure and trustworthy user authentication via reproducible, unique, non-volitional components of the electroencephalogram (EEG) brainwave responses, named “brainprints.” Moreover, we systematically evaluate how robust the cognitive brainprinting is to various cyber-attacks, particularly psychological and computational vulnerabilities. The preliminary results have proved the resistance of the brainprint authentication system to brainwave entrainment and impersonation. This research holds the potential to transform existing authentication systems into more secure, disclosure-resistant solutions; critical for high-security applications, as well as to strengthen our understanding of the unique cognitive and psychological secret of the human brain. “Brainprint” research has been reported by over 50 media outlets and named as one of the “future technology: 22 ideas about to change our world.”   Bio: Dr. Zhanpeng Jin is an Assistant Professor in Departments of Electrical and Computer Engineering, and Biomedical Engineering, and Director of Cyber-Med Lab at State University of New York at Binghamton. Prior to joining SUNY-Binghamton, He was a Postdoctoral Research Associate at the University of Illinois at Urbana-Champaign (UIUC) and received his Ph.D. degree in Electrical Engineering at the University of Pittsburgh. His research interests include emerging biometrics, cognitive neuroscience, cyber-physical security, neuromorphic computing, mobile health, and low-power sensing. He has served as the Associate Editor for the journals of Computers and Electrical Engineering, Computers in Biology and Medicine, and BioMedical Engineering Online, as well as served on the Technical Committees for many conferences. He received the BU ECE Outstanding Faculty Researcher Award in 2015, Best Paper Award in BHI’17, and Best Paper Award Nominee in ASP-DAC’17. His research has been supported by NSF, AFOSR, AFRL, SUNY Research Foundation, and a number of industrial companies. He is a senior member of IEEE.

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Free

ECE Leaders and Innovators Speaker Series and TEES Tech Talk

March 10 @ 11:30 am - 12:30 pm
WEB, Room 236-C,
Wisenbaker Engineering Building

New Frontiers of Information Networks: Opportunities & Challenges Dr. John D. Matyjas Air Force Research Laboratory Information Directorate Abstract: After a brief orientation on the Air Force Research Laboratory, this talk will focus on the innovation, development, and maturation of secure communications,  networking, and information management technologies. A timely, reliable, and mission-responsive Air Force network is critical to the translation of sensory data into actionable information and for assuring tailored communications globally. To build future elastic network capabilities that can respond to the mission and threat environment, we cannot rely solely on a data-neutral network. The future lies in affordable, extensible, interoperable communications architectures that intelligently distribute information in a robust way and enable shared situational awareness and timely decision-making, ultimately, to assure the mission. These desired attributes will be discussed in the context of broadly parallel consumer and industry demands for autonomous vehicle (ground and airborne) operations and human/machine-to-machine communications. Bio: Dr. John D. Matyjas received his Ph.D. in electrical engineering from State University of New York at Buffalo in 2004. Currently, he is serving as the Tech Advisor of the Computing & Communications Division at the Air Force Research Laboratory (AFRL) in Rome, NY. His research interests include dynamic multiple-access communications and networking, software defined RF, spectrum mutability, statistical signal processing and optimization, and neural networks. Dr. Matyjas was inducted as an AFRL Fellow in 2016. He is the recipient of the 2015 Air Force Association ‘Technology Manager of the Year’ Award, 2015 AFRL ‘Scientist of the Year’ Award, 2012 IEEE R1 Technology Innovation Award, and the 2010 IEEE Int’l Communications Conf. Best Paper Award. From 2012-2014, he served on the IEEE Trans. on Wireless Communications Editorial Advisory Board. He is an IEEE Senior Member, Secretary of the IEEE Mohawk Valley Section, chair of the IEEE Mohawk Valley Signal Processing Society, and member of Tau Beta Pi and Eta Kappa Nu Engineering Honor Societies.

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Free

CESG Teleseminar: Improving Cyber Security through Cyber Insurance and Data Analytics

March 3 @ 4:10 pm - 5:10 pm
WEB, Room 236-C,
Wisenbaker Engineering Building
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Parinaz Naghizadeh, University of Michigan Attempts to improve the state of cyber security have been on the rise over the past decade. In addition to enhancing existing software and infrastructure, there is a parallel need for incentivizing the adoption of these improved security practices by end users and organizations. My research aims to design such incentive mechanisms, and to leverage advances in data analytics for informed cyber-policy design. In this talk, I will first discuss the design of cyber insurance contracts, with an emphasis on users’ unobservable security decisions (moral hazard) and their interdependence in security. I will demonstrate the role of cyber insurance in instilling commitment towards improved cyber security by leveraging users’ interdependence. In addition, I will describe how predictive analytics based on machine learning techniques can be used as a tool for improving the design of these cyber-insurance contracts, and also for regulating security information sharing agreements. Further, I will present a game-theoretic framework for understanding individual users’ decisions towards security investments, and in particular, the effects of the network structure on the outcomes of their interactions. I will discuss how our findings extend several existing results in the literature, as well as their applications in other domains, including the study of spread of research and innovation, financial markets, and environmental pollution reduction policies. Bio: Parinaz Naghizadeh is a postdoctoral research fellow in EECS at the University of Michigan. Her research interests include cyber security, game theory, network economics, optimization, and data analytics. She received her Ph.D. in electrical engineering from the University of Michigan in 2016, M.Sc. degrees in electrical engineering and mathematics, both from the University of Michigan, in 2013 and 2014, respectively, and a B.Sc. in electrical engineering from Sharif University of Technology, Iran, in 2010. She was a recipient of the Barbour scholarship in the 2014-15 academic year.

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Free

CESG Teleseminar: Mixed Centralized/Decentralized Decision Protocols for Multi-Agent Systems

March 2 @ 2:30 pm - 4:00 pm

Abstract Multi-agent systems arise in diverse fields, including power systems, robotics, cyber-physical systems, and the Internet of Things. Coordinating these systems is often done using decentralized interactions, in which each agent only communicates with a small number of others. Decentralized algorithms offer several benefits, though they may have difficulty accommodating some performance demands, such as user privacy requirements. Toward addressing such challenges, I will present recent work on mixed centralized/decentralized decision protocols for multi-agent systems. Motivated by the availability of cloud computing, a centralized cloud computer is added to networks of agents in order to gather global information, perform centralized computations, and broadcast the results. As this happens, the agents continue to execute a decentralized behavior. The centralized nature of the cloud means it will be slower than the agents, though its slow, occasional transmissions do indeed enable multi-agent systems to handle various practical challenges. To this end, I will present mixed centralized/decentralized coordination algorithms that tolerate asynchronous information sharing and user privacy requirements, while still enabling strong theoretical guarantees of performance. In the asynchronous case, I will present an algorithm that allows each agent to perform useful work even if the agents have conflicting information about the network. For privacy, the framework of differential privacy is used, giving rise to a novel stochastic optimization algorithm. These algorithms draw from primal-dual optimization techniques and the theory of stochastic variational inequalities, and solve coordination tasks that are stated as convex optimization problems. The end result is a flexible coordination framework that tolerates an array of practical challenges, all while solving constrained coordination problems for teams of agents, regardless of whether an agent is a robot, a self-driving car, or any other physical entity. In addition to theoretical results, I will present robotic implementations of this work to demonstrate its applicability in practice. Bio Matthew Hale is a Ph.D. Candidate in Electrical and Computer Engineering at the Georgia Institute of Technology. In 2012, he received his B.S.E. in Electrical Engineering from the University of Pennsylvania, where he was a member of the GRASP Lab. He received his M.S. in Electrical and Computer Engineering from Georgia Tech in 2015, and was awarded the Colonel Oscar P. Cleaver Outstanding Graduate Student Award by the same department in 2013. His research interests include optimization and control for multi-agent systems, differential privacy, and hybrid systems. His work applies methods from these areas to cyber-physical systems and teams of robots.

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February 2017
Free

CESG Teleseminar: Making Wi-Fi Work in Multi-Hop Topologies: Automatic Negotiation and Allocation of Airtime

February 24 @ 4:10 pm - 5:10 pm
WEB, Room 236-C,
Wisenbaker Engineering Building
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Violet R. Syrotiuk – Arizona State University Abstract:  We propose a solution for mitigating the performance impairments of CSMA/CA protocols in multi-hop topologies based on the dynamic adaptation of the contention process experienced by nodes in a wireless network. A distributed protocol is used to negotiate the channel airtime for a node as a function of the traffic requirements of its neighborhood, taking into account bandwidth reserved for the control operations. A mechanism is provided for a node to tune its contention window depending on its allocated airtime. Different from previous schemes, a node’s contention window is fixed in size unless the traffic requirements of its neighborhood change.  The scheme is implemented on legacy commercial 802.11 devices.  Extensive experimental results, performed on the CREW European testbed, demonstrate the effectiveness of the approach. Bio: Violet R. Syrotiuk earned her Ph.D. in Computer Science from the University of Waterloo in Canada. She is currently an Associate Professor of Computer Science and Engineering at Arizona State University. Her interests lie in dynamic adaptation to changing conditions in wireless networks, especially at the MAC layer.  Her research has been supported by grants from NSF, ONR, and DSTO, and contracts with LANL, Raytheon, and General Dynamics among others. She serves on the editorial boards of Computer Networks and Computer Communications, as well as on the technical program and organizing committees of several major conferences sponsored by ACM and IEEE.

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CESG Teleseminar: Data-Driven Control and Optimization for Urban Infrastructures

February 24 @ 2:30 pm - 4:00 pm

Room 333 Wisenbaker Engineering Building (fishbowl) Shuo Han  – University of Pennsylvania   Abstract: Recent advances in sensing technology and autonomy have brought a myriad of new access points for sensing and control in urban infrastructures. This leads to the concept of “smart cities”, in which urban infrastructures are operated at an increased level of autonomy with the aid of sensing and control. A key component of smart cities is algorithms that convert data collected from sensors to decisions used for city operation. In many applications, data are used for modeling certain stochastic phenomena (e.g., human demand in cities) upon which decisions are made.  In order to provide rigorous performance guarantees in decision making, it is often desirable to not only obtain from data a nominal (probabilistic) model of the stochastic phenomenon but also uncertainty in the model. In this talk, I will present an optimization-based framework that explicitly quantifies and handles probabilistic model uncertainty for decision making. A distinctive feature of the framework is that it models the unknown stochastic phenomenon by a set of probability distributions that are consistent with data. For a large class of problems including several planning and scheduling problems in smart cities, I will show that the resulting optimization problem can be reformulated as a convex optimization problem whose solution can be computed efficiently. Using examples from power systems and transportation, I will show that our framework offers several advantages over conventional ways of modeling uncertainty.    Bio: Shuo Han is a postdoctoral researcher in the Department of Electrical and Systems Engineering at the University of Pennsylvania. He received his Ph.D. in Electrical Engineering from the California Institute of Technology in 2014. His current research focuses on developing rigorous frameworks for data-driven decision making that enable reliable and efficient operations of networked systems such as power and transportation networks. He was a finalist for the Best Student Paper Award at the 2013 American Control Conference.  

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CESG Teleseminar: Securing Distributed Systems Against Adversarial Attacks

February 23 @ 2:30 pm - 4:00 pm

Room 333, Wisenbaker Building (fishbowl) Lili Su – University of Illinois Abstract: Distributed systems are ubiquitous in both industry and our daily life. For example, we use clusters and networked workstations to analyze large amounts of data, use the worldwide web for information and resource sharing, and use the Internet of Things (IoT) to access a much wider variety of resources. In distributed systems, components are more vulnerable to adversarial attacks. In this talk, we model the distributed systems as multi-agent networks, and consider the most general attack model – Byzantine fault model. In particular, this talk will focus on the problem of distributed learning over multi-agent networks, where agents repeatedly collect partially informative observations (samples) about an unknown state of the world, and try to collaboratively learn the true state. We focus on the impact of the Byzantine agents on the performance of consensus-based non-Bayesian learning. Our goal is to design algorithms for the non-faulty agents to collaboratively learn the true state through local communication. At the end of this talk, I will also briefly mention our exploration on tolerating adversarial attacks in multi-agent optimization problems.   Bio: Lili Su is a Ph.D. candidate in the Electrical and Computer Engineering Department at the University of Illinois at Urbana-Champaign, working with Prof. Nitin Vaidya on distributed computing. She expects to receive her Ph.D. degree in May 2017. Her research intersects distributed computing, security, optimization, and learning. She was one of the three nominees for the 2016 International Symposium on Distributed Computing Best Student Paper Award. She received the 2015 International Symposium on Stabilization, Safety, and Security of Distributed Systems Best Student Paper Award. She also received the Sundaram Seshu International Student Fellowship for the academic year of 2016 to 2017 conferred by UIUC. In addition, she received the Outstanding Reviewer Award for her review service for IEEE Transactions on Communication in 2015.

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CESG Seminar: Leaping Over the Memory Wall with Data Prefetching and Cache Replacement

February 17 @ 4:10 am - 5:15 pm

Room 236C in Wisenbaker Building (WEB) Jinchun Kim – Texas A&M University   Abstract:  For decades, the primary tools in alleviating the “Memory Wall” have been large cache hierarchies and data prefetchers. Both approaches, become more challenging in modern, Chip-multiprocessor (CMP) design. Increasing the last-level cache (LLC) size yields diminishing returns on size; given VLSI power scaling trends, this approach becomes hard to justify. These trends also impact hardware budgets for data prefetchers and LLC replacement modules. Moreover, in the context of CMPs running multiple concurrent processes, prefetching and replacement accuracy is critical to prevent cache pollution effects.   In this talk, I will discuss two novel on-chip memory management techniques: Signature Path Prefetching (SPP) and Kill-the-PC (KPC) replacement algorithm. SPP is a data prefetcher that adaptively throttles itself on a per-prefetch stream basis. We compress a series of memory accesses into a small signature and iteratively use the signature until the prefetching confidence falls below a certain threshold. Also, unlike other history based algorithms which miss out on many prefetching opportunities when address patterns make a transition between physical pages, SPP tracks complex patterns across physical page boundaries and continues prefetching as soon as they move to new pages. While SPP is a pure prefetching scheme, KPC bridges the gap between data prefetcher and cache replacement. I’ll discuss how KPC can be used to eliminate the use of program counter and improve the performance of LLC replacement policy.   Bio: Jinchun Kim is a Ph.D. candidate of Electrical and Computer Engineering at Texas A&M University, advised by Dr. Paul V. Gratz. Jinchun’s research interests are in computer architecture with particular emphasis on future memory system design. He has been recognized with two best paper nominations at MICRO 2014 and MICRO 2016. Jinchun is currently on the job market for academic/industry research positions.    

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CESG Fishbowl Teleseminar: Efficient Fault-Tolerant Quantum Computing

February 16 @ 2:30 pm - 4:00 pm

Room 333 Wisenbaker Engineering Building (fishbowl) Martin Suchara – AT&T Labs Research ABSTRACT: Quantum error correction presents some of the most significant and interesting challenges that must be resolved before building an efficient quantum computer. Quantum error correcting codes allow to successfully run quantum algorithms on unreliable quantum hardware. Because quantum hardware suffers from errors such as decoherence, leakage or qubit loss, and these errors corrupt delicate quantum states rather than binary information, and the known error correction techniques are complex and have a high overhead.  In my talk, I first introduce the basics of quantum computing. Then I describe the two main families of quantum error correcting codes and quantify their overhead using specific examples of algorithms and hardware technologies. I describe several new techniques that I developed to reduce this overhead. For example, the maximum likelihood decoder (MLD) is an efficient algorithm that finds the recovery operation that maximizes the probability of a successful error correction given the observed error syndrome. Numerical simulations of the MLD algorithm for physical error rates around 10% showed a 100-fold reduction of the logical error probability compared to earlier techniques. I also show new designs of error correcting codes that are tailored to work more efficiently with the constraints of specific physical technologies.   BIO: Martin Suchara is a Principal Inventive Scientist at AT&T Labs Research since 2015. Prior to joining AT&T he was a Postdoctoral Scholar in the quantum-computing group at IBM T. J. Watson Research Center. His work focuses on making computation with quantum computers more efficient and reliable. He developed new quantum error correcting codes that improve error decoding efficiency. Martin received his PhD from the Computer Science department at Princeton University and postdoctoral training from UC Berkeley. Between 2011 and 2013 he coordinated the work of a small group of postdocs and students on the IARPA Quantum Computer Science Program and delivered the results to the Program Manager. Martin is the recipient of the Best Student Paper Award at ACM Sigmetrics 2011  

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