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X-WR-CALDESC:Events for CECS
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DTSTART;TZID=America/Los_Angeles:20190501T150000
DTEND;TZID=America/Los_Angeles:20190501T160000
DTSTAMP:20231011T190852Z
CREATED:20231011T190846Z
LAST-MODIFIED:20231011T190852Z
UID:14555-1556722800-1556726400@www.cecs.uci.edu
SUMMARY:"Safety Verification and Training for Learning-enabled Cyber-Physical Systems"
DESCRIPTION:Name: Jyotirmoy Vinay Deshmukh\nDate and Time: Tuesday\, May 14\, 2019 at 2:00 p.m. \nLocation: Donald Bren Hall 4011 \nAbstract: \nWith the increasing popularity of deep learning\, there have been several efforts to use neural network based controllers in cyber-physical system applications. However\, neural networks are equally well-known for their lack of interpretability\, explainability and verifiability. This is especially an issue for safety-critical cyber-physical systems such as unmanned aerial vehicles or autonomous ground vehicles. How can we verify that a neural network based controller will always keep the system safe? We look at a new verification approach based on automatically synthesizing a barrier certificate for the system to prove that: starting from a given set of initial conditions\, the system behavior can never reach an unsafe state. Barrier Certificates are essentially a generalization of inductive invariants for continuous dynamical systems\, and we will show how we can use nonlinear SMT solvers to establish the barrier certificate conditions. A more intriguing challenge is whether we can actually train neural networks to obey safety constraints. We will look at a new way of reward shaping in reinforcement learning that could help achieve this goal. \nBiography: \nJyotirmoy V. Deshmukh (Jyo) is an assistant professor in the Department of Computer Science in the Viterbi School of Engineering at the University of Southern California in Los Angeles\, USA. Before joining USC\, Jyo worked as a Principal Research Engineer in Toyota Motors North America R&D. He got his Ph.D. degree from the University of Texas at Austin and was a post-doctoral fellow at the University of Pennsylvania. Jyo’s research interest is in the broad area of formal methods. Currently\, Jyo is interested in using logic-based methods for machine learning\, and in techniques for the analysis\, design\, verification and synthesis of cyber-physical systems\, especially those that use AI-based perception\, control and planning algorithms.
URL:https://www.cecs.uci.edu/event/safety-verification-and-training-for-learning-enabled-cyber-physical-systems/
LOCATION:CA
CATEGORIES:Seminars at CECS
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DTSTART;TZID=America/Los_Angeles:20190522T150000
DTEND;TZID=America/Los_Angeles:20190522T160000
DTSTAMP:20231011T190852Z
CREATED:20231011T190846Z
LAST-MODIFIED:20231011T190852Z
UID:14556-1558537200-1558540800@www.cecs.uci.edu
SUMMARY:Data-Driven Modeling of Cyber-Physical Systems using Side-Channel Analysis
DESCRIPTION:Title: “Data-Driven Modeling of Cyber-Physical Systems using Side-Channel Analysis”\nName: Sujit Rokka Chhetri \nDate: May 20\, 2019 \nTime: 3:00 PM \nLocation: EH 5200 \nCommittee: Prof. Mohammad Al Faruque (Chair)\, Prof. Pramod Khargonekar\, Prof. Fadi Kurdahi \nAbstract: \nCyber-Physical System consists of the integration of computational components in the cyber-domain with the physical-domain processes. In cyber-domain\, embedded computers and networks monitor and control the physical processes\, and in the physical-domain the sensors and actuators aid in interacting with the physical world. This interaction between the cyber and physical domain brings unique modeling challenges one of which includes the integration of discrete models in cyber-domain with the continuous physical domain processes. However\, the same cyber-physical interaction also opens new opportunities for modeling. For example\, the information flow in the cyber-domain manifests physically in the form of energy flows in the physical domain. Some of these energy flows unintentionally provide information about the cyber-domain through the side-channels. \nIn this thesis\, the extensive analysis of the side-channels (such as acoustic\, magnetic\, thermal\, power and vibration) of the cyber-physical system is performed. Based on this analysis data-driven models are estimated. These models are then used to perform security vulnerability analysis (for confidentiality and integrity)\, whereby\, new attack models are explored.  Furthermore\, the data-driven models are also utilized to create a defensive mechanism to minimize the information leakage from the system and to detect attacks to the integrity of the system. The cyber-physical manufacturing systems are taken as use cases to demonstrate the applicability of the modeling approaches. \nFinally\, the side-channel analysis is also performed to aid in modeling digital twins of the cyber-physical systems. Specifically\, a novel methodology to utilize low-end sensors to analyze the side-channels and build the digital twins is proposed. These digital twins are used to capture the interaction between the cyber-domain and the physical domain of the manufacturing systems\, and aid in process quality inference and fault localization. Using side-channels these digital twins are kept up-to-date\, which is one of the fundamental requirements for building digital twins.
URL:https://www.cecs.uci.edu/event/data-driven-modeling-of-cyber-physical-systems-using-side-channel-analysis/
LOCATION:CA
CATEGORIES:PhD Defenses
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