Attack-Resilient and Privacy-Preserving Cyber-Physical Systems

ABSTRACT: The rapidly increasing dependence on Cyber-Physical Systems (CPS) in building critical infrastructures—in the context of smart cities, power grids, medical devices, and self-driving cars—has opened the gates to increasingly sophisticated and harmful attacks with financial, societal, criminal or political effects. While a traditional cyber-attack may leak credit-card or other personal sensitive information, a CPS-attack can lead to a loss of control in nuclear reactors, gas turbines, the power grid, transportation networks, and other critical infrastructure, placing the Nation’s security, economy, and public safety at risk.

In this talk, I will focus on two threat models namely (i) false data injection and (ii) Sybil attacks. Under the first threat model, we study the problem of estimating the state of a dynamical system when an adversary arbitrarily corrupts a subset of its sensors. Although of critical importance, this problem is NP-hard and combinatorial since the subset of attacked sensors in unknown. Using smart-grids and Quadrotors as examples, I will show how to tame the combinatorial nature of the problem using a novel technique named as “Satisfiability Modulo Convex Programming” or SMC for short. Under the second threat model, and motivated by the crowdsourcing aided road traffic estimation setup, we study the case where a fraction of users (vehicles) are malicious, and report wrong sensory information, or even worse, report the presence of Sybil (ghost) vehicles that do not physically exist. The motivation for such attacks lies in the possibility of creating a “virtual” congestion that can influence routing algorithms, leading to “actual” congestion and chaos. Similarly to the false data injection attack, our objective is to estimate the state of the physical system (average speed and congestion) from the corrupted information.

While in the previous two threat models we ignored the fact that these, possibly corrupted, sensor information is collected from different agents which may raise several privacy concerns, in the final part of this talk, I will show how to design privacy-preserving protocols based on partially homomorphic encryption where data is encrypted before sending it to an untrusted cloud computing infrastructure. The attack-resilient algorithms are then computed over the encrypted data without the ability to decrypt it leading to data analytics schemes that are both attack-resilient and privacy-preserving. I will finish by showing, through multiple experimental results, the real-time performance of the proposed algorithms.

BIO: Yasser Shoukry is an Assistant Professor in the Department of Electrical and Computer Engineering at University of Maryland, College Park. He received his Ph.D. in Electrical Engineering from the University of California, Los Angeles in 2015 where he was affiliated with both the Cyber-Physical Systems Lab as well as the Networked and Embedded Systems Lab. Before Joining UMD, Yasser spent two years as a joint post-doctoral associate at UC Berkeley, UCLA, and UPenn. Before pursuing his Ph.D. at UCLA, he spent four years as an R&D engineer in the industry of automotive embedded systems. Yasser’s research interests include the design and implementation of resilient Cyber-Physical Systems (CPS) and Internet-of-Things (IoT) by drawing on tools from embedded systems, formal methods, control theory, and machine learning.

Prof. Shoukry is the recipient of the Best Demo Award from the ACM/IEEE IPSN conference in 2017, the Best Paper Award from the ACM/IEEE ICCPS in 2016, the Distinguished Dissertation Award from UCLA EE department in 2016 and the UCLA Chancellor’s prize in 2011/2012. In 2015, he led the UCLA/Caltech/CMU team to win the NSF Early Career Investigators (NSF-ECI) research challenge. His team represented the NSF-ECI in the NIST Global Cities Technology Challenge, an initiative designed to advance the deployment of Internet of Things (IoT) technologies within a smart city.

Hosted by: Paul Bogdan