The vast majority of critical infrastructure, including transportation systems, smart grid, and gas distribution systems, is currently managed by Cyber-Physical Systems (CPS). CPS essentially consist of a physical process (dynamical system) and a network of sensors, controllers and actuators which realize a feedback loop for managing the underlying dynamical system. Due to close interaction between the cyber and physical components in a CPS, these systems pose unique security challenges which cannot be addressed by conventional cyber security methods. Motivated by the need to secure CPS against malicious attacks, we consider the problem of estimating the state of a noisy linear dynamical system when an unknown subset of sensors is arbitrarily corrupted by an adversary. We propose a secure state estimation algorithm, and derive (optimal) bounds on the achievable state estimation error given an upper bound on the number of attacked sensors. The proposed state estimator involves Kalman filters operating over subsets of sensors to search for a sensor subset which is reliable for state estimation. In addition, we give a coding theoretic view of attack detection and state estimation against sensor attacks in a noiseless dynamical system. This is joint work with Yasser Shoukry, Nikhil Karamchandani, Suhas Diggavi and Paulo Tabuada.
Shaunak Mishra is a PhD student in the Electrical Engineering Department at UCLA. He holds a B.Tech degree from the Indian Institute of Technology (IIT) Kharagpur (2010), and an M.S. degree from UCLA (2011). He is a recipient of the Henry Samueli Fellowship (2010-2011), and was a finalist for the Qualcomm Innovation Fellowship 2014. He has held summer internship positions at Yahoo! Labs (scalable machine learning group, 2015), Qualcomm Research (small cells team, 2013), and EPFL (2012, 2011). His research interests are broadly in information theory and statistics with applications in security, machine learning and wireless networks.