Adversarial Regression for Detecting Attacks in Cyber-Physical Systems

Attacks in cyber-physical systems (CPS) which manipulate sensor readings can cause enormous physical damage if undetected. Detection of attacks on sensors is crucial to mitigate this issue. We study supervised regression as a means to detect anoma- lous sensor readings, where each sensor's measure- ment is predicted as a function of other sensors. We show that several common learning approaches in this context are still vulnerable to stealthy at- tacks, which carefully modify readings of compro- mised sensors to cause desired damage while re- maining undetected. Next, we model the interac- tion between the CPS defender and attacker as a Stackelberg game in which the defender chooses detection thresholds, while the attacker deploys a stealthy attack in response. We present a heuris- tic algorithm for finding an approximately optimal threshold for the defender in this game, and show that it increases system resilience to attacks without significantly increasing the false alarm rate.