Adversarial Regression for Detecting Attacks in Cyber-Physical Systems
This addresses security for cyber-physical systems, but it is incremental as it builds on existing supervised regression methods.
The paper tackles the problem of detecting stealthy attacks that manipulate sensor readings in cyber-physical systems by modeling the defender-attacker interaction as a Stackelberg game and proposing a heuristic algorithm to set detection thresholds, resulting in increased resilience without significantly raising false alarms.
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 anomalous sensor readings, where each sensor's measurement is predicted as a function of other sensors. We show that several common learning approaches in this context are still vulnerable to \emph{stealthy attacks}, which carefully modify readings of compromised sensors to cause desired damage while remaining undetected. Next, we model the interaction 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 heuristic 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.