Attack-Resilient State Estimation for Noisy Dynamical Systems

Abstract

Several recent incidents have clearly illustrated the susceptibility of cyberphysical systems (CPS) to attacks, raising attention to security challenges in these systems. The tight interaction between information technology and the physical world has introduced new vulnerabilities that cannot be addressed with the use of standard cryptographic security techniques. Accordingly, the problem of state estimation in the presence of sensor and actuator attacks has attracted significant attention in the past. Unlike the existing work, in this paper, we consider the problem of attack-resilient state estimation in the presence of bounded-size noise. We focus on the most general model for sensor attacks where any signal can be injected via compromised sensors. Specifically, we present an $l_0$ -based state estimator that can be formulated as a mixed-integer linear program and its convex relaxation based on the $l_1$ norm. For both attack-resilient state estimators, we derive rigorous analytic bounds on the state-estimation errors caused by the presence of noise. Our analysis shows that the worst-case error is linear with the size of the noise and, thus, the attacker cannot exploit the noise to introduce unbounded state-estimation errors. Finally, we show how the $l_0$ and $l_1$ -based attack-resilient state estimators can be used for sound attack detection and identification; we provide conditions on the size of attack vectors that ensure correct identification of compromised sensors.