Date of this Version
Miroslav Pajic, Paulo Tabuada, Insup Lee, and George Pappas, "Attack-Resilient State Estimation in the Presence of Noise", Proceedings of the 54th IEEE Conference on Decision and Control (CDC 2015) , 527-532. December 2015.
We consider the problem of attack-resilient state estimation in the presence of noise. We focus on the most general model for sensor attacks where any signal can be injected via the compromised sensors. An l0-based state estimator that can be formulated as a mixed-integer linear program and its convex relaxation based on the l1 norm are presented. For both l0 and l1-based state estimators, we derive rigorous analytic bounds on the state-estimation errors. We show that the worst-case error is linear with the size of the noise, meaning that the attacker cannot exploit noise and modeling errors to introduce unbounded state-estimation errors. Finally, we show how the presented attack-resilient state estimators can be used for sound attack detection and identification, and provide conditions on the size of attack vectors that will ensure correct identification of compromised sensors.
CPS Embedded Control, CPS Security
Proceedings of the 54th IEEE Conference on Decision and Control (CDC 2015)
Date Posted: 26 January 2016
This document has been peer reviewed.