Attack-Resilient State Estimation in the Presence of Noise

Conference Paper

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 $l_0$-based state estimator that can be formulated as a mixed-integer linear program and its convex relaxation based on the $l_1$ norm are presented. For both $l_0$ and $l_1$-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.

Full Text

Duke Authors

Cited Authors

  • Pajic, M; Tabuada, P., ; Lee, I., ; Pappas, G.J.,

Start / End Page

  • 527 - 532

Pages

  • 6

Conference Name

  • 54th IEEE Conference on Decision and Control (CDC)

Conference Location

  • Osaka, Japan

Conference Start Date

  • December 15, 2015

Conference End Date

  • December 18, 2015