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Secure state estimation against sensor attacks in the presence of noise
We consider the problem of estimating the state of a noisy linear dynamical system when an unknown subset of sensors is arbitrarily corrupted by an adversary. We propose a secure state estimation algorithm, and derive (optimal) bounds on the achievable state estimation error given an upper bound on the number of attacked sensors. The proposed state estimator involves Kalman filters operating over subsets of sensors to search for a sensor subset which is reliable for state estimation. To further improve the subset search time, we propose Satisfiability Modulo Theory-based techniques to exploit the combinatorial nature of searching over sensor subsets. Finally, as a result of independent interest, we give a coding theoretic view of attack detection and state estimation against sensor attacks in a noiseless dynamical system
Secure State Estimation: Optimal Guarantees against Sensor Attacks in the Presence of Noise
Motivated by the need to secure cyber-physical systems against attacks, we
consider the problem of estimating the state of a noisy linear dynamical system
when a subset of sensors is arbitrarily corrupted by an adversary. We propose a
secure state estimation algorithm and derive (optimal) bounds on the achievable
state estimation error. In addition, as a result of independent interest, we
give a coding theoretic interpretation for prior work on secure state
estimation against sensor attacks in a noiseless dynamical system.Comment: A shorter version of this work will appear in the proceedings of ISIT
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