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In this paper, we consider the problem of attack-resilient state estimation,that is to reliably estimate the true system states despite two classes ofattacks: (i) attacks on the switching mechanisms and (ii) false data injectionattacks on actuator and sensor signals, in the presence of unbounded stochasticprocess and measurement noise signals. We model the systems under attack ashidden mode stochastic switched linear systems with unknown inputs and proposethe use of a multiple-model inference algorithm to tackle these securityissues. Moreover, we characterize fundamental limitations to resilientestimation (e.g., upper bound on the number of tolerable signal attacks) anddiscuss the topics of attack detection, identification and mitigation underthis framework. Simulation examples of switching and false data injectionattacks on a benchmark system and an IEEE 68-bus test system show the efficacyof our approach to recover resilient (i.e., asymptotically unbiased) stateestimates as well as to identify and mitigate the attacks.

Switching and Data Injection Attacks on Stochastic Cyber-Physical Systems: Modeling, Resilient Estimation and Attack Mitigation