Resilient state estimation against switching attacks on stochastic cyber-physical systems

In this paper, we address the resilient state estimation problem for some relatively unexplored security issues for cyber-physical systems, namely switching attacks and the presence of stochastic process and measurement noise signals, in addition to attacks on actuator and sensor signals. We model the systems under attack as hidden mode stochastic switched linear systems with unknown inputs and propose the use of the multiple model inference algorithm developed in [1] to tackle these issues. We also furnish the algorithm with the lacking asymptotic analysis. Moreover, we characterize fundamental limitations to resilient estimation (e.g., upper bound on the number of tolerable attacks) and discuss the issue of attack detection under this framework. Simulation examples of switching attacks on benchmark and power systems show the efficacy of our approach to recover unbiased state estimates.