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