3 years ago

High-Confidence Attack Detection via Wasserstein-Metric Computations. (arXiv:2003.07880v1 [math.DS])

Dan Li, Sonia Martínez
This paper considers a sensor attack and fault detection problem for linear cyber-physical systems, which are subject to possibly non-Gaussian noise that can have an unknown light-tailed distribution. We propose a new threshold-based detection mechanism that employs the Wasserstein metric, and which guarantees system performance with high confidence. The proposed detector may generate false alarms with a rate in normal operation, where can be tuned to be arbitrarily small by means of a benchmark distribution which is part of our mechanism. Thus, the proposed detector is sensitive to sensor attacks and faults which have a statistical behavior that is different from that of the system's noise. We quantify the impact of stealthy attacks---which aim to perturb the system operation while producing false alarms that are consistent with the natural system's noise---via a probabilistic reachable set. To enable tractable implementation of our methods, we propose a linear optimization problem that computes the proposed detection measure and a semidefinite program that produces the proposed reachable set.

Publisher URL: http://arxiv.org/abs/2003.07880

DOI: arXiv:2003.07880v1

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