High-confidence attack detection via Wasserstein-metric computations

Dan Li and Sonia Martínez
IEEE Control Systems Letters, 5 (2) (2020) 379-384

Abstract:

This letter considers a sensor attack and fault detection problem for linear cyber-physical systems, which are subject to system noise that can obey 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 with a finite number of measurements. The proposed detector may generate false alarms with a rate Δ in normal operation, where Δ can be tuned to be arbitrarily small by means of a benchmark distribution. Thus, the proposed detector is sensitive to sensor attacks and faults which have a statistical behavior that is different from that of the system noise. We quantify the impact of stealthy attacks on open-loop stable systems-which perturb the system operation while producing false alarms consistent with the natural system noise-via a probabilistic reachable set. Tractable implementation is enabled via a linear optimization to compute the detection measure and a semidefinite program to bound the reachable set.

File: (ArXiv version)

Bib-tex entry:

@article{DL-SM:20-cssl,
author = {D. Li and S. Mart{\'\i}nez},
title = {High-confidence attack detection via Wasserstein-metric computations },
journal= {IEEE Control Systems Letters},
pages = {379-384},
volume = {5},
number = {2},
year = {2020}
}