Abstract:This letter presents an optimal-transport (OT)-driven, distributionally robust attack detection algorithm, OT-DETECT, for cyber-physical systems (CPS) modeled as partially observed linear stochastic systems. The underlying detection problem is formulated as a minmax optimization problem using 1-Wasserstein ambiguity sets constructed from observer residuals under both the nominal (attack-free) and attacked regimes, and show that the minmax detection problem can be reduced to a finite-dimensional linear program for computing the worst-case distribution (WCD). Off-support residuals are handled via a kernel-smoothed score function that drives a CUSUM procedure for sequential detection. We also establish a non-asymptotic tail bound on the false-positive error of the CUSUM statistic under the nominal (attack-free) condition, under mild assumptions. Numerical illustrations are provided to evaluate the robustness properties of OT-DETECT.
| Comments: | 7 pages, 3 figures; submitted to a journal. This version contains detailed proofs of Theorems III.1 and IV.1, and elaborated contributions |
| Subjects: | Optimization and Control (math.OC); Signal Processing (eess.SP); Systems and Control (eess.SY) |
| Cite as: | arXiv:2603.16588 [math.OC] |
| (or arXiv:2603.16588v2 [math.OC] for this version) | |
| https://doi.org/10.48550/arXiv.2603.16588 arXiv-issued DOI via DataCite |
From: Souvik Das [view email]
[v1]
Tue, 17 Mar 2026 14:36:14 UTC (131 KB)
[v2]
Tue, 26 May 2026 15:01:25 UTC (436 KB)
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