Distributionally Robust Tuning of Anomaly Detectors in Cyber-Physical Systems with Stealthy Attacks
| Autor: | Venkatraman Renganathan, Justin Ruths, Navid Hashemi, Tyler H. Summers |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Computer science
Detector Cyber-physical system Systems and Control (eess.SY) Electrical Engineering and Systems Science - Systems and Control Constant false alarm rate Moment (mathematics) symbols.namesake Noise Gaussian noise Bounded function FOS: Electrical engineering electronic engineering information engineering symbols Anomaly (physics) Algorithm |
| Zdroj: | ACC |
| DOI: | 10.23919/acc45564.2020.9147661 |
| Popis: | Designing resilient control strategies for mitigating stealthy attacks is a crucial task in emerging cyber-physical systems. In the design of anomaly detectors, it is common to assume Gaussian noise models to maintain tractability; however, this assumption can lead the actual false alarm rate to be significantly higher than expected. We propose a distributionally robust anomaly detector for noise distributions in moment-based ambiguity sets. We design a detection threshold that guarantees that the actual false alarm rate is upper bounded by the desired one by using generalized Chebyshev inequalities. Furthermore, we highlight an important trade-off between the worst-case false alarm rate and the potential impact of a stealthy attacker by efficiently computing an outer ellipsoidal bound for the attack-reachable states corresponding to the distributionally robust detector threshold. We illustrate this trade-off with a numerical example and compare the proposed approach with a traditional chi-squared detector. |
| Databáze: | OpenAIRE |
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