Robustness of Anomaly Detection Models for Industrial Control Systems under Training-Time Data Contamination
[Kryptografia i bezpieczeństwo] Autorzy: Mustafa Umut Ozbek, Taiwo Ojo, Pooria Madani i 2 więcej
Machine-learning-based anomaly detection is increasingly used in industrial control systems (ICS), yet most studies assume that detector training data is trustworthy. In practice, training data may be corrupted through compromised logs, labeling errors, manipulated historian records, or unsafe retraining processes. This paper evaluates the robustness of offline ICS anomaly-detection pipelines on the Secure Water Treatment (SWaT) benchmark under training-time contamination. We assess 11 heterogeneous anomaly detectors under three contamination strategies: random injection, similarity-targeted injection, and feature-noise injection. The first two insert attack samples into the nominal training pool, while the third adds bounded Gaussian noise to selected normal training samples. These attacks are contamination-based rather than gradient-driven poisoning methods. Contamination budgets from…
- Kategoria arxiv
- cs.CR
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- Kryptografia i bezpieczeństwo