Robustness of Anomaly Detection Models for Industrial Control Systems under Training-Time Data Contamination
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 i
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- FuzzySimilar title/name (fuzzy) · 87%lllyasviel/ControlNet-v1-1 →
“Fuzzy title match (0.94): “Robustness of Anomaly Detection Models for Industrial Contro” ≈ “lllyasviel/ControlNet-v1-1””
- FuzzySimilar title/name (fuzzy) · 87%lllyasviel/ControlNet →
“Fuzzy title match (0.94): “Robustness of Anomaly Detection Models for Industrial Contro” ≈ “lllyasviel/ControlNet””
- PossiblePossibly related (embedding) · 49%IBM Granite Time Series models bring real-time forecasting and anomaly detection to Confluent Cloud - IBM →
- LinkedLinked via arxiv author · 85%Mustafa Umut Ozbek →
“Robustness of Anomaly Detection Models for Industrial Control Systems under Training-Time Data Contamination”
- LinkedLinked via arxiv author · 85%Taiwo Ojo →
“Robustness of Anomaly Detection Models for Industrial Control Systems under Training-Time Data Contamination”
- LinkedLinked via arxiv author · 85%Pooria Madani →
“Robustness of Anomaly Detection Models for Industrial Control Systems under Training-Time Data Contamination”
- LinkedLinked via arxiv author · 85%Khalil El-Khatib →
“Robustness of Anomaly Detection Models for Industrial Control Systems under Training-Time Data Contamination”