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This preprint evaluates unsupervised machine learning algorithms for anomaly detection in Industrial Control Systems, indicating effective strategies under specific constraints.
This is a preprint version of a manuscript intended for submission to a peer-reviewed journal.This version has not undergone peer review. The paper presents a comprehensive multi-layer anomaly detection framework for IndustrialControl Systems (ICS), evaluating unsupervised machine learning algorithms across IoT/OT,Linux, Windows, and network layers under realistic false positive rate constraints.
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José Israel Nadal Vidal (2026) studied this question.
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