Recording-Aware Evaluation of Multimodal OT Intrusion Detection↗ (원문, 새 창)

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arXiv:2610.24128v1 Announce Type: new Abstract: Machine-learning-based intrusion detection systems (IDSs) for operational technology (OT) are commonly evaluated on benchmark datasets in which normal and attack data are collected in separate recordings. In such settings, high performance may reflect recording-associated differences as well as attack behavior. We introduce a recording-aware evaluation procedure that compares attack windows with background from the same recording, uses prior data