Lightweight IIoT intrusion detection models exhibit poor cross-network generalization due to reliance on coarse port-category feature shortcuts, with evaluation outcomes sensitive to class imbalance.
Deep transfer learning for intrusion detection in industrial control networks: A comprehensive review.Journal of Network and Computer Applications, 220:103760
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
UNVERDICTED 2representative citing papers
A domain adaptation framework with spectral feature alignment and K-Medoids clustering after PCA improves unknown attack detection accuracy by up to 49% over baselines and gains another 26% from the clustering step in cross-domain ICS intrusion detection.
citing papers explorer
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Cross-Domain Generalization Failure in Lightweight Intrusion Detection Models for IIoT Networks
Lightweight IIoT intrusion detection models exhibit poor cross-network generalization due to reliance on coarse port-category feature shortcuts, with evaluation outcomes sensitive to class imbalance.
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Clustering-Enhanced Domain Adaptation for Cross-Domain Intrusion Detection in Industrial Control Systems
A domain adaptation framework with spectral feature alignment and K-Medoids clustering after PCA improves unknown attack detection accuracy by up to 49% over baselines and gains another 26% from the clustering step in cross-domain ICS intrusion detection.