Cross-Domain Generalization Failure in Lightweight Intrusion Detection Models for IIoT Networks
Lightweight machine learning models are increasingly proposed for intrusion detection in Industrial Internet of Things (IIoT) networks due to their suitability for resource-constrained edge deployment. Most reported results evaluate these models only within their training network, leaving behavior on unseen networks unverified. This study trains four lightweight architectures on one IIoT dataset and evaluates them, without retraining, on two structurally distinct IIoT datasets using a feature representation restricted to attributes available across all three sources. Explainability analysis across two top-performing models shows both rely overwhelmingly on coarse port-category features; t...
| Authors | MD Azizul Hakim, Md Shihab Uddin, Talha Ibne Anis |
|---|---|
| Primary category | cs.CR |
| Published | 1 July 2026 |
| Last updated at source | 1 July 2026 |
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