Medoid Prototype Alignment for Cross-Plant Unknown Attack Detection in Industrial Control Systems
Deploying an intrusion detector trained in one industrial plant to another remains difficult because Industrial Control System (ICS) traffic is highly site-dependent, labels are scarce, and unseen attacks often appear after deployment. To address this challenge, this paper introduces a medoid prototype alignment framework for cross-plant unknown attack detection. Instead of aligning all source and target samples directly, the method first compresses heterogeneous traffic into a comparable representation space and then extracts robust medoid prototypes that summarize local operational structure in each domain. A prototype-calibrated transfer objective is further designed to align target pr...
| Authors | Luyao Wang |
|---|---|
| Primary category | cs.CR |
| Published | 28 April 2026 |
| Last updated at source | 28 April 2026 |
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