Cross-Domain Generalization Failure in Lightweight Intrusion Detection Models for IIoT Networks
arXiv:2607.00553v1 Announce Type: cross Abstract: 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 ac
The proliferation of IIoT devices and the drive for edge AI deployment necessitate robust and lightweight intrusion detection, making cross-domain generalization failures an immediate concern for system security and reliability.
This study highlights a critical vulnerability in current AI security models for industrial systems, indicating that models effective in controlled environments may fail catastrophically in real-world, diverse deployments, posing significant operational and safety risks.
The understanding that current lightweight AI models for IIoT security lack inherent generalization, requiring more sophisticated adaptation methods or diverse training datasets for reliable deployment in heterogeneous industrial environments.
- · AI robustness and generalization researchers
- · Cybersecurity solution providers specializing in adaptive AI
- · Industrial control system manufacturers incorporating advanced security
- · IIoT operators relying on simplistic AI security deployments
- · Developers of non-generalizable lightweight AI models
- · Sectors with diverse IIoT network architectures
Increased focus on federated learning or domain adaptation techniques for IIoT security models to ensure cross-network performance.
Potential for new regulations or certification standards specifically addressing the generalization capabilities of AI/ML-based IIoT security solutions.
A shift in industrial security investment towards 'security by design' principles that account for structural network diversity rather than post-hoc AI patching.
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Read at arXiv cs.AI