Few-shot Detection of Anomalies in Industrial Cyber-Physical System via Prototypical Network and Contrastive Learning. (arXiv:2302.10601v1 [cs.CR])

The rapid development of Industry 4.0 has amplified the scope and
destructiveness of industrial Cyber-Physical System (CPS) by network attacks.
Anomaly detection techniques are employed to identify these attacks and
guarantee the normal operation of industrial CPS. However, it is still a
challenging problem to cope with scenarios with few labeled samples. In this
paper, we propose a few-shot anomaly detection model (FSL-PN) based on
prototypical network and contrastive learning for identifying anomalies with
limited labeled data from industrial CPS. …

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