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The rapid development of Industry 4.0 has amplified the scope anddestructiveness of industrial Cyber-Physical System (CPS) by network attacks.Anomaly detection techniques are employed to identify these attacks andguarantee the normal operation of industrial CPS. However, it is still achallenging problem to cope with scenarios with few labeled samples. In thispaper, we propose a few-shot anomaly detection model (FSL-PN) based onprototypical network and contrastive learning for identifying anomalies withlimited labeled data from industrial CPS. Specifically, we design a contrastiveloss to assist the training process of the feature extractor and learn morefine-grained features to improve the discriminative performance. Subsequently,to tackle the overfitting issue during classifying, we construct a robust costfunction with a specific regularizer to enhance the generalization capability.Experimental results based on two public imbalanced datasets with few-shotsettings show that the FSL-PN model can significantly improve F1 score andreduce false alarm rate (FAR) for identifying anomalous signals to guaranteethe security of industrial CPS.

Few-shot Detection of Anomalies in Industrial Cyber-Physical System via Prototypical Network and Contrastive Learning