Few-shot Detection of Anomalies in Industrial Cyber-Physical System via Prototypical Network and Contrastive Learning
This work addresses the challenge of few-shot anomaly detection for industrial cyber-physical systems, which is incremental as it builds on existing methods like prototypical networks and contrastive learning.
The paper tackles the problem of detecting anomalies in industrial cyber-physical systems with limited labeled data by proposing a few-shot anomaly detection model (FSL-PN) based on prototypical networks and contrastive learning, resulting in significant improvements in F1 score and reduced false alarm rates on two public datasets.
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. Specifically, we design a contrastive loss to assist the training process of the feature extractor and learn more fine-grained features to improve the discriminative performance. Subsequently, to tackle the overfitting issue during classifying, we construct a robust cost function with a specific regularizer to enhance the generalization capability. Experimental results based on two public imbalanced datasets with few-shot settings show that the FSL-PN model can significantly improve F1 score and reduce false alarm rate (FAR) for identifying anomalous signals to guarantee the security of industrial CPS.