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

arXiv: https://arxiv.org/abs/2302.10601 · HTML: https://mlsanity.com/paper/2302.10601/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 30 references that are in the map.
- First posted on arXiv 21 Feb 2023.

## Builds on (most cited first, 10 of 10)

- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [Learning deep representations by mutual information estimation and maximization](https://mlsanity.com/paper/1808.06670/) (2018)
- [Data-Efficient Image Recognition with Contrastive Predictive Coding](https://mlsanity.com/paper/1905.09272/) (2019)
- [Generalizing from a Few Examples: A Survey on Few-Shot Learning](https://mlsanity.com/paper/1904.05046/) (2019)
- [DetCo: Unsupervised Contrastive Learning for Object Detection](https://mlsanity.com/paper/2102.04803/) (2021)
- [Driver Anomaly Detection: A Dataset and Contrastive Learning Approach](https://mlsanity.com/paper/2009.14660/) (2020)
- [Detecting Cyberattacks in Industrial Control Systems Using Convolutional Neural Networks](https://mlsanity.com/paper/1806.08110/) (2018)
- [Masked Contrastive Learning for Anomaly Detection](https://mlsanity.com/paper/2105.08793/) (2021)
- [Cyber-Physical Systems Security -- A Survey](https://mlsanity.com/paper/1701.04525/) (2017)

## Abstract (arXiv)

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.