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首页> 外文期刊>IEEE transactions on industrial informatics >Siamese Neural Network Based Few-Shot Learning for Anomaly Detection in Industrial Cyber-Physical Systems
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Siamese Neural Network Based Few-Shot Learning for Anomaly Detection in Industrial Cyber-Physical Systems

机译：基于暹罗神经网络的工业网络 - 物理系统异常检测的少量学习

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摘要

With the increasing population of Industry 4.0, both AI and smart techniques have been applied and become hotly discussed topics in industrial cyber-physical systems (CPS). Intelligent anomaly detection for identifying cyber-physical attacks to guarantee the work efficiency and safety is still a challenging issue, especially when dealing with few labeled data for cyber-physical security protection. In this article, we propose a few-shot learning model with Siamese convolutional neural network (FSL-SCNN), to alleviate the over-fitting issue and enhance the accuracy for intelligent anomaly detection in industrial CPS. A Siamese CNN encoding network is constructed to measure distances of input samples based on their optimized feature representations. A robust cost function design including three specific losses is then proposed to enhance the efficiency of training process. An intelligent anomaly detection algorithm is developed finally. Experiment results based on a fully labeled public dataset and a few labeled dataset demonstrate that our proposed FSL-SCNN can significantly improve false alarm rate (FAR) and F1 scores when detecting intrusion signals for industrial CPS security protection.
机译：随着工业4.0的人口不断增加，人工智能和智能技术都已经应用并成为工业网络物理系统（CPS）热烈讨论的话题。智能异常检测识别网络物理攻击，以保证工作效率和安全性仍然是一个具有挑战性的问题，特别是与网络物理安全保护少数的标签数据的时候。在这篇文章中，我们提出了以连体卷积神经网络（FSL-SCNN）几拍的学习模式，以缓解过度拟合的问题，加强对工业CPS智能异常检测的准确性。甲连体CNN编码网络被构造成测量基于它们优化的特征表示输入样本的距离。一个强大的成本函数的设计包括三个具体损失，然后提出了加强培训过程的效率。智能异常检测算法，终于研制成功。基于充满标记公共数据集和一些标记数据集实验结果表明，工业CPS安全保护检测入侵信号时，我们提出的FSL-SCNN可以显著提高误报率（FAR）和F1分数。

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