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.

中文翻译：

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基于连体神经网络的少样本学习，用于工业信息物理系统中的异常检测


随着工业4.0的普及，人工智能和智能技术都得到了应用，并成为工业信息物理系统（CPS）的热门话题。用于识别信息物理攻击以保证工作效率和安全的智能异常检测仍然是一个具有挑战性的问题，特别是在处理少量标记数据进行信息物理安全保护时。在本文中，我们提出了一种采用连体卷积神经网络（FSL-SCNN）的小样本学习模型，以缓解过拟合问题并提高工业 CPS 中智能异常检测的准确性。构建 Siamese CNN 编码网络，根据输入样本的优化特征表示来测量输入样本的距离。然后提出了包括三个特定损失的鲁棒成本函数设计，以提高训练过程的效率。最终开发出智能异常检测算法。基于完全标记的公共数据集和一些标记数据集的实验结果表明，我们提出的 FSL-SCNN 在检测工业 CPS 安全防护的入侵信号时可以显着提高误报率（FAR）和 F1 分数。