工业信息物理系统攻击检测增强模型

Attack Detection Enhancement Model of Industrial Cyber Physical Systems

  • 摘要: 针对工业信息物理系统数据不平衡,从而使得基于深度学习的攻击检测方法对稀有攻击检测率低的问题,提出了一种工业信息物理系统攻击检测增强模型.该模型从原始数据集中选取出稀有攻击样本,使用梯度惩罚Wasserstein距离生成式对抗网络进行稀有攻击样本扩充,并将扩充得到的样本与原始数据集混合,形成新的数据集用于训练多层感知机,实现攻击检测.为检验模型性能,使用以Modbus作为通信协议的真实数据集进行测试.实验结果表明,与广泛采用的数据增强方法相比,攻击检测增强模型能显著改善攻击检测的能力.

     

    Abstract: The data of industrial cyber physical systems (ICPS) are imbalanced. As such, the detection rate of rare attacks based on deep learning is low. In this study, an enhanced attack detection model for industrial CPS is proposed to address this problem. The model selects rare attack samples from the original data set. It then uses the gradient penalty Wasserstein distance generative adversarial network to expand the rare attack samples. Afterward, it mixes the expanded samples with the original data set to form a new data set for training multilayer perception and consequently detect an attack. A real data set with Modbus as the communication protocol is used for testing to verify the performance of the model. Experimental results show that the proposed model can significantly improve the ability to detect an attack compared with that of widely used data augmentation methods.

     

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