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

作　　者：邓志刚 孙子文[1,2] DENG Zhigang;SUN Ziwen(School of Internet of Things,Jiangnan University,Wuxi 214122,China;Engineering Research Center of Internet of Things Technology Applications Ministry of Education,Wuxi 214122,China)

机构地区：[1]江南大学物联网工程学院,江苏无锡214122 [2]物联网技术应用教育部工程研究中心,江苏无锡214122

出　　处：《信息与控制》2021年第4期410-418,共9页Information and Control

基　　金：国家自然科学基金资助项目(61373126);中央高校基本科研业务费专项资金资助项目(JUSRP51510);江苏省自然科学基金资助项目(BK20131107)。

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.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.

关 键 词：工业信息物理系统 攻击检测 生成式对抗网络 多层感知机 数据增强 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]