文献类型：期刊文献

中文题名：基于改进CGAN算法的工控系统入侵检测方法

英文题名：Intrusion Detection Method of ICS Based on Improved CGAN Algorithm

作者：王华忠[1];田子蕾[1]

机构：[1]华东理工大学能源化工过程智能制造教育部重点实验室,上海200237

年份：2023

期号：1

起止页码：36

中文期刊名：信息网络安全

外文期刊名：Netinfo Security

收录：CSTPCD;;北大核心:【北大核心2020】；CSCD:【CSCD_E2023_2024】；

基金：国家自然科学基金[61973119];中央高校基本科研业务费专项资金[222201917006]。

语种：中文

中文关键词：工控系统;入侵检测;WCGAN;不平衡数据;支持向量机

外文关键词：ICS;intrusion detection;WCGAN;unbalance data;SVM

摘要：文章提出一种改进的CGAN算法,利用Wasserstein距离衡量合成样本与真实样本之间的距离,解决了忽略CGAN中两类样本重叠导致生成器梯度消失的不稳定问题,并在具有不平衡率的UCI数据集上验证了算法的有效性。文章还构建了WCGAN-SVM工控系统入侵检测模型,并在工控数据集SWaT上进行验证。实验结果表明,与SVM相比,该方法检测攻击样本的准确率提高了3.51%,漏报率和误报率分别降低2.29%和2.19%。
In this paper,an improved conditional generative adversarial network algorithm was proposed,and the Wasserstein distance was added to measure the distance between synthetic and real samples,for solving the instability problem that cause the generator gradient to disappear when two types of samples were ignored to overlap in CGAN.The effectiveness of the algorithm was verified on the UCI dataset with different imbalance rates.Then the WCGAN-SVM intrusion detection model of industrial control system was constructed and verified on the industrial control dataset SWaT.The experimental results show that the method increases the accuracy of detecting attack samples by 3.51%and decreases the false alarm rate and the false negative rate by 2.29%and 2.19%compared with SVM.