基于深度卷积网络及K均值的工控系统入侵检测研究
Research on Intrusion Detection of Industrial Control System Based on Deep Convolution Network and K-Means
DOI: 10.12677/CSA.2020.1011225, PDF,    国家自然科学基金支持
作者: 徐 峰, 张玉贤：衢州职业技术学院，浙江 衢州
关键词: 工控系统；卷积网络；网络攻击；Industrial Control Systems； Convolution Network； Network Attack
摘要: 随着物联网与智能制造的兴起，工业控制系统的信息安全问题亦日渐受到重视，尤其是公共安全控制要地受到网络攻击后极易导致城市生活网络的瘫痪。为避免严重的网络攻击灾害，本项目对入侵IDS进行深入研究，提出基于深度卷积网络及K均值的工控系统入侵检测方法。实验结果显示，在衢州某水库数据集上，本方法在效能指标上优于其它方法。
Abstract: With the rise of the Internet of things and intelligent manufacturing, the information security of industrial control system has been paid more and more attention, especially when the public security control points are attacked by network. It is easy to lead to the paralysis of urban living network. In order to avoid serious network attacks disaster, this project makes an indepth study on intrusion IDS, and proposes an intrusion detection method of industrial control system based on deep convolution network and k-means. Experimental results show that this method is superior to other methods in most performance indicators on a data set of a reservoir in Quzhou.
文章引用：徐峰, 张玉贤. 基于深度卷积网络及K均值的工控系统入侵检测研究[J]. 计算机科学与应用, 2020, 10(11): 2141-2146. https://doi.org/10.12677/CSA.2020.1011225

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