CAGCN:Centrality-Aware Graph Convolution Network for Anomaly Detection in Industrial Control Systems
作者机构：National Network New Media Engineering Research CenterInstitute of AcousticsChinese Academy of SciencesBeijing 100190China School of ElectronicElectrical and Communication EngineeringUniversity of Chinese Academy of SciencesBeijing 100049China
出 版 物：《Journal of Computer Science & Technology》 (计算机科学技术学报（英文版）)
年 卷 期：2024年第39卷第4期
页 面：967-983页
核心收录：
学科分类：07[理学] 0815[工学-水利工程] 0811[工学-控制科学与工程] 0701[理学-数学] 070101[理学-基础数学]
主 题：graph convolution network(GCN) data mining network centrality anomaly detection industrial control system
摘 要：In industrial control systems,the utilization of deep learning based methods achieves improvements for anomaly ***,most current methods ignore the association of inner components in industrial control *** industrial control systems,an anomaly component may affect the neighboring components;therefore,the connective relationship can help us to detect anomalies *** this paper,we propose a centrality-aware graph convolution network(CAGCN)for anomaly detection in industrial control *** the traditional graph convolution network(GCN)model,we utilize the concept of centrality to enhance the ability of graph convolution networks to deal with the inner relationship in industrial control *** experiments show that compared with GCN,our CAGCN has a better ability to utilize this relationship between components in industrial control *** performances of the model are evaluated on the Secure Water Treatment(SWaT)dataset and the Water Distribution(WADI)dataset,the two most common industrial control systems datasets in the field of industrial anomaly *** experimental results show that our CAGCN achieves better results on precision,recall,and F1 score than the state-of-the-art methods.
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