基于SCADA和投票分类模型的电力系统攻击检测技术  

Power system attack detection technology based on SCADA and voting classification model

作　　者：耿振兴 王勇 GENG Zhenxing;WANG Yong(School of Computer Science and Technology,Shanghai University of Electric Power,Shanghai 200120,China)

机构地区：[1]上海电力大学计算机科学与技术学院,上海200120

出　　处：《现代电子技术》2025年第4期18-23,共6页Modern Electronics Technique

摘　　要：为检测电力系统中的网络攻击行为,文中提出一种基于电力数据采集与监视控制(SCADA)系统的攻击检测方法,探讨了机器学习方法作为检测电力系统攻击的可行性,并评估了其性能,讨论了机器学习模型作为攻击检测方法的意义。此外,还提出一种基于机器学习的投票分类模型(RES),其由RF、ET和SVM三种基本分类器构成,使用投票分类中的软投票方法,并且考虑了基本分类器的权重对投票分类模型的影响。通过在密西西比州立大学和橡树岭国家实验室的电力系统攻击数据集上进行实验和分析,结果表明,与其他方法相比,RES模型在电力系统的攻击检测方面准确率得到大幅提升,在电力系统攻击数据集上的二分类准确率达到了98.40%,能够准确地检测电网中的网络攻击行为。In order to detect cyber-attack behaviors in power systems,a method of attack detection based on power SCADA(supervisory control and data acquisition)system is proposed,the feasibility of machine learning method for detecting power system attacks is discussed and its performance is evaluated,and the significance of machine learning model as an attack detection method is discussed.The machine learning based voting classification model(RES)is proposed,which is composed of three basic classifiers:random forest(RF),extra tree(ET),and support vector machine(SVM),the soft voting method in voting classification is adopted,and the influence of the weight of the basic classifier on the voting classification model is considered.Through experiments and analysis on the power system attack dataset from Mississippi State University and Oak Ridge National Laboratory,the results show that in comparison with other published methods,the RES model has substantially higher accuracy in attack detection in the power system,and the binary classification accuracy on the power system attack dataset can reach 98.40%,which is capable of accurately detecting cyber-attacks in the power grid.

关 键 词：SCADA系统 投票分类模型 电力系统 网络攻击 机器学习 入侵检测 

分 类 号：TN915.08-34[电子电信—通信与信息系统] TP391.4[电子电信—信息与通信工程] TP769[自动化与计算机技术—计算机应用技术]