摘要
Abstract
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Key words
SCADA system/voting classification model/power system/cyber attack/machine learning/intrusion detection分类
信息技术与安全科学
