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A Hybrid Security Situation Prediction Model for Information Network Based on Support Vector Machine and Particle Swarm Optimization

GAO Kunlun1,Liu Jianming2,XU Ruzhi3,WANG Yufei3,LI Yikang3(1.Information & Communication Department of China Electric Power Research Institute,Hardian District,Beijing 100192,China; 2.State Grid Information & Telecommunication Company Limited,Xuanwu District,Beijing 100761,China; 3.School of Control and Computer Engineering,North China Electric Power University,Changping District,Beijing 102206,China)  
A security situation prediction model for information network based on support vector machine(SVM) and particle swarm optimization(PSO) is proposed.By use of sliding window,in the proposed model a continuous time series that is partially linearly dependent is constructed by security situation values sampled from original discrete time monitoring points,and taking the time series as the sample set of security situation data the SVM is trained to generate a prediction model.During the training of SVM,the PSO algorithm is used to search for the optimal training parameters of SVM to reduce the blindness in the selection of SVM parameters and improve precision of prediction.Through the experiments based on on-site installation and monitoring data of a lot of power enterprise information networks,the effectiveness of the proposed security situation prediction model is verified.
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