Network security situation automatic prediction model based on accumulative CMA-ES optimization  

作　　者：Wang Jian Li Ke Zhao Guosheng 

机构地区：[1]School of Computer Science and Technology,Harbin University of Science and Technology [2]School of Computer Science and Information Engineering,Harbin Normal University

出　　处：《The Journal of China Universities of Posts and Telecommunications》2017年第3期33-43,共11页中国邮电高校学报（英文版）

基　　金：supported by the National Natural Science Foundation of China (61403109,61202458);the Specialized Research Fund for the Doctoral Program of Higher Education of China (20112303120007);the Specialized Research Fund for Scientific and Technological Innovation Talents of Harbin (2016RAQXJ036)

摘　　要：To improve the accuracy of the network security situation, a security situation automatic prediction model based on accumulative data preprocess and support vector machine （SVM） optimized by covariance matrix adaptive evolutionary strategy （CMA-ES） is proposed. The proposed model adopts SVM which has strong nonlinear ability. Also, the hyper parameters for SVM are optimized through the CMA-ES which owns good performance in finding optimization automatically. Considering the irregularity of network security situation values, we accumulate the original sequence, so that the internal rules of discrete data can be revealed and it is easy to model. Simulation experiments show that the proposed model has faster convergence-speed and higher prediction accuracy than other extant prediction models.To improve the accuracy of the network security situation, a security situation automatic prediction model based on accumulative data preprocess and support vector machine （SVM） optimized by covariance matrix adaptive evolutionary strategy （CMA-ES） is proposed. The proposed model adopts SVM which has strong nonlinear ability. Also, the hyper parameters for SVM are optimized through the CMA-ES which owns good performance in finding optimization automatically. Considering the irregularity of network security situation values, we accumulate the original sequence, so that the internal rules of discrete data can be revealed and it is easy to model. Simulation experiments show that the proposed model has faster convergence-speed and higher prediction accuracy than other extant prediction models.

关 键 词：security situation automatic prediction covariance matrix adaptive evolution strategy support vector machine 

分 类 号：TP393.08[自动化与计算机技术—计算机应用技术] O211.67[自动化与计算机技术—计算机科学与技术]