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 adapti...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.展开更多
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)
文摘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.
