基于GA参数优化的t-SVR网络安全风险评估方法

摘要

为了提高网络安全风险评估的准确性和实时性，提出一种t时延参数优化支持向量回归机的网络安全风险评估模型（t-SVR）。利用遗传算法（GA）的全局搜索性，对t-SVR模型中的关键参数进行组合寻优。通过对网络安全风险数据集进行仿真，结果表明，基于GA参数优化的t-SVR评估模型已经解决了SVR存在的不足，提高了网络安全风险评估的准确率，缩短了评估时间，评估性能更加稳定。%In order to improve the accuracy and real-time of network security risk assessment, this paper proposes a model about network security risk assessment based on the Support Vector machine for Regression optimized by t time-delay parameter. It combines and optimizes the key parameters of t-SVR, making use of the global search performance of GA. The simulation result of network security risk data-set indicates that the assessment model of t-SVR evaluation based on GA parameter optimization has solved the shortage of SVR, and the risk assessment is made more accurate, the time more less and the performance more stable.