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.
出处
《计算机工程与应用》
CSCD
2014年第12期91-95,共5页
Computer Engineering and Applications
基金
甘肃省自然科学基金(No.1208RJZA191)
关键词
网络安全风险
t-支持向量回归机(SVR)评估模型
遗传算法
参数组合寻优
network security risk
t-Support Vector machine for Regression（SVR） assessment model
Genetic Algorithm（GA）
optimal parameters combination
