Computer Engineering and Applications ›› 2014, Vol. 50 ›› Issue (12): 91-95.
Previous Articles Next Articles
CHENG Ke, SONG Haisheng, AN Zhanfu, KONG Yongsheng
Online:
Published:
成 科,宋海声,安占福,孔永胜
Abstract: 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.
Key words: network security risk, t-Support Vector machine for Regression(SVR) assessment model, Genetic Algorithm(GA), optimal parameters combination
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。
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
CHENG Ke, SONG Haisheng, AN Zhanfu, KONG Yongsheng. Method for evaluation of network security risk based on t-SVR with parameters optimization by GA[J]. Computer Engineering and Applications, 2014, 50(12): 91-95.
成 科,宋海声,安占福,孔永胜. 基于GA参数优化的t-SVR网络安全风险评估方法[J]. 计算机工程与应用, 2014, 50(12): 91-95.
0 / Recommend
Add to citation manager EndNote|Ris|BibTeX
URL: http://cea.ceaj.org/EN/
http://cea.ceaj.org/EN/Y2014/V50/I12/91