摘要
研究网络安全的预测问题,面对海量恶意攻击,信息要及时告警,进行安全管理。针对当前预测模型只能对过去和现在网络安全态势进行分析,不能对将来网络安全态势进行预测的缺陷,为了提高预测精度,提出了支持向量机的网络安全态势预测方法。支持向量机可以利用过去和当前的网络安全态势值,对将来网络安全状态进行预测,同时采用遗传算法对支持向量机参数优化,加快网络安全态势预测速度。通过仿真对预测方法性能进行检验,结果表明,预测方法能够准确反映网络安全的整体变化趋势,提高了网络安全态势的预测精度,相对于传统预测方法,更适用于现实的网络环境中。
Traditional prediction models can only analyze past and present network security situations, and can not predict future network security situation. This paper proposed a network security situation prediction model of support vector machine. The support vector machine used the past and current situation of the network security to predict the future value of network security, the parameters were optimized by genetic algorithm, and the prediction speed was increased. Prediction model was tested by simulation experiments, and the results show that the prediction mdoel can accurately reflect the changing trends of network security, improve the prediction precision of network security situation compared with the traditional prediction method, and is more suitable real network environment.
出处
《计算机仿真》
CSCD
北大核心
2012年第2期98-101,共4页
Computer Simulation
关键词
网络安全态势
支持向量机
遗传算法
预测
Network security situation
Support vector machine （SVM）
GA
Prediction
