Hybrid Optimization of Support Vector Machine for Intrusion Detection，成果详细信息-中国科技论文在线

郁松年

44浏览
0点赞
0收藏
0分享
63下载
0评论
引用

期刊论文

Hybrid Optimization of Support Vector Machine for Intrusion Detection

郁松年， XI Fu-li ， YU Song-nian ， HAO Wei

Journal of Donghua University(Eng. Ed.) Vol.22, No.3(2005):51-56，-0001，（）：

URL:

摘要/描述

Support vector machine (SVM) technique has recently become a research focus in intrusion detection field for its better generalization performance when given less priori knowledge than other soft-computing techniques. But the randomicity of parameter selection in its implement often prevents it achieving expected performance. By utilizing genetic algorithm (GA) to optimize the parameters in data preprocessing and the training model of SVM simultaneous-ly, a hybrid optimization algorithm is proposed in the paper to address this problem. The experimental results demonst rate that it's an effective method and can improve the performance of SVM-based intrusion detection system further.

关键词: intrusion detection system( ， IDS) ， , ， support vector machine( ， SVM) ， , ， genetic algorithm( ， GA) ， , ， system call trace, ， ξα-estimator, ， sequential minimal optimization

问答

暂无问题，成为第一个提问者

我要提问全部问题

【免责声明】以下全部内容由[郁松年]上传于[2009年07月21日 15时56分44秒]，版权归原创者所有。本文仅代表作者本人观点，与本网站无关。本网站对文中陈述、观点判断保持中立，不对所包含内容的准确性、可靠性或完整性提供任何明示或暗示的保证。请读者仅作参考，并请自行承担全部责任。

我要评论

全部评论 共 0 条

本学者其他成果

同领域成果