摘要
入侵检测系统已经成为网络安全技术的重要组成部分,然而传统的异常入侵检测技术需要通过对大量训练样本的学习,才能达到较高的检测精度,而大量训练样本集的获取在现实网络环境中是比较困难的。文章研究在网络入侵检测中,采用基于支持向量机(SVM)的主动学习算法,解决训练样本获取代价过大带来的问题。文中通过基于SVM的主动学习算法与传统的被动学习算法的对比实验,显示出主动学习算法与传统的学习算法相比,能有效地减少学习样本,极大地提高入侵检测系统的分类性能。
Intrusion Detection System（IDS） has become an important part of network security,the traditional abnormal detection are heavily dependent on the large training data set to get high accuracy rate,but to obtain sufficient training data is difficult in real network environment.In order to solve the problem of the excessive expense caused by obtaining the training data set,in this paper,we use the Support Vector Machine（SVM） active learning algorithm in network intrusion detection.Compared with the traditional self-learning algorithm,our experiment shows,active learning algorithm can immensely reduce the number of the training date and improve the performance of classifier in intrusion detection system.
出处
《计算机工程与应用》
CSCD
北大核心
2006年第1期117-119,211,共4页
Computer Engineering and Applications
基金
国家自然科学基金资助项目(编号:60403032)
湖南省教育厅青年资助项目(编号:03B009)
关键词
入侵检测
支持向量机
主动学习
intrusion detection,support vector machine,active learning
