【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。本文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。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说明,主动学习算法能有效地减少学习样本数及训练时间,能有效地提高入侵检测系统的分类性能。更多还原

【Abstract】 The Intrusion Detection System(IDS) has become an important part of network security.However, the traditional abnormal detection is heavily dependent on the large training samples to get high detection precision,but it is difficult to obtain sufficient training data in a real network environment. In order to solve the problem of excessive expense caused by obtaining the training samples,we use the SVM(Support Vector Machine)active learning algorithm in network intrusion detection. Compared with the traditional self-learning algorithm, our experiment shows that the active learning algorithm can immensely reduce the number of training samples and the training time,thus efficiently improving the performance of classification in IDSs.更多还原