基于聚类支持向量机的入侵检测算法  

An Intrusion Detection Algorithm Based on Clustering Support Vector Machine

作　　者：雷红艳[1] 邹汉斌[1] 周慧灿[1] 

机构地区：[1]湖南文理学院计算机科学与技术学院,湖南常德415000

出　　处：《无线电工程》2009年第2期45-47,56,共4页Radio Engineering

基　　金：湖南省教育厅资助科研项目(06C585)

摘　　要：针对支持向量机应用到入侵检测中训练时间长的特点,提出了一种基于聚类的支持向量机的入侵检测算法。该方法可以对训练数据进行剪枝,以靠近判别边界的聚类中心集合作为有效的训练样本集合对支持向量机进行训练,减少了样本的训练时间,提高了算法的效率。实验结果表明该方法对入侵检测是有效的。On the basis of the long training time of support vector machines applied in intrusion detection, the paper proposes an intrusion detection algorithm based on clustering support vector machine. This method can realize training data pruning and train support vector machine by using the cluster center set close to the distinguishing boundary as the effective training sample set, which may reduce the sample training time and improve the algorithm efficiency. The experimental result indicates that this method is effective for intrusion detection.

关 键 词：聚类 支持向量机 入侵检测 异常检测 

分 类 号：TP309.2[自动化与计算机技术—计算机系统结构]