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基于聚类支持向量机的入侵检测算法
引用本文:雷红艳,邹汉斌,周慧灿.基于聚类支持向量机的入侵检测算法[J].无线电工程,2009,39(2):45-47.
作者姓名:雷红艳  邹汉斌  周慧灿
作者单位:湖南文理学院,计算机科学与技术学院,湖南,常德,415000
基金项目:湖南省教育厅资助项目 
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。

关 键 词:聚类  支持向量机  入侵检测  异常检测

An Intrusion Detection Algorithm Based on Clustering Support Vector Machine
LEI Hong-yan,ZOU Han-bin,ZHOU Hui-can.An Intrusion Detection Algorithm Based on Clustering Support Vector Machine[J].Radio Engineering of China,2009,39(2):45-47.
Authors:LEI Hong-yan  ZOU Han-bin  ZHOU Hui-can
Affiliation:( School of Computer Science and Technology, Hunan University of Art and Science, Changde Hu' nan 415000, China)
Abstract: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.
Keywords:clustering  support vector machine  intrusion detection  anomaly detection
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