基于阈值聚类和KNN分类的入侵检测  

New Intrusion Detection Based on Threshold Clustering and KNN Algorithm

作　　者：谭三[1] 刘宁[1] 

机构地区：[1]西华大学数学与计算机学院,四川成都610039

出　　处：《郑州大学学报（理学版）》2010年第1期86-88,共3页Journal of Zhengzhou University:Natural Science Edition

摘　　要：利用基于阈值聚类算法首先对带类标记的样本数据集进行有指导性聚类,其主要目的是压缩训练数据集,解决KNN分类算法的样本选择问题以及孤立点的发现,用少量的更具代表性的聚类中心替代KNN算法中巨大的样本集,然后利用聚类密度改进KNN分类算法,从而提高KNN分类检测的准确度和速度.A guidance clustering of labeled empirical data is studied using threshold-based clustering. The main target of clustering is to compress huge training dataset to the limited number of clustering centroids, to solve the problem of sample selection in KNN classification algorithm, and discover isolated point. A smaller number of more representative clustering centroids are used in place of the original huge sample dataset of KNN algorithm. Then, the clustering density is used to revise KNN classification algorithm to improve the detection accuracy and detection speed.

关 键 词：网络安全 入侵检测 KNN算法 数据挖掘 

分 类 号：TP309[自动化与计算机技术—计算机系统结构]