| 基于阈值聚类和KNN分类的入侵检测 |
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| 引用本文： | 谭三,刘宁.基于阈值聚类和KNN分类的入侵检测[J].郑州大学学报(理学版),2010,42(1). |
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| 作者姓名： | 谭三 刘宁 |
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| 作者单位： | 西华大学,数学与计算机学院,四川,成都,610039 |
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| 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.
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| 关 键 词： | 网络安全 入侵检测 KNN算法 数据挖掘 |
New Intrusion Detection Based on Threshold Clustering and KNN Algorithm |
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| TAN San
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LIU Ning.New Intrusion Detection Based on Threshold Clustering and KNN Algorithm[J].Journal of Zhengzhou University:Natural Science Edition,2010,42(1). |
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| Authors: | TAN San LIU Ning |
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| Institution: | TAN San,LIU Ning(School of Mathematical & Computer Science,Xihua University,Chengdu 610039,China) |
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| Abstract: | 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... |
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| Keywords: | network security intrusion detection KNN algorithm data mining |
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