Application research of improved K-means algorithm in intrusion detection

被引：0
|
作者：
Liu Xiaoguo [1 ]
Tian Jing [2 ]
机构：
[1] Jilin Agr Univ Jilin, Changchun 130118, Peoples R China
[2] Changchun Vocat Inst Technol, Changchun 130033, Jilin, Peoples R China
来源：
PROCEEDINGS OF THE 2015 INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE AND ENGINEERING | 2015年 / 17卷
关键词：
Intrusion detection; Data mining; Clustering analysis; K-means clustering; Minimum spanning tree;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
An improved K-means clustering algorithm is put forward on basis of the split-merge method for the purpose of remedying defects both in determination of value in K and in selection of initial cluster centre of traditional K-means clustering. At first, the concept of independence degree of date was incorporated into the experimental date subset construction theory, using independence degree to evaluate the importance of nature. Next, the database is merged into several classes in respect of density of date points, the combination of the minimum spanning tree algorithm and traditional K-means clustering algorithm is conducive to the achievement of splitting. Eventually, the KDD Cup99 database is applied to conduct simulation experiment on the application of the improved algorithm in intrusion detection. The results indicate that the improved algorithm prevails over traditional K-means algorithm in detection rate and false alarm rate.
引用
收藏
页码：96 / 100
页数：5
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