An Improved K-Means Using in Anomaly Detection

被引：6

作者：

Yin, Chunyong ^{[1
]}

Zhang, Sun ^{[1
]}

Wang, Jin ^{[2
]}

Kim, Jeong-Uk ^{[3
]}

机构：

[1] Nanjing Univ Informat Sci & Technol, Jiangsu Key Lab Meteorol Observat & Informat Proc, Jiangsu Engn Ctr Network Monitoring, Sch Comp & Software, Nanjing, Jiangsu, Peoples R China

[2] Nanjing Univ Informat Sci & Technol, Sch Comp & Software, Nanjing, Jiangsu, Peoples R China

[3] Sangmyung Univ, Dept Energy Grid, Seoul, South Korea

来源：

2015 FIRST INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE THEORY, SYSTEMS AND APPLICATIONS (CCITSA 2015) | 2015年

基金：

中国国家自然科学基金;

关键词：

anomaly detection; cluster analysis; K-means; information entropy; DD algorithm;

D O I：

10.1109/CCITSA.2015.11

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Anomaly detection, as a part of network security, is an important question, which has attracted much attention. The characteristics of data mining make it suitable for anomaly detection. Cluster analysis is a kind of data mining technology and it can divide records into different clusters, which is convenient for anomaly detection. Traditional K-manes is affected by the selection of initial centers, the number of clusters and isolated points. We combine information entropy and DD algorithm to improve K-means and use KDD CUP99 data set to analysis the performance. From twice experiences, we find that improved K-means has higher detection rate and lower false positive rate than traditional K-means.

引用

页码：129 / 131

页数：3

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