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
收藏
页码：129 / 131
页数：3
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