K-means algorithm based on particle swarm optimization algorithm for anomaly intrusion detection

被引：0

作者：

Xiao, Lizhong ^{[1
]}

Shao, Zhiqing ^{[1
]}

Liu, Gang ^{[1
]}

机构：

[1] East China Univ Sci & Technol, Coll Informat Sci & Engn, Shanghai 200237, Peoples R China

来源：

WCICA 2006: SIXTH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION, VOLS 1-12, CONFERENCE PROCEEDINGS | 2006年

关键词：

PSO; K-means algorithm; global optimization; intrusion detection;

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：

K-means as a clustering algorithm has been studied in intrusion detection. However, with the deficiency of global search ability it is not satisfactory. Particle swarm optimization (PSO) is one of the evolutionary computation techniques based on swarm intelligence, which has high global search ability. So K-means algorithm based on PSO (PSO-KM) is proposed in this paper. Experiment over network connection records from KDD CUP 1999 data set was implemented to evaluate the proposed method. A Bayesian classifier was trained to select some fields in the data set. The experimental results clearly showed the outstanding performance of the proposed method.

引用

页码：5854 / +

页数：2

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