DocumentCode :
2781619
Title :
Anomaly Intrusion Detection Method Based on K-Means Clustering Algorithm with Particle Swarm Optimization
Author :
Li, Zhengjie ; Li, Yongzhong ; Xu, Lei
Author_Institution :
Sch. of Comput. Sci. & Eng., Jiangsu Univ. of Sci. & Technol., Zhenjiang, China
Volume :
2
fYear :
2011
fDate :
24-25 Sept. 2011
Firstpage :
157
Lastpage :
161
Abstract :
K-means clustering algorithm is an effective method that has been proved for apply to the intrusion detection system. Particle swarm optimization (PSO) algorithm which is evolutionary computation technology based on swarm intelligence has good global search ability. With the deficiency of global search ability for K-means clustering algorithm, we propose a K-means clustering algorithm based on particle swarm optimization (PSO-KM) in this paper. The proposed algorithm has overcome falling into local minima and has relatively good overall convergence. Experiments on data sets KDD CUP 99 has shown the effectiveness of the proposed method and also shows the method has higher detection rate and lower false detection rate.
Keywords :
evolutionary computation; particle swarm optimisation; pattern clustering; search problems; security of data; KDD CUP 99 data sets; anomaly intrusion detection method; evolutionary computation technology; global search ability; k-mean clustering algorithm; particle swarm optimization algorithm; swarm intelligence; Algorithm design and analysis; Clustering algorithms; Convergence; Educational institutions; Intrusion detection; Optimization; Particle swarm optimization; IDS; K-means clustering; PSO;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information Technology, Computer Engineering and Management Sciences (ICM), 2011 International Conference on
Conference_Location :
Nanjing, Jiangsu
Print_ISBN :
978-1-4577-1419-1
Type :
conf
DOI :
10.1109/ICM.2011.184
Filename :
6113492
Link To Document :
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