Title :
K-means Algorithm Based on Particle Swarm Optimization Algorithm for Anomaly Intrusion Detection
Author :
Xiao, Lizhong ; Shao, Zhiqing ; Liu, Gang
Author_Institution :
Coll. of Inf. Sci. & Eng., East China Univ. of Sci. & Technol., Shanghai
Abstract :
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
Keywords :
belief networks; evolutionary computation; particle swarm optimisation; pattern clustering; security of data; Bayesian classifier; K-means algorithm; anomaly intrusion detection; clustering algorithm; evolutionary computation; global optimization; global search ability; network connection records; particle swarm optimization; swarm intelligence; Acceleration; Bayesian methods; Birds; Clustering algorithms; Educational institutions; Evolutionary computation; Genetics; Information science; Intrusion detection; Particle swarm optimization; K-means algorithm; PSO; global optimization; intrusion detection;
Conference_Titel :
Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on
Conference_Location :
Dalian
Print_ISBN :
1-4244-0332-4
DOI :
10.1109/WCICA.2006.1714200
