Anomaly Intrusion Detection Method Based on K-Means Clustering Algorithm with Particle Swarm Optimization

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.