Intrusion Data Analysis Based on Self-control Particle Swarm Optimization
Jiaomin Liu · Jisuanji gongcheng · 2012
Intrusion detection methods based on abnormity need a normal data set to establish the normal behavior mode,but there is not an effective method to get the data set.The number of clustering needs to be initialized in Particle Swarm Optimization(PSO) fuzzy clustering algorithm,and this number is confirmed according to experience,so its accuracy is lower.To solve these problems,this paper proposes a self-control PSO fuzzy clustering algorithm,getting the training sets from the network data.It sets the particle swarm with different number of clustering and the number can be adjusted by control-vector according to the validity function.And the data can be converged into an appropriate number of clustering.Experimental results show that the method can improve the veracity of clustering,and reliable training sets can be got from the network data.