关键词: 粒子群优化, 模糊聚类, 入侵检测, 免疫

Abstract: The intrusion detection based on abnormity needs real-time and effective sets, a kind of intrusion data classification algorithm combining self-control particle swarm and immune evolutionary is proposed to cluster the network data and get the reliable training sets. The number of clustering needs to be confirmed before the Particle Swarm Optimization（PSO） and Fuzzy C-Means clustering（FCM） algorithm and this is difficult for the network data. The proportion of the particle swarm with different number of clustering is adjusted according to the information from the working and it can make the sets to an optimization clustering. The immune evolutionary is imposed to solve the problem that the PSO-FCM is easier to fall into local optimization and this can make the particle to mutate and displace under surveillance.

Key words: particle swarm optimization, fuzzy clustering, intrusion detection, immune