融合自控粒子群和免疫进化的入侵数据分类

Intrusion data classification combining self-control particle swarm and immune evolutionary

在对基于异常的入侵检测进行训练时,缺少一个实时有效的训练集,提出了一种融合自控粒子群和免疫进化的入侵数据分类方法,对网络数据进行聚类分析,生成可靠的训练数据。粒子群模糊C均值聚类算法需要提前确定聚类数目,这在网络数据分析处理中是很难把握的,引入自控粒子群的方法根据迭代演算情况自动调节不同聚类数目的粒子群规模,使数据最后聚合在一个数目最优的聚类集中,同时为了克服陷入局部最优的问题,引入免疫进化机制,使部分粒子在当前最优指导下进行合理变异和替换,跳出局部最优解。

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 clus- tering. 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.