粒子群模糊聚类算法在入侵检测中的研究

摘要

Fuzzy C-means clustering algorithm is widely used in intrusion detection currently.But this algorithm has some shortcomings that is difficult to determine the clustering number and easy to fall into the local minimum when iterating,which can affect the accuracy of intrusion detection system.In view of this,propose a fuzzy clustering algorithm based on PSO algorithm,through introducing the PSO global search ability and particle inverting operation,avoid the problem of falling into local minimum and premature convergence.Final-ly,the experimental results show that the new algorithm has higher detection rate than the C-mean clustering algorithm,which can be well applied to intrusion detection systems.%目前模糊C均值聚类算法广泛应用于入侵检测算法中，但是存在聚类数目难以确定，目标函数的局部极小点使得算法容易陷入局部最优的现象，影响入侵检测的准确率。鉴于此，文中提出一种基于粒子群算法的模糊聚类算法，引入PSO全局搜索能力和粒子翻转变异操作，避免传统C均值聚类算法对孤立点敏感，容易陷入局部最优，过早收敛的问题。最后通过实验结果表明，新算法检测率明显优于C均值聚类算法，能很好地应用于目前入侵检测系统之中。