基于粒子群优化的异常入侵检测算法的研究

提出了一种基于粒子群优化的异常入侵检测算法．首先，对基于动态聚类分析的异常入侵检测系统进行了建模和关键模块分析，对聚类算法区别正常和异常数据记录的过程，进行了详细的介绍，然后针对基本PSO算法存在的局部早熟收敛问题，利用改进的粒子属性进行了算法改进，增加了粒子多样性．通过初始化种群、更新速度、更新位置、计算每个粒子的适应度值、更新pgd、循环迭代，得到最优解．最后，利用该算法对基于聚类的入侵检测系统进行实验，结果显示该算法明显提升了入侵检测系统的正确率．

A Study of Anomaly Intrusion Detection Algorithm Based on Particle Swarm Optimization

This paper presents a particle swarm optimization based anomaly intrusion detection algorithm. First, cluster analysis based on dynamic anomaly intrusion detection system modeling and analysis of key modules of the difference between normal and abnormal clustering algorithm data recording process, carried out a detailed introduction, and then the basic PSO algorithm for the existence of localized premature convergence problems, the use of improved particle properties of the algorithm improved by increasing the particle diversity. By initializing the population, the speed, the update location, calculate the fitness value of each particle, update the Pgd, iteration, and finally get the optimal solution. Finally, the algorithm of intrusion detection system based on clustering experimental results shows that the algorithm significantly improves the correct rate of intrusion detection systems.