IWO-Kohonen聚类算法在IDS中的应用  

Application of IWO-Kohonen Clustering Algorithm in Intrusion Detection System

作　　者：徐守坤[1] 王薇[1] 乐光学[1,2] 

机构地区：[1]常州大学信息科学与工程学院,江苏常州213164 [2]怀化学院计算机科学与技术系,湖南怀化418000

出　　处：《计算机工程》2014年第1期139-143,共5页Computer Engineering

基　　金：湖南省自然科学基金资助项目(07JJ6140;07JJ6109);湖南省科技计划基金资助项目(05FJ3018)

摘　　要：针对Kohonen神经网络模型网络入侵聚类正确率较低的问题,将入侵杂草优化(IWO)算法与Kohonen神经网络相结合,提出IWO-Kohonen聚类算法。利用IWO算法优化Kohonen神经网络的初始权值,训练Kohonen神经网络模型得到最优值。使用IWO算法增强算法的搜索能力,提高聚类正确率,并加快算法的收敛速度。实验结果表明,该算法与模糊聚类算法和广义神经网络聚类算法相比,分类正确率较高;与蚂蚁聚类算法和模糊C均值聚类算法相比,网络入侵检测率较高,误报率较低。To improve the correct rate of the Kohonen neural network model for clustering of network intrusion, this paper combines the lnvasive Weed Optimization（IWO） algorithm and the Kohonen neural network, and proposes IWO-Kohonen clustering algorithm. It uses IWO algorithm to optimize the initialized weights of the Kohonen neural network, and trains the Kohonen neural network model to calculate an optimal value. By using IWO algorithm, the search ability of the clustering algorithm is enhanced, which not only improves the correct rate of clustering, but also accelerates the convergence speed of the algorithm. Experimental results show that the proposed algorithm has higher correct rate comparing with fuzzy clustering algorithm and generalized neural network clustering algorithm, and it has higher detection rate and lower false alarm rate comparing with ant clustering algorithm and C-means clustering algorithm.

关 键 词：入侵杂草优化 KOHONEN神经网络 入侵检测系统 聚类 检测率 误报率 

分 类 号：TP309[自动化与计算机技术—计算机系统结构]