基于非欧式距离的模糊核聚类算法在入侵检测的应用  

Application of Optimized KFCM Algorithm in Intrusion Detection Based on Non-Euclidean Distance

作　　者：李丛[1] 王云[1] 胡文军[2] 丁勇[1] 

机构地区：[1]南京理工大学泰州科技学院计算机科学与技术系,泰州225300 [2]湖州师范学院信息工程学院,湖州313000

出　　处：《计算机与数字工程》2014年第12期2235-2238,2340,共5页Computer & Digital Engineering

基　　金：浙江省自然科学基金项目(编号:LY13F020011)资助

摘　　要：模糊核C-均值聚类(KFCM)的主要思想是在模糊C-均值聚类(FCM)中引入核函数,样本点被非线性变换映射到高维特征空间进行聚类,解决了高维数据空间的聚类问题。同经典的FCM算法及其派生算法一样,KFCM算法对噪声或野值数据敏感。论文在KFCM基础上,利用鲁棒统计观点对目标函数进行改进,通过引入非欧式距离度量代替欧氏距离度量,提高其对噪声或野值数据的抗干扰能力。将该算法用于构建入侵检测系统模型并通过模拟仿真实验表明,改进算法有效解决了传统的聚类算法在入侵检测中稳定性差,检测准确率低的问题。Based on FCM and by introducing the kernel function,Kernel fuzzy C-means clustering（KFCM）make the sample points nonlinear mapped to a high-dimensional feature space for clustering which can solve the problem of high dimensional data space clustering.Like the classic FCM clustering algorithm and its Derived algorithm,KFCM clustering algorithm is sensitive to noises or outliers.Based on modified objective function by using the robust statistical view,a new non-Euclidean distance is introduced to replace the Euclidean distance which can improve anti-jamming capability of noise or outliers data.The improved algorithm is used to construct the model of intrusion detection system.Our experimental results show the proposed algorithm can solve poor stability and low detection accuracy of the traditional clustering algorithms in intrusion detection.

关 键 词：KFCM 非欧式距离 入侵检测 

分 类 号：TP393.1[自动化与计算机技术—计算机应用技术]