文献类型：期刊文献

中文题名：基于聚类分析的网络流量高斯混合模型

英文题名：Gaussian Mixture Model of Network Traffic Based on Clustering Analysis

作者：程华[1];房一泉[1]

机构：[1]华东理工大学计算机科学与工程系,上海200237

年份：2010

卷号：36

期号：2

起止页码：255

中文期刊名：华东理工大学学报（自然科学版）

外文期刊名：Journal of East China University of Science and Technology

收录：CSTPCD;;Scopus;北大核心:【北大核心2008】；CSCD:【CSCD2011_2012】；

语种：中文

中文关键词：高斯混合模型;EM算法;聚类;Log-normal分布;幂律关系

外文关键词：Gaussian mixture model; EM algorithm; clustering; Log-normal distribution; power law

摘要：基于聚类算法对数据对象多个属性综合聚类的特点,研究网络流量的GMM模型及其在数据流尺度上的Log-normal分布。用EM算法研究了具有交互特征的网络流量的分类;通过与K-means算法比较,讨论了EM算法在流量聚类中的适用性;通过平衡和不平衡流量的聚类分析,研究了不同类型流量GMM建模的有效性。研究流量的幂律关系及其在不同尺度间的传递性,用户行为和应用程序特征通过传输层控制协议分解传递到IP层后,在数据包尺度上表现出分形和自相似性,在数据流尺度上表现出Log-normal分布。
The cluster algorithm may make classification on a few attributes of objects.Based on the above feature,this paper studies the Gaussian mixture model（GMM） of network traffic and its log-normal distribution on flow scale.The EM algorithm is used to cluster traffics with interactive features.It is shown that EM algorithm is more appropriate on traffic clustering than K-means algorithm.The clustering analysis on both the balanced and unbalanced traffics shows that GMM is effective on different kinds of traffics.The log-normal distribution and the transitivity of power law from application layer to IP layer are studied. After the log-normal distribution in application layer produced by user behaviors and application features is transferred to IP layer via the control protocols in transport layer,the traffic presents fractal and self-similar on the packet scale.