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基于聚类分析的网络流量高斯混合模型 被引量：2

Gaussian Mixture Model of Network Traffic Based on Clustering Analysis
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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.
作者 程华 房一泉
机构地区 华东理工大学计算机科学与工程系
出处 《华东理工大学学报（自然科学版）》 CAS CSCD 北大核心 2010年第2期255-260,共6页 Journal of East China University of Science and Technology
关键词 高斯混合模型 EM算法 聚类 Log-normal分布 幂律关系 Gaussian mixture model EM algorithm clustering Log-normal distribution power law
分类号 TP393 [自动化与计算机技术—计算机应用技术]
  • 相关文献

参考文献12

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共引文献70

同被引文献18

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引证文献2

二级引证文献6

华东理工大学学报（自然科学版）
2010年 第2期

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