文献类型：期刊文献

中文题名：重尾分布的网络流量SVM分类

英文题名：SVM Classification of Heavy-Tailed Internet Traffic

作者：程华[1];夏宁[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：6

起止页码：807

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：流量分类;突发;重尾分布;不平衡数据集;支持向量机(SVM)

外文关键词：traffic classification; burstiness; heavy-tailed distribution; imbalanced dataset; SVM

摘要：网络流量表现出突发和自相似等动态特性,使得网络应用很难进行准确分类。本文分析了流量动态特性产生的不平衡性及其重尾分布,提出了基于重尾分布的流量分类定量分析模型。基于该分析模型,研究分类算法中训练集采集位置和规模大小的选取。考虑到混合流量中的次要数据流通常是小样本,选用支持向量机(SVM)算法进行流量分类。实验结果表明:重尾分布的流量分类训练集可以选择最佳采集位置和规模,以获得较好的分类模型,该定量分析模型对流量分类及提高分类精度有指导意义。
The dynamic characteristics of burstiness and self-similarity make it difficult to classify the various applications from internet traffic.By analyzing the imbalance and heavy-tailed distribution of internet traffic,a heavy-tailed traffic based classification model for quantitative analysis is proposed.Both the sampling position and the window of training set in the long tail of traffic are studied.The SVM algorithm is adopted for the traffic classification,since the minor one included in a combined traffic is generally small sampling.The experimental results show that the heavy-tailed traffic can obtain a better classification mode by choosing best sampling position and scale of training set.Moreover,the quantitative analysis model has positive significance for achieving better classification precisions.