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山东大学学报（理学版） ›› 2014, Vol. 49 ›› Issue (09): 109-114.doi: 10.6040/j.issn.1671-9352.2.2014.106

• 论文 • 上一篇    下一篇

一种基于相似度的高效网络流量识别方案

杜瑞颖1, 杨勇2, 陈晶1, 王持恒1   

  1. 1. 武汉大学计算机学院, 湖北 武汉 430072;
    2. 武汉大学国际软件学院, 湖北 武汉 430072
  • 收稿日期:2014-06-24 修回日期:2014-08-27 出版日期:2014-09-20 发布日期:2014-09-30
  • 作者简介:杜瑞颖（1964-），女，教授，博士，主要研究领域为网络安全.E-mail：duraying@126.com
  • 基金资助:
    国家自然科学基金资助项目（61272451，61173154）

An efficient network traffic classification scheme based on similarity

DU Rui-ying1, YANG Yong2, CHEN Jing1, WANG Chi-heng1   

  1. 1. School of Computer, Wuhan University, Wuhan 430072, Hubei, China;
    2. International School of Software, Wuhan University, Wuhan 430072, Hubei, China
  • Received:2014-06-24 Revised:2014-08-27 Online:2014-09-20 Published:2014-09-30

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摘要： 支持向量机（support vector machine，SVM）是分类算法中集高效性、准确率和实时性于一体的分类方案。但由于在SVM分类决策的过程中，无关的分类器也参与了投票，使得方案的实时性和分类可靠性有一定程度的降低。提出了基于相似度的高效SVM网络流量识别方案（efficient SVM based on similarity，ESVMS）。ESVMS通过估算待分类实例可能所属的类别范围，排除SVM中那些无关分类器的投票决策。实验结果表明ESVMS较SVM分类准确度几乎没有降低，但分类实时性进一步提高。

关键词: 网络流量识别, 机器学习, 支持向量机

Abstract: Support Vector Machine is a classification algorithm that combines high efficiency, high accuracy and real time. There's a problem when SVM makes its decision for an un-labeled instance because uninvolved classifiersparticipate in that affects SVM's real time performance and reliability. Thus, a method utilized Efficient SVM based on Similarity (ESVMS) for traffic classification was proposed. ESVMS estimates the classes that an un-labeled instances may belongs to as to kick out the uninvolved classifiers. Experimental results show that ESVMS holds the accuracy of SVM's and improves its real time performance.

Key words: machine learning, support vector machine, network traffic classification

中图分类号: 

  • TP393
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