计算机工程与应用 ›› 2010, Vol. 46 ›› Issue (25): 78-81.DOI: 10.3778/j.issn.1002-8331.2010.25.023

• 网络、通信、安全 • 上一篇    下一篇

使用贝叶斯学习算法分类网络流量

邱 密1,阳爱民1,2,刘永定1,何震凯1   

  1. 1.湖南工业大学 计算机与通信学院,湖南 株洲 412008
    2.广东外语外贸大学 信息科学技术学院,广州 510006
  • 收稿日期:2009-02-18 修回日期:2009-04-13 出版日期:2010-09-01 发布日期:2010-09-01
  • 通讯作者: 邱 密

Application of Bayes learning algorithm to classify network traffic

QIU Mi1,YANG Ai-min1,2,LIU Yong-ding1,HE Zhen-kai1   

  1. 1.School of Computer and Communication,Hunan University of Technology,Zhuzhou,Hunan 412008,China
    2.Information Science and Technology College,Guangdong University of Foreign Studies,Guangzhou 510006,China
  • Received:2009-02-18 Revised:2009-04-13 Online:2010-09-01 Published:2010-09-01
  • Contact: QIU Mi

摘要: 随着网络应用(如P2P)的快速增长,使得传统的基于端口与有效载荷的网络流量分类方法效率大大降低。基于FCBF特征选择方法选择最优特征子集,研究使用贝叶斯学习方法对网络流量进行分类;实验结果显示提出的方法取得了较好的分类准确率。

Abstract: As network applications such as P2P rapidly increase,which makes the efficiency of traditional network traffic classification method that is based on the port and payload reduces.In this paper,it introduces a FCBF feature selection method,which can choose the best feature subsets.It uses Bayes learning algorithm to classify the network.The result of experiment shows a better classification accuracy.

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