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基于流量特征的用户互联网访问类型识别 被引量：2

Traffic Features Based Categories Identification on Users' Network Behavior
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摘要 近年来,对互联网用户在网络上的行为分析研究吸引了广泛的兴趣,分析的结果对网络运营商和普通用户都有重要的意义.研究用户在网络上的访问行为的类型识别问题,分析了一个由22万个网络数据包组成的数据集,从中提取统计特征,设计用户网络访问的类型识别算法,实验结果显示本文算法具有相当高的识别准确率. In recent years, the research on analyzing the users’ network behavior has attracted much attention. In this paper, we study the problem of identifying users’ network behavior categories. The research is based on a dataset that consists of 220 thousand network packets, with which we extract the statistical features needed for the identifications. We propose the identifying algorithm, and we also apply the algorithm to make categories identification on the network dataset. The results show the presented work can achieve very high accuracy.
作者 陈康 黄晓宇 陶彩霞 关迎晖 李磊 王爱宝
机构地区 中国电信股份有限公司广东研究院 华南理工大学经济与贸易学院 中山大学软件研究所 中国电信集团公司
出处 《计算机系统应用》 2014年第9期177-181,共5页 Computer Systems & Applications
基金 国家高技术研究发展计划(863)(2012AA12A203)
关键词 网络流量 类型识别 特征选择 决策树 network traffic category identification feature selection decision tree
分类号 TP393.08 [自动化与计算机技术—计算机应用技术]
  • 相关文献

参考文献17

  • 1Nguyen TTT, Armitage Ca A survey of techniques for intemet waffic classification using machine learning. IEEE Communications Surveys & Tutorials, 2008, 10(4): 56-76.
  • 2刘颖秋,李巍,李云春.网络流量分类与应用识别的研究[J].计算机应用研究,2008,25(5):1492-1495. 被引量：21
  • 3彭芸,刘琼.Internet流分类方法的比较研究[J].计算机科学,2007,34(8):58-61. 被引量：17
  • 4Moore A, Papagiannaki K. Toward the accurate identification of network applications. Proc. of Passive and Active Measurement Workshop (PAM2005). Boston (USA). 2005.
  • 5Sun AX, Lim EP, Ng WK. Web Classification Using Support Vector Machines. Proc. ACM Workshop on Web Information and Data Management (WIDM'02) with ACM CIKM. Virginia, USA, Nov. 2002. 96-99.
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二级参考文献41

  • 1Karagiannis T,Papagiannaki D,Faloutsos M.BLINC:Multilevel Traffic Classification in the Dark[C].ACM SIGCOMM,Philadelphia,PA,USA,August 2005
  • 2Plissonneau L,Costeux J L,Brown P.Analysis of Peer-to-Peer Traffic on ADSL[J].Passive and Active Network Measurement,2005,3431:69-82
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  • 6Bleul H,Rathgeb EP.A Simple,Efficient and Flexible Approach to Measure Multi-protocol Peer-to-Peer Traffic[J].Networking-ICN 2005,2005,3421:606-616
  • 7Sen S,Spatscheck O,Wang D.Accurate,Scalable In-network Identification of P2P Traffic Using Application Signatures[C].In:Proceedings of the 13th international conference on World Wide Web,New York,2004
  • 8Zander S,Nguyen T,Armitage G.Automated Traffic Classification and Application Identification using Machine Learning[C].In:Proceedings of the IEEE Conference on Local Computer Networks 30th Anniversary,2005
  • 9NetMate[EB/OL](http://sourceforge.net/projects/netmate-meter/)(August 2005)
  • 10Zander S,Nguyen T,Armitage G.Self-learning IP Traffic Classification Based on Statistical Flow Characteristics[J].Passive and Active Networking Measurement,2005,3431:325-328

共引文献34

同被引文献6

引证文献2

二级引证文献4

计算机系统应用
2014年 第9期

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