Network Traffic Classification Using a Random Field Model

被引:0
作者
Shen, Gang [1 ]
Niu, Zhaojie [1 ]
Duan, Liyuan [1 ]
机构
[1] Huazhong Univ Sci & Technol, Wuhan 430074, Peoples R China
来源
FIFTH INTERNATIONAL CONFERENCE ON DIGITAL IMAGE PROCESSING (ICDIP 2013) | 2013年 / 8878卷
关键词
Network traffic classification; Semantics models; Conditional random fields;
D O I
10.1117/12.2030954
中图分类号
O43 [光学];
学科分类号
070207 ; 0803 ;
摘要
The accurate identification of the different protocols used by various applications plays an important role in many network management and monitoring tasks. However, the development of emerging applications and the evolution of existing applications have made the early success of port number or payload signature based classification methods no longer repeatable. On the other hand, machine learning based approaches have achieved steady progress in classification accuracy, with the statistical features extracted from packets and flows. In this paper, by introducing a Markov random field to model the semantics of network application protocols, we investigate a new approach to classifying network traffic into application protocols. First the packets in a flow are aggregated into messages that contain the related semantics information. We assume that the simple message features like the length and the direction of a message are observable, while the semantics of messages are invisible in both training and test phases. Tested with traffic traces collected from heterogeneous sources, this approach was demonstrated to be able to deliver good accuracy and speed.
引用
收藏
页数:5
相关论文
共 15 条
  • [1] [Anonymous], 2006, P 2 ANN ACM WORKSH M
  • [2] Characterizing network traffic by means of the NETMINE framework
    Apiletti, Daniele
    Baralis, Elena
    Cerquitelli, Tania
    D'Elia, Vincenzo
    [J]. COMPUTER NETWORKS, 2009, 53 (06) : 774 - 789
  • [3] Auld Tom, 2006, IEEE2006
  • [4] Bernaile Lautent, 2007, P 16 INT C WORLD WID
  • [5] Crotti M, 2006, IEEE ICC, P170
  • [6] Karagiannis T., 2005, SIGCOMM
  • [7] Kim H., 2008, INTERNET TRAFFIC CLA
  • [8] Maia Jose Everardo Bessa, 2010, 10 INT C HYBR INT SY
  • [9] Moore A. W., 2005, Performance Evaluation Review, V33, P50, DOI 10.1145/1071690.1064220
  • [10] A Survey of Techniques for Internet Traffic Classification using Machine Learning
    Nguyen, Thuy T. T.
    Armitage, Grenville
    [J]. IEEE COMMUNICATIONS SURVEYS AND TUTORIALS, 2008, 10 (04): : 56 - 76