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A Novel Network Traffic Analysis Method Based on Fuzzy Association Rules

  • Xinyu Yang
  • Wenjing Yang
  • Ming Zeng
  • Yi Shi
Conference paper
  • 658 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3131)

Abstract

For network traffic analysis and forecasting, a novel method based on fuzzy association rules is proposed in this paper. Connecting fuzzy logic theory with association rules, the method sets up the fuzzy association rules and could analyze the traffic of the global network by using data mining algorithm. Therefore, this method can represent the traffic’s characters much more precisely and forecast the behaviors of traffic in advance. The paper firstly introduces the new classification method on network traffic. Then the fuzzy association rules are applied to analyze the behaviors of traffic in existence. Finally, the results of simulation experiments indicating that the fuzzy association rule is very effective in discovering the relativity of different traffic in the analysis of traffic flow are shown.

Keywords

Membership Function Association Rule Traffic Flow Network Traffic Membership Degree 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Awduche, D.O., Chin, A., Flwalid, A., et al.: A framework for internet traffic engineering. internet-draft, draft-ietf-tewg-framework-00.txt , http://www.ietf.org
  2. 2.
    Lee, Y.D., van de Liefvoort, A., Wallace, V.L.: Modeling correlated traffic with a generalized IPP. Performance Evaluation, 99–114 (2000)Google Scholar
  3. 3.
    Kang, K., Kim, C.: Performance analysis of statistical multiplexing of heterogeneous discrete-time Markovian arrival processes in an ATM network. Computer Communications 20(11), 970–978 (1997)CrossRefGoogle Scholar
  4. 4.
    Xinyu, Y., Shouqi, Z., Ming, Z., Li, Z., Hengyi, W.: The Path Restrained Association Rules Algorithmic for Network Traffic Engineering. Xi’an Jiao Tong University transaction 8, 834–838 (2001)Google Scholar
  5. 5.
    Delgado, M., Marin, N., Sanchez, D., Vila, M.-A.: Fuzzy association rules: general model and applications. Fuzzy Systems 11(2) (2003)Google Scholar
  6. 6.
    Agrawal, R., Srikant, R.: Fast Algorithms for Mining Association Rules. In: Proceedings of the 20th VLDB Conferences, Santiago Chile (1994)Google Scholar
  7. 7.
    Tzungpei, H., Chansheng, K., Shengchai, C.: Mining Fuzzy Sequential Patterns from Quantitative Data. Systems, Man, and Cybernetics 3, 12–15 (1999)Google Scholar
  8. 8.
    Han, J., Kamber, M.: Data mining concepts and techniques. 1st edn. China Machine Press, Beijing (2001)Google Scholar
  9. 9.
    Naiqian, L., Junyi, S.: An Algorithm Automatic Generating Fuzzy Sets for Quantitative Attributes. Computer Engineering and Application 21, 10–11 (2002)Google Scholar
  10. 10.
    Shu, J.Y., Tsang, E.C.C., Yeung, D.S.: Query fuzzy association rules in relational database. In: IFSA World Congress and 20th NAFIPS International Conference (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Xinyu Yang
    • 1
  • Wenjing Yang
    • 1
  • Ming Zeng
    • 1
  • Yi Shi
    • 1
  1. 1.Dept. of Computer Science and TechnologyXi an Jiaotong UniversityXi anChina

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