Revealing the Unknown ADSL Traffic Using Statistical Methods

  • Marcin Pietrzyk
  • Guillaume Urvoy-Keller
  • Jean-Laurent Costeux
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5537)

Abstract

Traffic classification is one of the most significant issues for ISPs and network administrators. Recent research on the subject resulted in a large variety of algorithms and methods applicable to the problem. In this work, we focus on several issues that have not received enough attention so far. First, the establishment of an accurate reference point. We use an ISP internal Deep Packet Inspection (DPI) tool and confront its results with state of the art, freely available classification tools, finding significant differences. We relate those differences to the weakness of some signatures and to the heuristics and design choices made by DPI tools. Second, we highlight methodological issues behind the choices of the traffic classes and the way of analyzing the results of a statistical classifier. Last, we focus on the often overlooked problem of mining the unknown traffic, i.e., traffic not classified by the DPI tool used to establish the reference point. We present a method, relying on the level of confidence of the statistical classification, to reveal the unknown traffic. We further discuss the result of the classifier using a variety of heuristics.

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References

  1. 1.
    Trestian, I., Ranjan, S., Kuzmanovic, A., Nucci, A.: Unconstrained Endpoint Profiling (Googling the Internet). In: Proceedings of ACM SIGCOMM 2008, Seattle, WA (August 2008)Google Scholar
  2. 2.
    Bernaille, L., Teixeira, R., Salamatian, K.: Early Application Identification. In: The 2nd ADETTI/ISCTE CoNEXT Conference, Lisboa, Portugal (December 2006)Google Scholar
  3. 3.
    Erman, M.A., Mahanti, A.: Traffic Classification Using Clustering Algorithms. In: Proceedings of the 2006 SIGCOMM workshop on Mining network data, Pisa (Italy), September 2006, pp. 281–286 (2006)Google Scholar
  4. 4.
    Dreder, H., Feldmann, A., Paxson, V., Sommer, R.: Operational Experiences with High-Volume Network Intrusion Detection. In: Proceedings of the 11th ACM conference on Computer and communications security, Washington DC, USA (2004)Google Scholar
  5. 5.
    Szabo, G., Orincsay, D., Malomsoky, S., Szabó, I.: On the Validation of Traffic Classification Algorithms. In: Claypool, M., Uhlig, S. (eds.) PAM 2008. LNCS, vol. 4979, pp. 72–81. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  6. 6.
    Paxson, V.: Empirically derived analytic models of wide-area TCP connections. IEEE/ACM Transactions on Networking 2(4), 316–336 (1994)CrossRefGoogle Scholar
  7. 7.
    Kim, H., Claffy, K.C., Fomenkova, M., Barman, D., Faloutsos, M., Lee, K.Y.: Internet Traffic Classificatoin Demystified: Myths, Caveats, and the Best Practices. In: ACM CoNEXT, Madrid, Spain (December 2008)Google Scholar
  8. 8.
    Nguyen, T.T.T., Armitage, G.: A Survey of Techniques for Internet Traffic Classification using Machine Learning. In: IEEE Communications Surveys Tutorials, 4th edn. (2008)Google Scholar
  9. 9.
  10. 10.
  11. 11.

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Marcin Pietrzyk
    • 1
  • Guillaume Urvoy-Keller
    • 2
  • Jean-Laurent Costeux
    • 1
  1. 1.Orange LabsFrance
  2. 2.Institute EurecomFrance

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