Abstract
Peer-to-Peer (P2P) detection using machine learning (ML) classification is affected by its training quality and recency. In this paper, a practical retraining mechanism is proposed to retrain an on-line P2P ML classifier with the changes in network traffic behavior. This mechanism evaluates the accuracy of the on-line P2P ML classifier based on the training datasets containing flows labeled by a heuristic based training dataset generator. The on-line P2P ML classifier is retrained if its accuracy falls below a predefined threshold. The proposed system has been evaluated on traces captured from the Universiti Teknologi Malaysia (UTM) campus network between October and November 2011. The overall results shows that the training dataset generation can generate accurate training dataset by classifying P2P flows with high accuracy (98.47%) and low false positive (1.37%). The on-line P2P ML classifier which is built based on J48 algorithm which has been demonstrated to be capable of self-retraining over time.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bernaille, L., Teixeira, R., Salamatian, K.: Early application identification. In: Proceedings of the 2006 ACM CoNEXT conference (CoNEXT 2006), Lisboa, Portugal, pp. 6:1–6:12 (2006)
Chen, Z., Yang, B., Chen, Y., Abraham, A., Grosan, C., Peng, L.: Online hybrid traffic classifier for peer-to-peer systems based on network processors. Applied Soft Computing 9(2), 685–694 (2009)
Hassan, M., Marsono, M.: A three-class heuristics technique: Generating training corpus for peer-to-peer traffic classification. In: Proceedings of the 2010 IEEE 4th International Conference on Internet Multimedia Services Architecture and Application (IMSAA 2010), pp. 1–5 (2010)
John, W., Tafvelin, S.: Heuristics to classify internet backbone traffic based on connection patterns. In: ICOIN 2008: 22nd International Conference on Information Networking, pp. 1–5 (2008)
Karagiannis, T., Broido, A., Faloutsos, M., Claffy, K.: Transport layer identification of P2P traffic. In: Proceedings of the 4th ACM SIGCOMM Conference on Internet Measurement, Taormina, Sicily, Italy, pp. 121–134 (2004)
Li, W., Moore, A.W.: A machine learning approach for efficient traffic classification. In: Proceedings of 15th IEEE International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems, Washington, DC, USA, pp. 310–317 (2007)
Madhukar, A., Williamson, C.: A longitudinal study of P2P traffic classification. In: Proceedings of the 2007 15th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS 2007), Washington, DC, USA, pp. 179–188 (2006)
Moore, A.W., Papagiannaki, K.: Toward the Accurate Identification of Network Applications. In: Dovrolis, C. (ed.) PAM 2005. LNCS, vol. 3431, pp. 41–54. Springer, Heidelberg (2005)
Mula-Valls, O.: A practical retraining mechanism for network traffic classification in operational environments. Master thesis, Universitat Politècnica de Catalunya (2011)
Nguyen, T., Armitage, G.: A Survey of Techniques for Internet Traffic Classification using Machine Learning. IEEE Communications Surveys & Tutorials 10(4), 56–76 (2008)
Perényi, M., Dang, T.D., Gefferth, A., Molnár, S.: Identification and analysis of peer-to-peer traffic. Journal of Communication 1(7), 36–46 (2006)
Raahemi, B., Hayajneh, A., Rabinovitch, P.: Peer-to-peer IP traffic classification using decision tree and IP layer attributes. International Journal of Business Data Communications and Networking 3(4), 60–74 (2007)
Sen, S., Wang, J.: Analyzing peer-to-peer traffic across large networks. IEEE/ACM Transaction on Networking 12, 219–232 (2004)
Sen, S., Spatscheck, O., Wang, D.: Accurate, scalable in-network identification of P2P traffic using application signatures. In: Proceedings of the 13th International Conference on World Wide Web, WWW 2004, pp. 512–521. ACM, New York (2004)
Soysal, M., Schmidt, E.G.: Machine learning algorithms for accurate flow-based network traffic classification: Evaluation and comparison. Performance Evaluation 67(6), 451–467 (2010)
Tian, X., Sun, Q., Huang, X., Ma, Y.: A dynamic online traffic classification methodology based on data stream mining. In: 2009 WRI World Congress on Computer Science and Information Engineering, vol. 1, pp. 298–302 (2009)
Williams, N., Zander, S., Armitage, G.: A preliminary performance comparison of five machine learning algorithms for practical IP traffic flow classification. SIGCOMM Computer Communication Review 36(5), 5–16 (2006)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zarei, R., Monemi, A., Marsono, M.N. (2013). Retraining Mechanism for On-Line Peer-to-Peer Traffic Classification. In: Abraham, A., Thampi, S. (eds) Intelligent Informatics. Advances in Intelligent Systems and Computing, vol 182. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32063-7_40
Download citation
DOI: https://doi.org/10.1007/978-3-642-32063-7_40
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-32062-0
Online ISBN: 978-3-642-32063-7
eBook Packages: EngineeringEngineering (R0)