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Early Classification of Network Traffic through Multi-classification

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Traffic Monitoring and Analysis (TMA 2011)

Part of the book series: Lecture Notes in Computer Science ((LNCCN,volume 6613))

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Abstract

In this work we present and evaluate different automated combination techniques for traffic classification. We consider six intelligent combination algorithms applied to both traditional and more recent traffic classification techniques using either packet content or statistical properties of flows. Preliminary results show that, when selecting complementary classifiers, some combination algorithms allow a further improvement – in terms of classification accuracy – over already well-performing stand-alone classification techniques. Moreover, our experiments show that the positive impact of combination is particularly significant when there are early-classification constraints, that is, when the classification of a flow must be obtained in its early stage (e.g. first 1 – 4 packets) in order to perform network operations online.

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Author information

Authors and Affiliations

  1. Department of Computer Engineering and Systems, Universitá di Napoli Federico II, Italy

    Alberto Dainotti, Antonio Pescapé & Carlo Sansone

Authors
  1. Alberto Dainotti
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  2. Antonio Pescapé
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  3. Carlo Sansone
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Editor information

Editors and Affiliations

  1. Universitat Politècnica de Catalunya (UPC) - Barcelona TECH, Campus Nord, Mòdul D6, Jordi Girona 1-3, 08034, Barcelona, Spain

    Jordi Domingo-Pascual

  2. The Iby and Aladar Fleischman Faculty of Engineering, School of Electrical Engineering, Tel Aviv University, Wolfson Building for Software Engineering, 69978, Ramat Aviv, Israel

    Yuval Shavitt

  3. An-Institut Deutsche Telekom Laboratories, FG INET, Research Group Anja Feldmann, Technische Universität Berlin, Sekr. TEL 16, Ernst-Reuter-Platz 7, 10587, Berlin, Germany

    Steve Uhlig

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Dainotti, A., Pescapé, A., Sansone, C. (2011). Early Classification of Network Traffic through Multi-classification. In: Domingo-Pascual, J., Shavitt, Y., Uhlig, S. (eds) Traffic Monitoring and Analysis. TMA 2011. Lecture Notes in Computer Science, vol 6613. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20305-3_11

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  • DOI: https://doi.org/10.1007/978-3-642-20305-3_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-20304-6

  • Online ISBN: 978-3-642-20305-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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