Skip to main content
SpringerLink
Log in

Waterfall Traffic Identification: Optimizing Classification Cascades

  • Conference paper
  • First Online:
Computer Networks (CN 2015)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 522))

Included in the following conference series:

Abstract

The Internet transports data generated by programs which cause various phenomena in IP flows. By means of machine learning techniques, we can automatically discern between flows generated by different traffic sources and gain a more informed view of the Internet.

In this paper, we optimize Waterfall, a promising architecture for cascade traffic classification. We present a new heuristic approach to optimal design of cascade classifiers. On the example of Waterfall, we show how to determine the order of modules in a cascade so that the classification speed is maximized, while keeping the number of errors and unlabeled flows at minimum. We validate our method experimentally on 4 real traffic datasets, showing significant improvements over random cascades.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    Downloaded from http://www.ing.unibs.it/ntw/tools/traces/.

  2. 2.

    See https://github.com/iitis/mutrics/tree/bks.

References

  1. Karagiannis, T., Papagiannaki, K., Faloutsos, M.: Blinc: multilevel traffic classification in the dark. In: ACM SIGCOMM Computer Communication Review, vol. 35, pp. 229–240. ACM (2005)

    Google Scholar 

  2. Finamore, A., Mellia, M., Meo, M., Rossi, D.: KISS: stochastic packet inspection classifier for UDP traffic. IEEE/ACM Trans. Netw. 18(5), 1505–1515 (2010)

    Google Scholar 

  3. Bermolen, P., Mellia, M., Meo, M., Rossi, D., Valenti, S.: Abacus: accurate behavioral classification of P2P-TV traffic. Comp. Netw. 55(6), 1394–1411 (2011)

    Google Scholar 

  4. Nguyen, T.T., Armitage, G.: A survey of techniques for internet traffic classification using machine learning. Commun. Surv. Tutor. IEEE 10(4), 56–76 (2008)

    Google Scholar 

  5. Callado, A., Kamienski, C., Szabó, G., Gero, B., Kelner, J., Fernandes, S., Sadok, D.: A survey on internet traffic identification. Commun. Surv. Tutor. IEEE 11(3), 37–52 (2009)

    Google Scholar 

  6. Dainotti, A., Pescape, A., Claffy, K.C.: Issues and future directions in traffic classification. Netw. IEEE 26(1), 35–40 (2012)

    Google Scholar 

  7. Foremski, P.: On different ways to classify Internet traffic: a short review of selected publications. Theor. Appl. Inf. 25(2), 119–136 (2013)

    Google Scholar 

  8. Dainotti, A., Pescapé, A., Sansone, C.: Early classification of network traffic through multi-classification. In: Domingo-Pascual, J., Shavitt, Y., Uhlig, S. (eds.) TMA 2011. LNCS, vol. 6613, pp. 122–135. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  9. Foremski, P., Callegari, C., Pagano, M.: Waterfall: rapid identification of IP flows using cascade classification. In: Kwiecień, A., Gaj, P., Stera, P. (eds.) CN 2014. CCIS, vol. 431, pp. 14–23. Springer, Heidelberg (2014)

    Chapter  Google Scholar 

  10. Kuncheva, L.I.: Combining Pattern Classifiers: Methods and Algorithms. Wiley (2004)

    Google Scholar 

  11. Alpaydin, E., Kaynak, C.: Cascading classifiers. Kybernetika 34(4), 369–374 (1998)

    MATH  Google Scholar 

  12. Chellapilla, K., Shilman, M., Simard, P.: Optimally combining a cascade of classifiers. Proceed. SPIE 6067, 207–214 (2006)

    Google Scholar 

  13. Abdelazeem, S.: A greedy approach for building classification cascades. In: Seventh International Conference on Machine Learning and Applications, ICMLA 2008, pp. 115–120. IEEE (2008)

    Google Scholar 

  14. Land, A.H., Doig, A.G.: An automatic method of solving discrete programming problems. Econometrica 28(3), 497–520 (1960)

    Article  MATH  MathSciNet  Google Scholar 

  15. Foremski, P., Callegari, C., Pagano, M.: DNS-class: immediate classification of IP flows using DNS. Int. J. Netw. Manag. 24(4), 272–288 (2014)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. The Institute of Theoretical and Applied Informatics of the Polish Academy of Sciences, Bałtycka 5, 44-100, Gliwice, Poland

    Paweł Foremski

  2. Department of Information Engineering, University of Pisa, Via Caruso 16, 56122, Pisa, Italy

    Christian Callegari & Michele Pagano

Authors
  1. Paweł Foremski
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Christian Callegari
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Michele Pagano
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Paweł Foremski .

Editor information

Editors and Affiliations

  1. Silesian University of Technology, Gliwice, Poland

    Piotr Gaj

  2. Silesian University of Technology, Gliwice, Poland

    Andrzej Kwiecień

  3. Silesian University of Technology, Gliwice, Poland

    Piotr Stera

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Foremski, P., Callegari, C., Pagano, M. (2015). Waterfall Traffic Identification: Optimizing Classification Cascades. In: Gaj, P., Kwiecień, A., Stera, P. (eds) Computer Networks. CN 2015. Communications in Computer and Information Science, vol 522. Springer, Cham. https://doi.org/10.1007/978-3-319-19419-6_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-19419-6_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-19418-9

  • Online ISBN: 978-3-319-19419-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation