Telecommunication Systems

, Volume 63, Issue 2, pp 191–204 | Cite as

A streaming flow-based technique for traffic classification applied to 12 + 1 years of Internet traffic

  • Valentín Carela-Español
  • Pere Barlet-Ros
  • Albert Bifet
  • Kensuke Fukuda
Article

Abstract

The continuous evolution of Internet traffic and its applications makes the classification of network traffic a topic far from being completely solved. An essential problem in this field is that most of proposed techniques in the literature are based on a static view of the network traffic (i.e., they build a model or a set of patterns from a static, invariable dataset). However, very little work has addressed the practical limitations that arise when facing a more realistic scenario with an infinite, continuously evolving stream of network traffic flows. In this paper, we propose a streaming flow-based classification solution based on Hoeffding Adaptive Tree, a machine learning technique specifically designed for evolving data streams. The main novelty of our proposal is that it is able to automatically adapt to the continuous evolution of the network traffic without storing any traffic data. We apply our solution to a 12 + 1 year-long dataset from a transit link in Japan, and show that it can sustain a very high accuracy over the years, with significantly less cost and complexity than existing alternatives based on static learning algorithms, such as C4.5.

Keywords

Traffic classification Machine learning Stream classification Hoeffding adaptive tree Network monitoring 

Notes

Acknowledgments

This research was funded by the NII International Internship Program, by the Spanish Ministry of Economy and Competitiveness under contract TEC2011-27474 (NOMADS project) and by AGAUR (ref. 2014-SGR-1427).

References

  1. 1.
    Dainotti, A., Pescapè, A., & Claffy, K. C. (2012). Issues and future directions in traffic classification. IEEE Network, 26(1), 35–40. doi:10.1109/MNET.2012.6135854.CrossRefGoogle Scholar
  2. 2.
    Alcock, S., & Nelson, R. (2015). Libprotoident: traffic classification using lightweight packet inspection. Technical report, University of Waikato (2012). [Online]. Retrieved June 22, 2015 from http://www.wand.net.nz/publications/lpireport.
  3. 3.
    Carela-Español, V., Bujlow, T., & Barlet-Ros, P. (2014). Is our ground-truth for traffic classification reliable? In Proceedings of the 15th international conference on passive and active network measurement, PAM’14 (pp. 98–108). Berlin: Springer. doi:10.1007/978-3-319-04918-2_10.
  4. 4.
    Lim, Y. S., Kim, H. C., Jeong, J., Kim, C. K., Kwon, T. T., & Choi, Y. (2010). Internet traffic classification demystified: On the sources of the discriminative power. In Proceedings of the 6th international conference, Co-NEXT’10 (pp. 9:1–9:12). New York, NY: ACM. doi:10.1145/1921168.1921180.
  5. 5.
    Nguyen, T. T., & Armitage, G. (2008). A survey of techniques for internet traffic classification using machine learning. IEEE on Communications Surveys & Tutorials, 10(4), 56–76. doi:10.1109/SURV.2008.080406.CrossRefGoogle Scholar
  6. 6.
    Carela-Español, V., Barlet-Ros, P., Cabellos-Aparicio, A., & Solé-Pareta, J. (2011). Analysis of the impact of sampling on netflow traffic classification. Computer Networks, 55(5), 1083–1099. doi:10.1016/j.comnet.2010.11.002.CrossRefGoogle Scholar
  7. 7.
    Alcock, S., & Nelson, R. (2013). Measuring the accuracy of open-source payload-based traffic classifiers using popular internet applications. In IEEE 38th conference on local computer networks workshops (LCN workshop on network measurements) (pp. 956–963). doi:10.1109/LCNW.2013.6758538.
  8. 8.
    Bujlow, T., Carela-Español, V., & Barlet-Ros, P. (2015). Independent comparison of popular dpi tools for traffic classification. Computer Networks, 76, 75–89. doi:10.1016/j.comnet.2014.11.001.CrossRefGoogle Scholar
  9. 9.
    de Donato, W., Pescape, A., & Dainotti, A. (2014). Traffic identification engine: An open platform for traffic classification. IEEE on Network, 28(2), 56–64. doi:10.1109/MNET.2014.6786614.CrossRefGoogle Scholar
  10. 10.
    Gama, J. A., Sebastião, R., & Rodrigues, P. P. (2009). Issues in evaluation of stream learning algorithms. In Proceedings of the 15th ACM SIGKDD international conference on knowledge discovery and data mining, KDD’09 (pp. 329–338). New York, NY: ACM. doi:10.1145/1557019.1557060.
  11. 11.
    Bifet, A., Holmes, G., Kirkby, R., & Pfahringer, B. (2010). Moa: Massive online analysis. Journal of Machine Learning Research, 11, 1601–1604. http://www.jmlr.org/proceedings/papers/v11/bifet10a.html.
  12. 12.
    Carela-Español, V., Barlet-Ros, P., Mula-Valls, O., & Sole-Pareta, J. (2013). An automatic traffic classification system for network operation and management. Journal of Network and Systems Management. http://link.springer.com/article/10.1007/s10922-013-9293-1.
  13. 13.
    Cisco IOS NetFlow: [Online]. Retrieved June 22, 2015, from http://www.cisco.com/c/en/us/products/ios-nx-os-software/ios-netflow/index.html.
  14. 14.
    MAWI Working Group Traffic Archive: [Online]. Retrieved June 22, 2015, from http://mawi.wide.ad.jp/mawi/.
  15. 15.
    Quinlan, J. (1993). C4. 5: Programs for machine learning. San Francisco, CA: Morgan Kaufmann.Google Scholar
  16. 16.
    Gama, J. (2012). A survey on learning from data streams: current and future trends. Progress in Artificial Intelligence, 1(1), 45–55. doi:10.1007/s13748-011-0002-6.CrossRefGoogle Scholar
  17. 17.
    Tian, X., Sun, Q., Huang, X., & Ma, Y. (2008). Dynamic online traffic classification using data stream mining. In Proceedings of the 2008 international conference on multimedia and information technology, MMIT’08 (pp. 104–107). Washington, DC: IEEE Computer Society. doi:10.1109/MMIT.2008.185.
  18. 18.
    Tian, X., Sun, Q., Huang, X., & Ma, Y. (2009). A dynamic online traffic classification methodology based on data stream mining. In Proceedings of the 2009 WRI world congress on computer science and information engineering, CSIE ’09 (vol. 01, pp. 298–302). Washington, DC: IEEE Computer Society. doi:10.1109/CSIE.2009.904.
  19. 19.
    Raahemi, B., Zhong, W., & Liu, J. (2008). Peer-to-peer traffic identification by mining ip layer data streams using concept-adapting very fast decision tree. In Proceedings of the 2008 20th IEEE international conference on tools with artificial intelligence, ICTAI’08 (vol. 01, pp. 525–532). Washington, DC: IEEE Computer Society. doi:10.1109/ICTAI.2008.12.
  20. 20.
    Hulten, G., Spencer, L., & Domingos, P. (2001). Mining time-changing data streams. In Proceedings of the seventh ACM SIGKDD international conference on knowledge discovery and data mining, KDD’01 (pp. 97–106). New York: ACM. doi:10.1145/502512.502529.
  21. 21.
    Moore, A. W., & Papagiannaki, K. (2005). Toward the accurate identification of network applications. In Proceedings of the 6th international conference on passive and active network measurement, PAM’05 (pp. 41–54). Berlin: Springer. doi:10.1007/978-3-540-31966-5_4.
  22. 22.
    Dainotti, A., Gargiulo, F., Kuncheva, L. I., Pescape, A., & Sansone, C. (2010). Identification of traffic flows hiding behind tcp port 80. In IEEE international conference on communications (ICC) (pp. 1–6). doi:10.1109/ICC.2010.5502266.
  23. 23.
    Hoeffding, W. (1963). Probability inequalities for sums of bounded random variables. Journal of the American Statistical Association, 58(301), 13–30. doi:10.2307/2282952.CrossRefGoogle Scholar
  24. 24.
    Bifet, A., & Gavaldà, R. (2009). Adaptive learning from evolving data streams. In Proceedings of the 8th international symposium on intelligent data analysis: Advances in intelligent data analysis VIII, IDA’09 (pp. 249–260). Berlin: Springer. doi:10.1007/978-3-642-03915-7_22.
  25. 25.
    Bifet, A., & Gavaldà, R. (2007). Learning from time-changing data with adaptive windowing. In Siam international data mining conference (pp. 443–448). http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.144.2279.
  26. 26.
    NBAR2 or Next Generation NBAR—Cisco: [Online]. Retrieved 22, June, 2015, http://www.cisco.com/en/US/prod/collateral/iosswrel/ps6537/ps6558/ps6616/qa_c67-697963.html.
  27. 27.
    Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009). The weka data mining software: An update. SIGKDD Explorations, 11(1), 10–18. doi:10.1145/1656274.1656278.
  28. 28.
    Bifet, A., & Kirkby, R. (2009). Data stream mining a practical approach. Citeseer. http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.192.1957.
  29. 29.
    Li, W., Canini, M., Moore, A. W., & Bolla, R. (2009). Efficient application identification and the temporal and spatial stability of classification schema. Computer Networks, 53(6), 790–809. doi:10.1016/j.comnet.2008.11.016.CrossRefGoogle Scholar
  30. 30.
    Williams, N., Zander, S., & Armitage, G. (2006). A preliminary performance comparison of five machine learning algorithms for practical ip traffic flow classification. ACM SIGCOMM Computer Communication Review Journal, 36(5), 5–16. doi:10.1145/1163593.1163596.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Valentín Carela-Español
    • 1
  • Pere Barlet-Ros
    • 1
  • Albert Bifet
    • 2
  • Kensuke Fukuda
    • 3
  1. 1.UPC BarcelonaTechBarcelonaSpain
  2. 2.HUAWEI Noah’s Ark LabShatinHong Kong
  3. 3.National Institute of Informatics (NII)TokyoJapan

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