Skip to main content

TCP Traffic Classification Using Relaxed Constraints Support Vector Machines

  • Chapter

Abstract

The traffic classification problem is critical for management, security monitoring, and traffic engineering in computer networks. It has recently taken into consideration by both network operators and researchers. It allows network operators to predict future traffics and detect anomalous behavior and also allows researchers to create traffic models. In this paper, we use a new architecture of support vector machines, namely relaxed constraints support vector machines (RSVMs), to present a traffic classifier that can achieve a high accuracy without any source or destination address or port information. We just use packet length to predict the application class for each flow. RSVM is an efficient and noise-aware implementation of support vector machines that assigns an importance degree to each training sample in such a manner that noisy samples and outliers are given a less degree of importance. Experimental results with UNIBS and AUCKLAND, two sets of traffic traces coming from different topological points in the Internet, show that the proposed classifier is more reliable and has better accuracy.

Keywords

  • Traffic classification
  • Support vector machines
  • Relaxed constraints

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-642-34471-8_11
  • Chapter length: 11 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   109.00
Price excludes VAT (USA)
  • ISBN: 978-3-642-34471-8
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   149.99
Price excludes VAT (USA)
Hardcover Book
USD   169.99
Price excludes VAT (USA)

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Baker, F., Foster, B., Sharp, C.: Cisco architecture for lawful intercept in IP networks. Internet Engineering Task Force, RFC 3924 (2004)

    Google Scholar 

  2. Yuan, R., Li, Z., Guan, X., Xu, L.: An SVM based machine learning method for accurate internet traffic classification. Information Systems Frontiers 12(2), 149–156 (2010)

    CrossRef  Google Scholar 

  3. Este, A., Gringoli, F., Salgarelli, L.: Support Vector Machines for TCP traffic classification. Computer Networks 53, 2476–2490 (2009)

    MATH  CrossRef  Google Scholar 

  4. Nguyen, T., Grenville, A.: A Survey of Techniques for Internet Traffic Classification using Machine Learning. IEEE Communications Surveys & Tutorials 10(4), 56–76 (2008)

    CrossRef  Google Scholar 

  5. Internet Assigned Numbers Authority, IANA (2008), http://www.iana.org/assignments/port-numbers

  6. Carela-Español, V., Barlet-Ros, P., Cabellos-Aparicio, A., Solé-Pareta, J.: Analysis of the impact of sampling on NetFlow traffic classification. Computer Networks 55, 1083–1099 (2011)

    CrossRef  Google Scholar 

  7. Karagiannis, T., Broido, A., Faloutsos, M.: Transport layer identification of P2P traffic. In: Proceedings of ACM SIGCOMM IMC (2004)

    Google Scholar 

  8. Sen, S., Spatscheck, O., Wang, D.: Accurate, scalable in network identification of P2P traffic using application signatures. In: WWW 2004, New York (2004)

    Google Scholar 

  9. Moore, A., Papagiannaki, K.: Toward the accurate identification of network applications. In: Proc. Passive and Active Measurement Workshop (2005)

    Google Scholar 

  10. Williams, N., Zander, S., Armitage, G.: A preliminary performance comparison of five machine learning algorithms for practical IP traffic flow classification. ACM SIGCOMM Comput. Commun. Rev. 36(5) (2006)

    Google Scholar 

  11. Erman, J., Mahanti, A., Arlitt, M., Cohen, I., Williamson, C.: Offline/realtime traffic classification using semi-supervised learning. Performance Evaluation 64, 9–12 (2007)

    CrossRef  Google Scholar 

  12. Auld, T., Moore, A., Gull, S.: Bayesian Neural Networks for Internet Traffic Classification. IEEE Transactions on Neural Networks 18(1), 223–239 (2007)

    CrossRef  Google Scholar 

  13. Vapnik, V.: Statistical Learning Theory. In: Adaptive and Learning Systems for Signal Processing. Communications, and Control. Wiley, New York (1998)

    Google Scholar 

  14. Moore, A., Zuev, D.: Internet traffic classification using Bayesian analysis techniques. Performance Evaluation Review 33, 50–60 (2005)

    CrossRef  Google Scholar 

  15. Li, Z., Yuan, R., Guan, X.: Accurate classification of the internet traffic based on the SVM method. In: International Conference on Communications, pp. 1373–1378 (2007)

    Google Scholar 

  16. Este, A., Gringoli, F., Salgarelli, L.: Support Vector Machines for TCP traffic classification. Computer Networks 53, 2476–2490 (2009)

    MATH  CrossRef  Google Scholar 

  17. Crotti, M., Dusi, M., Gringoli, F., Salgarelli, L.: Traffic classification through simple statistical fingerprinting. ACM SIGCOMM Computer Communication Review 37(1), 5–16 (2007)

    CrossRef  Google Scholar 

  18. Sabzekar, M., Sadoghi Yazdi, H., Naghibzadeh, M.: Relaxed constraints support vector machine. Expert Systems (2011), doi:10.1111/j.1468-0394.2011.00611.x

    Google Scholar 

  19. Liu, P., Chen, P., Jiang, Q., Li, N.: Short-term traffic flow prediction based on rough set and support vector machine. In: International Conference on Fuzzy Systems and Knowledge Discovery, pp. 1526–1530 (2011)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Mostafa Sabzekar .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Sabzekar, M., Moghaddam, M.H.Y., Naghibzadeh, M. (2013). TCP Traffic Classification Using Relaxed Constraints Support Vector Machines. In: Fathi, M. (eds) Integration of Practice-Oriented Knowledge Technology: Trends and Prospectives. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34471-8_11

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-34471-8_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-34470-1

  • Online ISBN: 978-3-642-34471-8

  • eBook Packages: EngineeringEngineering (R0)