Skip to main content
SpringerLink
Log in

P2P Traffic Identification Using Support Vector Machine and Cuckoo Search Algorithm Combined with Particle Swarm Optimization Algorithm

  • Conference paper
  • First Online:
Frontiers in Internet Technologies (ICoC 2014)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 502))

Included in the following conference series:

Abstract

As peer-to-peer (P2P) technology booms lots of problems arise such as rampant piracy, congestion, low quality etc. Thus, accurate identification of P2P traffic makes great sense for efficient network management. As one of the optimal classifiers, support vector machine (SVM) has been successfully used in P2P traffic identification. However, the performance of SVM is largely dependent on its parameters and the traditional tuning methods are inefficient. In the paper, a novel hybrid method to optimize parameters of SVM based on cuckoo search algorithm combined with particle swarm optimization algorithm is proposed. The first stage of the proposed approach is to tune the best parameters for SVM with training data. Subsequently, the SVM configured with the best parameters is employed to identify P2P traffic. In the end, we demonstrate the effectiveness of our approach on-campus traffic traces. Experimental results indicate that the proposed method outperforms SVM based on genetic algorithm, particle swarm optimization algorithm and cuckoo search algorithm.

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

References

  1. Steinmetz, R., Wehrle, K.: Peer-to-Peer Systems and Applications. Springer, Berlin (2005)

    Book  Google Scholar 

  2. http://en.wikipedia.org/wiki/Peer-to-peer

  3. Constantinou, F., Mavrommatis, P.: Identifying known and unkonwn peer-to-peer traffic. In: Fifth IEEE International Symposium on Network Computing and Application, pp. 93–102. IEEE Press, Cambridge, MA (2006)

    Google Scholar 

  4. Kim, M.-S., Kang, H.-J., Hong, J.W.: Towards Peer-to-Peer Traffic Analysis Using Flows. In: Brunner, Marcus, Keller, Alexander (eds.) DSOM 2003. LNCS, vol. 2867, pp. 55–67. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  5. Bleul, H., Rathgeb, E.P.: A Simple, efficient, and flexible approach to measure multi-protocol peer-to-peer traffic. In: Lorenz, P., Dini, P. (eds.) Networking - ICN 2005. LNCS, vol. 3427. Springer, Heidelberg, pp. 606–616 (2005)

    Google Scholar 

  6. Sen, S., Spatscheck, O., Wang, D.: Accurate, scalable in-network identification of P2P traffic using application signatures. In: WWW2004, pp. 512–521. ACM Press, New York (2004)

    Google Scholar 

  7. Won, Y.J., Park, B.-C, Ju, H.-T.: A hybrid approach for accurate application traffic identification. In: 4th IEEE/IFIP Workshop on End-to-End Monitoring Techniques and Services, 2006, pp. 1–8. IEEE Press, Canada (2006)

    Google Scholar 

  8. Xu, K., Zhang, M., Ye, M., Chiu, D.M., Wu, J.: Identify P2P traffic by inspecting data transfer behavior. Comput. Commun. 33, 1141–1150 (2010)

    Article  Google Scholar 

  9. Keralapura, R., Nucci, A., Chuah, C.N.: A novel self-learning architecture for P2P traffic classification in high speed networks. Comput. Netw. 54, 1055–1068 (2010)

    Article  MATH  Google Scholar 

  10. Liu, B.: A semi-supervised clustering approach for P2P traffic classification. J. Netw. 6(3), 424–431 (2011)

    Google Scholar 

  11. Moore, A.W., Zuev, D.: Internet traffic classification using Bayesian analysis techniques. In: Proceedings of ACM Sigmetrics, pp. 50–60 (2005)

    Google Scholar 

  12. Jin, F., Duan, Y.: A P2P flow identification model based on Bayesian network. In: 7th International Conference Wireless Communications, Networking and Mobile Computing (WiCOM), 2011, pp. 1–4. IEEE Press, Wuhan (2011)

    Google Scholar 

  13. Chen, H., Hu, Z., Ye, Z.:. Research of P2P traffic identification based on neural network. In: IEEE Conference on Computer Network and Multimedia Technology, 2009, pp. 18–20. IEEE Press, Wuhan (2009)

    Google Scholar 

  14. Chen, H., Zhou, X., You, F., Xu, H., Wang, C., et al.: A SVM approach for P2P traffic identification based on multiple traffic mode. J. Netw. 5(11), 1381–1388 (2010)

    Google Scholar 

  15. Zheng, J., Xu, Y.: Identification of network traffic based on support vector machine. In: 2010 3rd International Conference on Advanced Computer Theory and Engineering, pp. 286–291. IEEE Press, Chengdu (2010)

    Google Scholar 

  16. VapNik, V.N.: The Nature of Statistical Learning Theory. Springer, New York (1995)

    Book  MATH  Google Scholar 

  17. Eberhart, R.C., Shi, Y.: Comparison between genetic algorithms and particle swarm optimization. In: Proceedings of 1998 7th Annual Conference on Evoluationary Programming, vol. 1447, pp. 611–616. Springer, Berlin, Heidelberg (1998)

    Google Scholar 

  18. Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, 1995, pp. 1942–1948. IEEE Press, Piscataway (1995)

    Google Scholar 

  19. Esmin, A., Torres, G., Zambroni, A.: A hybrid particle swarm optimization applied to loss power minimization. IEEE Trans. Power Syst. 20(2), 859–866 (2005)

    Article  Google Scholar 

  20. Ting, T.O.: A novel approach for unit commitment problem via an effective hybrid particle swarm optimization. IEEE Trans. Power Syst. 21(1), 11–418 (2006)

    Article  Google Scholar 

  21. Parimala, R.: Feature selection using a novel particle swarm optimization and It’s variants. I.J. Inf. Technol. Comput. Sci. 5, 16–24 (2012)

    Google Scholar 

  22. Yang, X., Deb, S.: Cuckoo search via levy fights. In: 2009 World Congress on Nature and Biologically Inspired Computing (NaBIC 2009), India, pp. 210–215. IEEE Press, Coimbatore (2009)

    Google Scholar 

  23. Civicioglu, P., Besdok, E.: A conceptual comparison of the cuckoo-search, particle swarm optimization, differential evolution and artificial bee colony algorithms. Artif. Intell. Rev. 39, 315–346 (2013)

    Article  Google Scholar 

  24. Wang, F., Luo, L., He, X., Wang, Y.: Hybird optimization algorithm of PSO and Cickoo search. In: IEEE 2011 2nd International Conference on Artificial Intelligence, Management Science and Electronic Commerce (AIMSEC), pp. 1172–1175. IEEE Press, Deng Leng (2011)

    Google Scholar 

  25. http://en.wikipedia.org/wiki/Support_vector_machine

  26. Boser, B.E., Guyon, I.M., Vapnik, V.N.: A training algorithm for optimal margin classifiers. In: 5th Annual ACM Workshop on COLT, pp. 144–152. ACM Press, New York (1992)

    Google Scholar 

Download references

Acknowledgment

This work is supported by Natural Science Foundation of China (No. 41301371, 61170135 and 61202287), the Emergency Management Program for National Natural Science Foundation of China (No. 61440024), Doctor Fund of Hubei University of technology (BSQD13081 BSQD12032).

Author information

Authors and Affiliations

  1. School of Computer Science, Hubei University of Technology, Wuhan, China

    Zhiwei Ye, Mingwei Wang, Chunzhi Wang & Hui Xu

Authors
  1. Zhiwei Ye
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mingwei Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chunzhi Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hui Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhiwei Ye .

Editor information

Editors and Affiliations

  1. Fudan University, Shanghai, China

    Shiyong Zhang

  2. Department of Computer Science, Tsinghua University, Beijing, China

    Ke Xu

  3. Tsinghua University, Beijing, China

    Mingwei Xu

  4. Fudan University, Shanghai, China

    Jie Wu

  5. Fudan University, Shanghai, China

    Chengrong Wu

  6. Fudan University, Shanghai, China

    Yiping Zhong

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Ye, Z., Wang, M., Wang, C., Xu, H. (2015). P2P Traffic Identification Using Support Vector Machine and Cuckoo Search Algorithm Combined with Particle Swarm Optimization Algorithm. In: Zhang, S., Xu, K., Xu, M., Wu, J., Wu, C., Zhong, Y. (eds) Frontiers in Internet Technologies. ICoC 2014. Communications in Computer and Information Science, vol 502. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-46826-5_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-662-46826-5_10

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-46825-8

  • Online ISBN: 978-3-662-46826-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Societies and partnerships

Search

Navigation