Performance Evaluation for Four Supervised Classifiers in Internet Traffic Classification

  • Alhamza MuntherEmail author
  • Imad J. Mohammed
  • Mohammed Anbar
  • Anwer Mustafa HilalEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1132)


Supervised machine learning is a method to predict a class for labeled data, to improve different QoS metrics of several scopes such as educational, industrial and medical etc. This paper presents in-deep study focusing on four supervised classifiers were used widely to distinguish or categorize TCP/IP network traffic model and how they can be employed, these four are Naïve Bayes, Probabilistic Neural Network, Support Vector Machine and C4.5 decision tree. The classifiers are compared with regard to three significant metrics namely classification accuracy, classification speed and memory consumption. The implementation results of simulation and comparisons show that C4.5 decision tree introduce best results with high accuracy up to 99.6% using the benchmark dataset consist of 24863 packets compared to the rest three tested classifiers.


Network machine learning Supervised learning Internet traffic engineering Internet traffic classification 


  1. 1.
    Callado, A., Kamienski, C., Fernandes, S., Sadok, D.: A survey on internet traffic identification and classification (2009)CrossRefGoogle Scholar
  2. 2.
    Munther, A., et al.: Active build-model random forest method for network traffic classification. Int. J. Eng. Technol. (IJET) 6(2), 796–804 (2014)Google Scholar
  3. 3.
    Karagiannis, T., Broido, A., Faloutsos, M.: Transport layer identification of P2P traffic. In: Proceedings of the 4th ACM SIGCOMM Conference on Internet Measurement, pp. 121–134 (2004)Google Scholar
  4. 4.
    Munther, A., et al.: Network traffic classification—a comparative study of two common decision tree methods: C4.5 and random forest. In: 2014 2nd International Conference on Electronic Design (ICED). IEEE (2014)Google Scholar
  5. 5.
    Munther, A., Othman, R.R., Alsaadi, A.S., Anbar, M.: A performance study of hidden Markov model and random forest in internet traffic classification. In: Kim, K., Joukov, N. (eds.) Information Science and Applications (ICISA) 2016. LNEE, vol. 376, pp. 319–329. Springer, Singapore (2016). Scholar
  6. 6.
    Sen, S., Spatscheck, O., Wang, D.: Accurate, scalable in-network identification of P2P traffic using application signatures. In: Proceedings of the 13th International Conference on World Wide Web, pp. 512–521 (2004)Google Scholar
  7. 7.
    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, 5–16 (2006)CrossRefGoogle Scholar
  8. 8.
    Moore, A.W., Zuev, D.: Internet traffic classification using bayesian analysis techniques. In: ACM SIGMETRICS Performance Evaluation Review, pp. 50–60 (2005)CrossRefGoogle Scholar
  9. 9.
    Liu, Y., Li, W., Li, Y.: Network traffic classification using k-means clustering. In: Second International Multi-Symposiums on Computer and Computational Sciences, IMSCCS 2007, pp. 360–365 (2007)Google Scholar
  10. 10.
    Li, Z., Yuan, R., Guan, X.: Accurate classification of the internet traffic based on the svm method. In: IEEE International Conference on Communications, ICC 2007, pp. 1373–1378 (2007)Google Scholar
  11. 11.
    Fu, L., Tang, B., Yuan, D.: The study of traffic classification methods based on C4.5 algorithm (2012)Google Scholar
  12. 12.
    IANA: Internet Assigned Numbers AuthorityGoogle Scholar
  13. 13.
    Dreger, H., Feldmann, A., Mai, M., Paxson, V., Sommer, R.: Dynamic application-layer protocol analysis for network intrusion detection. In: USENIX Security Symposium, pp. 257–272 (2006)Google Scholar
  14. 14.
    Moore, A.W., Papagiannaki, K.: Toward the accurate identification of network applications. In: Dovrolis, C. (ed.) PAM 2005. LNCS, vol. 3431, pp. 41–54. Springer, Heidelberg (2005). Scholar
  15. 15.
    Erman, J., Mahanti, A., Arlitt, M.: Qrp05-4: internet traffic identification using machine learning. In: Global Telecommunications Conference, GLOBECOM 2006, pp. 1–6. IEEE (2006)Google Scholar
  16. 16.
    Li, W., Moore, A.W.: A machine learning approach for efficient traffic classification. In: 15th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems, MASCOTS 2007, pp. 310–317 (2007)Google Scholar
  17. 17.
    Hao, S., et al.: Improved SVM method for internet traffic classification based on feature weight learning. In: 2015 International Conference on Control, Automation and Information Sciences (ICCAIS). IEEE (2015)Google Scholar
  18. 18.
    Sivaprasad, A., et al.: Machine learning based traffic classification using statistical analysis. Int. J. Recent Innov. Trends Comput. Commun. 6(3), 187–191 (2018)MathSciNetGoogle Scholar
  19. 19.
    Kim, H., Claffy, K.C., Fomenkov, M., Barman, D., Faloutsos, M., Lee, K.: Internet traffic classification demystified: myths, caveats, and the best practices. In: Proceedings of the 2008 ACM CoNEXT Conference, p. 11 (2008)Google Scholar
  20. 20.
    Haffner, P., Sen, S., Spatscheck, O., Wang, D.: ACAS: automated construction of application signatures. In: Proceedings of the 2005 ACM SIGCOMM Workshop on Mining Network Data, pp. 197–202 (2005)Google Scholar
  21. 21.
    Ma, J., Levchenko, K., Kreibich, C., Savage, S., Voelker, G.M.: Unexpected means of protocol inference. In: Proceedings of the 6th ACM SIGCOMM Conference on Internet Measurement, pp. 313–326 (2006)Google Scholar
  22. 22.
    Karagiannis, T., Papagiannaki, K., Faloutsos, M.: BLINC: multilevel traffic classification in the dark. In: ACM SIGCOMM Computer Communication Review, pp. 229–240 (2005)CrossRefGoogle Scholar
  23. 23.
    Clarke, B.S., Fokoue, E., Zhang, H.H.: Principles and Theory for Data Mining and Machine Learning. Springer, Heidelberg (2009). Scholar
  24. 24.
    Mitchell, T.M.: Machine Learning. WCB. McGraw-Hill, Boston (1997)zbMATHGoogle Scholar
  25. 25.
    Fagan, T.J.: Letter: nomogram for Bayes theorem. New Engl. J. Med. 293, 257 (1975)Google Scholar
  26. 26.
    Bennett, K.P., Campbell, C.: Support vector machines: hype or hallelujah? ACM SIGKDD Explor. Newsl. 2, 1–13 (2000)CrossRefGoogle Scholar
  27. 27.
    Breiman, L.: Random forests. Mach. Learn. 45, 5–32 (2001)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.IT Department, Sur College of Applied ScienceMinistry of Higher EducationSurSultanate of Oman
  2. 2.Department of Computer Science – College of ScienceUniversity of BaghdadBaghdadIraq
  3. 3.National Advanced IPv6 Centre of ExcellenceUniversiti Sains MalaysiaPenangMalaysia
  4. 4.Department of Computer and Self DevelopmentPrince Sattam Bin Abdulaziz UniversityKharjKingdom of Saudi Arabia

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