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Impact of Self C Parameter on SVM-based Classification of Encrypted Multimedia Peer-to-Peer Traffic

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Advanced Information Networking and Applications (AINA 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 449))

Abstract

Home users are increasingly acquiring, at lower prices, electronic devices such as video cameras, portable audio players, smartphones, and video game devices, which are all interconnected through the Internet. This increase in digital equipment ownership induces a massive production and sharing of multimedia content between these users. The supervised learning machine method Support Vector Machine (SVM) is vastly used in classification. It is capable of recognizing patterns of samples of predefined classes and supports multi-class classification. The purpose of this article is to explore the classification of multimedia P2P traffic using SVMs. To obtain relevant results, it is necessary to properly adjust the so-called Self C parameter. Our results show that SVM with linear kernel leads to the best classification results of P2P video with an F-Measure of 99% for C parameter ranging from 10 to 70 and to the best classification results of P2P file-sharing with an F-Measure of 98% for C parameter ranging from 30 to 70. We also compare these results with the ones obtained with Kolmogorov-Smirnov (KS) tests and Chi-square tests. It is shown that SVM with linear kernel leads to a better classification performance than KS and chi-square tests, which reached an F-Measure of 67% and 70% for P2P file-sharing and P2P video, respectively, for KS test, and reached an F-Measure of 85% for both P2P file-sharing and P2P video for chi-square test. Therefore, SVM with linear kernel and suitable values for the Self C parameter can be a good choice for identifying encrypted multimedia P2P traffic on the Internet.

This work was financed by CAPES (Brazilian Federal Agency for Support and Evaluation of Graduate Education) within the Ministry of Education of Brazil under a scholarship supported by the International Cooperation Program CAPES/COFECUB - Project 9090-13-4/2013 at the University of Beira Interior. This work is also funded by FCT/MCTES through national funds and, when applicable, co-funded by EU funds under the project UIDB/50008/2020 and by FCT/COMPETE/FEDER under the project SECURIoTESIGN with reference number POCI-01-0145-FEDER-030657, and by operation Centro-01–0145-FEDER-000019 - C4 - Centro de Competêencias em Cloud Computing, co-funded by the European Regional Development Fund (ERDF) through the Programa Operacional Regional do Centro (Centro 2020), in the scope of the Sistema de Apoio á Investigação Científica e Tecnológica - Programas Integrados de IC&DT.

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Notes

  1. 1.

    https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html.

  2. 2.

    https://pypi.org/project/psrecord.

References

  1. Sandvine, The global internet phenomena report covid-19 spotlight, 7 May 2020. https://www.sandvine.com/covid-internet-spotlight-report?hsCtaTracking=69c3275d-0a47-4def-b46d-506266477a50%7Cac52173f-34c1-42df-8469-a091e7219e7a

  2. Yang, J., Yuan, L., Dong, C., Cheng, G., Ansari, N., Kato, N.: On characterizing peer-to-peer streaming traffic. IEEE J. Select. Areas Commun. 31(9), 175–188 (2013)

    Article  Google Scholar 

  3. Pal, K., Govil, M.C., Ahmed, M., Chawla, T.: A survey on adaptive multimedia streaming. In: Recent Trends in Communication Networks, pp. 185–202, IntechOpen (2019)

    Google Scholar 

  4. Rezvani, S., Wang, X., Pourpanah, F.: Intuitionistic fuzzy twin support vector machines. IEEE Trans. Fuzzy Syst. 27(11), 2140–2151 (2019)

    Article  Google Scholar 

  5. Tang, J., Chen, X., Hu, Z., Zong, F., Han, C., Li, L.: Traffic flow prediction based on combination of support vector machine and data denoising schemes. Phys. Stat. Mech. Appl. 534, 120642 (2019)

    Google Scholar 

  6. Syarif, I., Prugel-Bennett, A., Wills, G.: SVM parameter optimization using grid search and genetic algorithm to improve classification performance. Telkomnika 14(4), 1502 (2016)

    Article  Google Scholar 

  7. Mavroforakis, M.E., Theodoridis, S.: A geometric approach to support vector machine (SVM) classification. IEEE Trans. Neural Netw. 17(3), 671–682 (2006)

    Article  Google Scholar 

  8. Aggarwal, R., Singh, N.: A new hybrid approach for network traffic classification using svm and naïve bayes algorithm. Int. J. Comput. Sci. Mobile Comput 6, 168–174 (2017)

    Google Scholar 

  9. Yuan, R., Li, Z., Guan, X., Xu, L.: An SVM-based machine learning method for accurate internet traffic classification. Inf. Syst. Front. 12(2), 149–156 (2010)

    Article  Google Scholar 

  10. Akinyelu, A.A., Ezugwu, A.E.: Nature inspired instance selection techniques for support vector machine speed optimization. IEEE Access 7, 154581–154599 (2019)

    Article  Google Scholar 

  11. Xiao, J.: SVM and KNN ensemble learning for traffic incident detection. Phys. Stat. Mech. Appl. 517, 29–35 (2019)

    Article  Google Scholar 

  12. Sankaranarayanan, S., Mookherji, S.: SVM-based traffic data classification for secured IoT-based road signaling system. Int. J. Intell. Inf. Technol. (IJIIT) 15(1), 22–50 (2019)

    Article  Google Scholar 

  13. Han, W., Xue, J., Yan, H.: Detecting anomalous traffic in the controlled network based on cross entropy and support vector machine. IET Inf. Secur. 13(2), 109–116 (2019)

    Article  Google Scholar 

  14. Luo, C., et al.: Short-term traffic flow prediction based on least square support vector machine with hybrid optimization algorithm. Neural Process. Lett. 50(3), 2305–2322 (2019)

    Article  Google Scholar 

  15. Şentaş, A., et al.: Performance evaluation of support vector machine and convolutional neural network algorithms in real-time vehicle type and color classification. Evol. Intell. 13(1), 83–91 (2018). https://doi.org/10.1007/s12065-018-0167-z

    Article  Google Scholar 

  16. Aamir, M., Zaidi, S.M.A.: Clustering based semi-supervised machine learning for DDOS attack classification. J. King Saud Univ. Comput. Inf. Sci (2019)

    Google Scholar 

  17. Raikar, M.M., Meena, S., Mulla, M.M., Shetti, N.S., Karanandi, M.: Data traffic classification in software defined networks (SDN) using supervised-learning. Procedia Comput. Sci. 171, 2750–2759 (2020)

    Article  Google Scholar 

  18. Budiman, F.: SVM-RBF parameters testing optimization using cross validation and grid search to improve multiclass classification. Sci. Vis. 11(1), 80–90 (2019)

    MathSciNet  Google Scholar 

  19. Singla, M., Shukla, K.K.: Robust statistics-based support vector machine and its variants: a survey. Neural Comput. Appl. 32(15), 11173–11194 (2019). https://doi.org/10.1007/s00521-019-04627-6

    Article  Google Scholar 

  20. Marwala, T.: Support vector machines. In: Handbook of Machine Learning, Wold Scientific, pp. 97–112 (2018)

    Google Scholar 

  21. Zhu, Y., Zheng, Y.: Traffic identification and traffic analysis based on support vector machine. Neural Comput. Appl. 32(7), 1903–1911 (2019). https://doi.org/10.1007/s00521-019-04493-2

    Article  Google Scholar 

  22. Fan, Z., Liu, R.: Investigation of machine learning based network traffic classification. In : 2017 International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6, IEEE (2017)

    Google Scholar 

  23. Duan, K.-B., Keerthi, S.S.: Which is the best multiclass SVM method? an empirical study. In: International workshop on multiple classifier systems, pp. 278–285, Springer (2005)

    Google Scholar 

  24. Velasco-Mata, J., Fidalgo, E., González-Castro, V., Alegre, E., Blanco-Medina, P.: Botnet detection on TCP traffic using supervised machine learning. In: International Conference on Hybrid Artificial Intelligence Systems, pp. 444–455, Springer (2019)

    Google Scholar 

  25. Pedregosa, F., et al.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2011)

    MathSciNet  MATH  Google Scholar 

  26. Vanice-ufmt. https://github.com/Vanice-ufmt/Codigo, 30 October 2020

  27. Reports, C.: 28 April 2020. https://scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html

  28. Cunha, V.C., Zavala, A.A., Inácio, P.R., Magoni, D., Freire, M.M.: Classification of encrypted internet traffic using kullback-leibler divergence and euclidean distance. In: International Conference on Advanced Information Networking and Applications, pp. 883–897, Springer (2020)

    Google Scholar 

  29. Han, J., Pei, J., Kamber, M.: Data Mining: Concepts and Techniques. Elsevier (2011)

    Google Scholar 

  30. Neto, M., Gomes, J.V., Freire, M.M., Inácio, P.R.: Real-time traffic classification based on statistical tests for matching signatures with packet length distributions. In: 2013 19th IEEE Workshop on Local & Metropolitan Area Networks (LANMAN), pp. 1–6. IEEE (2013)

    Google Scholar 

  31. Pandis, N.: The chi-square test. Am. J. Orthod. Dentofac. Orthop. 150(5), 898–899 (2016)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Universidade Federal de Mato Grosso, Cuiabá, Brazil

    Vanice Canuto Cunha

  2. Instituto de Telecomunicações, Universidade da Beira Interior, Covilhã, Portugal

    Vanice Canuto Cunha, Pedro R. M. Inácio & Mario M. Freire

  3. LaBRI-CNRS, Université de Bordeaux, Talence, France

    Damien Magoni

Authors
  1. Vanice Canuto Cunha
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  2. Damien Magoni
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  3. Pedro R. M. Inácio
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  4. Mario M. Freire
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Corresponding author

Correspondence to Mario M. Freire .

Editor information

Editors and Affiliations

  1. Department of Information and Communication Engineering, Fukuoka Institute of Technology, Fukuoka, Japan

    Leonard Barolli

  2. University of Technology Sydney, Sydney, NSW, Australia

    Farookh Hussain

  3. Faculty of Business Administration, Rissho University, Tokyo, Japan

    Tomoya Enokido

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Cunha, V.C., Magoni, D., Inácio, P.R.M., Freire, M.M. (2022). Impact of Self C Parameter on SVM-based Classification of Encrypted Multimedia Peer-to-Peer Traffic. In: Barolli, L., Hussain, F., Enokido, T. (eds) Advanced Information Networking and Applications. AINA 2022. Lecture Notes in Networks and Systems, vol 449. Springer, Cham. https://doi.org/10.1007/978-3-030-99584-3_16

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