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

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Part of the Lecture Notes in Networks and Systems book series (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.

Keywords

  • Chi-square test
  • Kolmogorov-smirnov test
  • P2P Traffic
  • Support vector machine
  • SVM

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.

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Correspondence to Mario M. Freire .

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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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