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DOI: 10.14569/IJACSA.2017.080729
PDF

Network Traffic Classification using Machine Learning Techniques over Software Defined Networks

Author 1: Mohammad Reza Parsaei
Author 2: Mohammad Javad Sobouti
Author 3: Seyed Raouf khayami
Author 4: Reza Javidan

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 8 Issue 7, 2017.

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Abstract: Nowadays Internet does not provide an exchange of information between applications and networks, which may results in poor application performance. Concepts such as application-aware networking or network-aware application programming try to overcome these limitations. The introduction of Software-Defined Networking (SDN) opens a path towards the realization of an enhanced interaction between networks and applications. SDN is an innovative and programmable networking architecture, representing the direction of the future network evolution. Accurate traffic classification over SDN is of fundamental importance to numerous other network activities, from security monitoring to accounting, and from Quality of Service (QoS) to providing operators with useful forecasts for long-term provisioning. In this paper, four variants of Neural Network estimator are used to categorize traffic by application. The proposed method is evaluated in the four scenarios: feedforward; Multilayer Perceptron (MLP); NARX (Levenberg-Marquardt) and NARX (Naïve Bayes). These scenarios respectively provide accuracy of 95.6%, 97%, 97% and 97.6%.

Keywords: Software defined networks; openflow; traffic classification; neural network; multilayer perceptron

Mohammad Reza Parsaei, Mohammad Javad Sobouti, Seyed Raouf khayami and Reza Javidan, “Network Traffic Classification using Machine Learning Techniques over Software Defined Networks” International Journal of Advanced Computer Science and Applications(IJACSA), 8(7), 2017. http://dx.doi.org/10.14569/IJACSA.2017.080729

@article{Parsaei2017,
title = {Network Traffic Classification using Machine Learning Techniques over Software Defined Networks},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2017.080729},
url = {http://dx.doi.org/10.14569/IJACSA.2017.080729},
year = {2017},
publisher = {The Science and Information Organization},
volume = {8},
number = {7},
author = {Mohammad Reza Parsaei and Mohammad Javad Sobouti and Seyed Raouf khayami and Reza Javidan}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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