Traffic Classification in Software-Defined Networking Using Genetic Programming Tools

Spiridoula V. Margariti, Ioannis G. Tsoulos (), Evangelia Kiousi and Eleftherios Stergiou
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Spiridoula V. Margariti: Department of Informatics and Telecommunications, University of Ioannina, 45110 Ioannina, Greece
Ioannis G. Tsoulos: Department of Informatics and Telecommunications, University of Ioannina, 45110 Ioannina, Greece
Evangelia Kiousi: Department of Informatics and Telecommunications, University of Ioannina, 45110 Ioannina, Greece
Eleftherios Stergiou: Department of Informatics and Telecommunications, University of Ioannina, 45110 Ioannina, Greece

Future Internet, 2024, vol. 16, issue 9, 1-17

Abstract: The classification of Software-Defined Networking (SDN) traffic is an essential tool for network management, network monitoring, traffic engineering, dynamic resource allocation planning, and applying Quality of Service (QoS) policies. The programmability nature of SDN, the holistic view of the network through SDN controllers, and the capability for dynamic adjustable and reconfigurable controllersare fertile ground for the development of new techniques for traffic classification. Although there are enough research works that have studied traffic classification methods in SDN environments, they have several shortcomings and gaps that need to be further investigated. In this study, we investigated traffic classification methods in SDN using publicly available SDN traffic trace datasets. We apply a series of classifiers, such as MLP (BFGS), FC2 (RBF), FC2 (MLP), Decision Tree, SVM, and GENCLASS, and evaluate their performance in terms of accuracy, detection rate, and precision. Of the methods used, GenClass appears to be more accurate in separating the categories of the problem than the rest, and this is reflected in both precision and recall. The key element of the GenClass method is that it can generate classification rules programmatically and detect the hidden associations that exist between the problem features and the desired classes. However, Genetic Programming-based techniques require significantly higher execution time compared to other machine learning techniques. This is most evident in the feature construction method where at each generation of the genetic algorithm, a set of learning models is required to be trained to evaluate the generated artificial features.

Keywords: software-defined networking; genetic algorithms; optimization; neural networks; genetic programming (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2024
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