Abstract:

In computer networks, diverse applications generate network traffic with different characteristics. Network traffic classification is significant to manage networks better, improve service quality and ensure security. Software-Defined Networks (SDN) provides flexible and adaptable techniques for traffic classification with its programmable structure. SDN flows naturally exhibit particular characteristics of network applications and protocols. Therefore, it can be said that SDN can present significant opportunities in traffic classification using machine learning. This study proposes a traffic classification approach using machine learning models in SDN. In this study, DNS, Telnet, Ping and Voice traffic flows were created on the SDN using the Distributed Internet Traffic Generator (D-ITG) tool. Twelve-attributes representing these traffic flows (the number of packets transmitted, average transmission time, the number of instantly transmitted packets, etc.) were determined, and over the SDN controller in the physical network, a real-time data set was created by collecting data depending on the attributes. Later, the performance of k Nearest Neighbor (k-NN), Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), Decision Tree (DT) and Naive Bayes (NB) machine learning models were tested for traffic classification on this data set. When the k-NN model was tested on this real-time data set, its classification accuracy was obtained as the maximum with 99.4%. Therefore this model has been determined as a machine learning giving the highest classification performance with the lowest cost flow attributes in traffic classification in SDN.

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