Software defined networking based network traffic classification using machine learning techniques

Abstract

The classification of network traffic has become increasingly crucial due to the rapid growth in the number of internet users. Conventional approaches, such as identifying traffic based on port numbers and payload inspection are becoming ineffective due to the dynamic and encrypted nature of modern network traffic. A number of researchers have implemented Software Defined Networking (SDN) based traffic classification using Machine Learning (ML) and Deep Learning (DL) models. However, the studies had various limitations such as encrypted traffic detection, payload inspection, poor detection accuracy, and challenges with testing models both in offline and real-time traffic modes. ML models together with SDN are adopted nowadays to enhance classification performance. In this paper, both supervised (Logistic Regression, Decision Tree, Random Forest, AdaBoost, and Support Vector Machine) and unsupervised (K-means clustering) ML models were used to classify Domain Name System (DNS), Telnet, Ping, and Voice traffic flows simulated using the Distributed Internet Traffic Generator (D-ITG) tool. The use of this tool effectively manages and classifies traffic types based on their application. The study discussed the dataset used, model selection, implementation of the model, and implementation techniques (such as pre-processing, feature extraction, ML algorithm, and model evaluation metrics). The proposed model in SDN was implemented in Mininet for designing the network architecture and generating network traffic. Anaconda Python environment was utilized for traffic classification using various ML techniques. Among the models tested, the Decision Tree supervised learning achieved the highest accuracy of 99.81%, outperforming other supervised and unsupervised learning algorithms. These results indicate that the integration of ML with SDN provides an efficient classification method for identifying and accurately classifying both offline and real-time network traffic, enhanced quality of service (QoS), detection of encrypted packets, deep packet inspection and management.