Performance evaluation of feature selection and tree-based algorithms for traffic classification

Abstract

The rapid development of smart devices triggers a surge in new traffic and applications. Thus, network traffic classification has become a challenge in modern communications and may be applied to a various range of applications ranging from QoS provisioning to security-related applications. Developing Machine Learning (ML) methods, which can successfully distinguish network applications from each other, is one of the most important tasks. Since ML algorithms are as good as the quality of data, feature selection has become a crucial step in the ML process. Therefore, selecting effective and relevant features for traffic analysis is also another essential issue. In this paper, we are interested in identifying the most relevant features to characterize network traffic. Empirical results indicate that significant input feature selection is important to classify network traffic. Then, a comparative analysis of various Decision Tree-based models (both traditional and recent algorithms) has been conducted with feature selection methods in terms of accuracy, training, and classification time.