Features of Application of Machine Learning Methods for Classification of Network Traffic (Features, Advantages, Disadvantages)

The problem of traffic classification has been studied for a long time: a significant amount of research work has been devoted to its analysis and the search for effective solutions under various conditions and limitations, including in recent years. Classification of network traffic is widely required for many network management tasks, such as stream prioritization, traffic shaping/control, and diagnostic monitoring. When solving network management problems, problems arise such as characterization and modelling of workload, capacity planning, route preparation, and accurate identification of network traffic. This article provides a description of network traffic and an overview of machine learning methods for classifying it, such as Naive Bayes, C4.5, AdaBoost and Random Forest, their advantages, disadvantages and features.