Internet Traffic Classification Using Machine Learning Techniques

Internet traffic classification is a fundamental task for network services and management. There are good machine learning models to identify the class of traffic. However, finding the most discriminating features to have efficient models remains essential. In this paper, we use interpretable machine learning algorithms such as random forest and gradient boosting to find the most discriminating features for internet traffic classification. This paper aims to overcome these challenges by proposing machine learning classification mechanism.