Network traffic analyzing algorithms on the basis of machine learning methods

Abstract

Traffic analysis systems are widely used in monitoring the network activity of users or a specific user and restricting client access to certain types of services (VPN, HTTPS) which makes content analysis impossible. Algorithms for classifying encrypted traffic and detecting VPN traffic are proposed. Three algorithms for constructing classifiers are considered - MLP, RFT and KNN. The proposed classifier demonstrates recognition accuracy on a test sample up to 80%. The MLP, RFT and KNN algorithms had almost identical performance in all experiments. It was also found that the proposed classifiers work better when the network traffic flows are generated using short values of the time parameter (timeout). The novelty lies in the development of network traffic analysis algorithms based on a neural network, differing in the method of selection, generation and selection of features, which allows to classify the existing traffic of protected connections of selected users according to a predetermined set of categories.