A Model for Classifying Network Traffic Using Reinforcment Learning
P. Cheskidov, Vitalii Varkentin, A.O. Shults · 2020 International Multi-Conference on Industrial Engineering and Modern Technologies (FarEastCon) · 2020
Recently, more attention has been paid to the task of traffic classification. This is due to the fact that, in the era of active development of digital systems, it is necessary to pay particular attention to the issues of application security and the protection of user data). There are many different algorithms and approaches with their own advantages and disadvantages, which differ from each other in processing speed, field of applicability and accuracy of the final results. One of the most actively developing areas at the moment is the use of various machine learning algorithms. This direction also has some disadvantages. In particular, there is a problem in the classification of completely new traffic, which was not during training, the need for periodic retraining when changing the characteristics of network protocols or other important parameters for any particular task. This article describes a network traffic classification model using reinforcement learning methods.