EFFECTIVENESS OF CLASSIFICATION OF COMPUTER NETWORK TRAFFIC ANALYSIS APPLICATIONS USING MACHINE LEARNING METHODS

Abstract

The article considers the relevance of network traffic research, which is explained by the introduction of computer networks into the life of every person. The rapid development of computer networks has caused increased attention to issues of quality and reliability of their work. The study of computer network traffic analysis is relevant for ensuring the quality of wired and wireless communication, information resources and information search. The study of network traffic indicates the need for its classification to display network data into traffic classes and application types. It is advisable to use machine learning methods, which facilitates the adaptation of the system to constantly changing Internet resources, taking into account the specifics of network traffic. Network traffic analysis indicates that to successfully classify network traffic, all traffic passing through the network must be stored or processed. It was determined that the classification of network traffic is an important task in the field of computer networks. The purpose of network traffic classification is to map the flow of network data into specific application types or traffic classes. An overview of network traffic classification methods was conducted. The results of a comparison of modern approaches to the classification of network traffic are given. Despite the range of methods, the classification of network traffic is still at the stage of development. It should be noted that modern methods, in particular based on machine learning, prove effective results.