Open AccessA Survey of Network Traffic Classification
Author(s) -
Aleksandr Igorevich Getman,
Мария Кирилловна Иконникова
Publication year - 2020
Publication title -
trudy instituta sistemnogo programmirovaniâ ran/trudy instituta sistemnogo programmirovaniâ
Language(s) - English
Resource type - Journals
eISSN - 2220-6426
pISSN - 2079-8156
DOI - 10.15514/ispras-2020-32(6)-11
Subject(s) - task (project management) , computer science , relation (database) , field (mathematics) , machine learning , artificial intelligence , feature (linguistics) , selection (genetic algorithm) , feature selection , traffic classification , data mining , engineering , world wide web , mathematics , linguistics , philosophy , the internet , systems engineering , pure mathematics
This survey is dedicated to the task of network traffic classification, particularly to the use of machine learning algorithms in this task. The survey begins with the description of the task, its variations and possible uses in real-world problems. It then proceeds to the description of the methods used historically to solve this task, their limitations and evolution of traffic making machine learning the main way to solve the problem. Then the most popular machine learning algorithms used in this task are described, with the examples of research papers, providing the insight into their advantages and disadvantages in relation to this field. The task of feature selection is discussed, followed by the more global problem of acquiring the suitable dataset to use in the research; some examples of such popular datasets and their descriptions are provided. The paper concludes with the outline of the current problems in this research area to be solved.