Towards the Deployment of Machine Learning Solutions in Network Traffic Classification: A Systematic Survey

By Fannia Pacheco, Ernesto Exposito, Mathieu Gineste, Cédric Baudoin, José Aguilar. IEEE Communications Surveys and Tutorials, 2019

337 citations recorded by the source corpus.

Traffic analysis is a compound of strategies intended to find relationships, patterns, anomalies, and misconfigurations, among others things, in Internet traffic. In particular, traffic classification is a subgroup of strategies in this field that aims at identifying the application’s name or type of Internet traffic. Nowadays, traffic classification has become a challenging task due to the rise of new technologies, such as traffic encryption and encapsulation, which decrease the performance of classical traffic classification strategies. Machine learning (ML) gains interest as a new direction in this field, showing signs of future success, such as knowledge extraction from encrypted traffic, and more accurate Quality of Service management. ML is fast becoming a key tool to build traffic classification solutions in real network traffic scenarios; in this sense, the purpose of this investigation is to explore the elements that allow this technique to work in the traffic classification field. Therefore, a systematic review is introduced based on the steps to achieve traffic classification by using ML techniques. The main aim is to understand and to identify the procedures followed by the existing works to achieve their goals. As a result, this survey paper finds a set of trends derived from the analysis performed on this domain; in this manner, the authors expect to outline future directions for ML-based traffic classification.

Subfields: Artificial Intelligence, Computer Networks and Communications, Signal Processing

View Paper: https://www.semanticscholar.org/paper/2a11a15ec7a1909f8497911a7ed40a89cab5370d

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Citations in the corpus

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks (2019, methodology): “[37] surveyed the ML techniques based on several steps to achieve”
Challenges in Deploying Machine Learning: A Survey of Case Studies (2020, background): “[100], which surveys applications of machine learning to network traic classiication, with tasks such as protocol identiication or attack detection.”