Analysis of traffic classification methods in software-defined networks

Authors

  • O.Y. Kulakov National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute” https://orcid.org/0009-0001-1647-0336
  • V.Y. Cherednyk National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”

DOI:

https://doi.org/10.18372/2073-4751.82.20365

Keywords:

SDN, traffic classification, machine learning, neural networks, QoS, artificial intelligence

Abstract

This paper examines traffic classification methods in Software-Defined Networks (SDN). The purpose of this work is to analyze and systematize modern approaches to network traffic classification in the SDN environment.

Traditional classification methods, particularly port-based and Deep Packet Inspection (DPI), are analyzed. Machine learning methods, including SVM, Random Forest, neural networks, and clustering algorithms, along with their advantages and disadvantages, are investigated. The accuracy and efficiency of different classification methods are compared based on published research results.

The authors propose further research on the use of artificial intelligence algorithms in software-defined networks to improve traffic classification accuracy and adaptability to changes in the network environment. The results of comparing different traffic classification methods with a focus on their effectiveness in SDN networks are presented.

References

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Published

2025-08-23

How to Cite

Kulakov, O., & Cherednyk, V. (2025). Analysis of traffic classification methods in software-defined networks. Problems of Informatization and Control, 2(82), 26–32. https://doi.org/10.18372/2073-4751.82.20365

Issue

Vol. 2 No. 82 (2025)

Section

Статті

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