Traffic Data Classification using Machine Learning Algorithms in SDN Networks

Jungmin Kwon, Daeun Jung, Hyunggon Park

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

13 Scopus citations


As an efficient approach to proactively monitoring network dynamics, automatically analyzing network data, and predicting network usage, machine learning has been widely deployed. This enables the networks to be efficiently and autonomously coped with in SDN/NFV environment. In particular, network intelligent technology can be adopted into the infrastructure management, network operations, and service assurance. In this paper, we study the automatic network data classification based on machine learning, where several machine learning algorithms are deployed to automatically classify real network traffic data collected from ONOS (Open Network Operating System) platform. From the experiment results with simple network topology, we conclude that machine learning algorithms can effectively classify the network traffic data. However, it is also observed machine algorithms may only show a limited performance in practice if they are blindly deployed. This is because there exists not only the data that needs to be delivered to the receivers but also the data required for network maintenance in a real network system. Therefore, it is essential to develop machine learning algorithms that explicitly consider the characteristics of real network traffic data in target network scenarios.

Original languageEnglish
Title of host publicationICTC 2020 - 11th International Conference on ICT Convergence
Subtitle of host publicationData, Network, and AI in the Age of Untact
PublisherIEEE Computer Society
Number of pages3
ISBN (Electronic)9781728167589
StatePublished - 21 Oct 2020
Event11th International Conference on Information and Communication Technology Convergence, ICTC 2020 - Jeju Island, Korea, Republic of
Duration: 21 Oct 202023 Oct 2020

Publication series

NameInternational Conference on ICT Convergence
ISSN (Print)2162-1233
ISSN (Electronic)2162-1241


Conference11th International Conference on Information and Communication Technology Convergence, ICTC 2020
Country/TerritoryKorea, Republic of
CityJeju Island

Bibliographical note

Funding Information:
This work was supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2019-0-00024, Supervised Agile Machine Learning Techniques for Network Automation based on Network Data Analytics Function) and supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2020R1A2B5B01002528).

Publisher Copyright:
© 2020 IEEE.


  • automatic network data classification
  • Machine learning
  • ONOS
  • supervised learning


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