TY - GEN
T1 - Traffic Data Classification using Machine Learning Algorithms in SDN Networks
AU - Kwon, Jungmin
AU - Jung, Daeun
AU - Park, Hyunggon
N1 - 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.
PY - 2020/10/21
Y1 - 2020/10/21
N2 - 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.
AB - 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.
KW - automatic network data classification
KW - Machine learning
KW - ONOS
KW - supervised learning
UR - http://www.scopus.com/inward/record.url?scp=85098969576&partnerID=8YFLogxK
U2 - 10.1109/ICTC49870.2020.9289174
DO - 10.1109/ICTC49870.2020.9289174
M3 - Conference contribution
AN - SCOPUS:85098969576
T3 - International Conference on ICT Convergence
SP - 1031
EP - 1033
BT - ICTC 2020 - 11th International Conference on ICT Convergence
PB - IEEE Computer Society
Y2 - 21 October 2020 through 23 October 2020
ER -