TY - GEN
T1 - Automatic Classification of Network Traffic Data based on Deep Learning in ONOS Platform
AU - Kwon, Jungmin
AU - Lee, Jungjin
AU - Yu, Miseon
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 - Machine learning has been deployed in networks for automatically analyzing network data, proactively monitoring network dynamics, and predicting network resource availability. This becomes one of key technologies for efficient and autonomous network management in particular for software defined networks (SDN) environments. Especially, deep learning has brought recent breakthrough in machine learning algorithm as it can extract features based on artificial neural networks from data. In this paper, we study the deployment of deep neural network (DNN) for network traffic data classification, where DNN is deployed to automatically classify real network traffic data collected from ONOS (Open Network Operating System) platform. From the experiment results with simple network topologies, we conclude that DNN can be a potential approach to effective network packet classification. Moreover, it is confirmed that a deployment of DNN for a real network traffic data classification should consider not only the data packets that are intended to be delivered but also data packets required to maintain networks, as the classification performance of DNN significantly depends on the network traffic data.
AB - Machine learning has been deployed in networks for automatically analyzing network data, proactively monitoring network dynamics, and predicting network resource availability. This becomes one of key technologies for efficient and autonomous network management in particular for software defined networks (SDN) environments. Especially, deep learning has brought recent breakthrough in machine learning algorithm as it can extract features based on artificial neural networks from data. In this paper, we study the deployment of deep neural network (DNN) for network traffic data classification, where DNN is deployed to automatically classify real network traffic data collected from ONOS (Open Network Operating System) platform. From the experiment results with simple network topologies, we conclude that DNN can be a potential approach to effective network packet classification. Moreover, it is confirmed that a deployment of DNN for a real network traffic data classification should consider not only the data packets that are intended to be delivered but also data packets required to maintain networks, as the classification performance of DNN significantly depends on the network traffic data.
KW - automatic network data classification
KW - deep neural network
KW - Machine learning
KW - ONOS
UR - http://www.scopus.com/inward/record.url?scp=85098996579&partnerID=8YFLogxK
U2 - 10.1109/ICTC49870.2020.9289257
DO - 10.1109/ICTC49870.2020.9289257
M3 - Conference contribution
AN - SCOPUS:85098996579
T3 - International Conference on ICT Convergence
SP - 1028
EP - 1030
BT - ICTC 2020 - 11th International Conference on ICT Convergence
PB - IEEE Computer Society
T2 - 11th International Conference on Information and Communication Technology Convergence, ICTC 2020
Y2 - 21 October 2020 through 23 October 2020
ER -