Abstract
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.
Original language | English |
---|---|
Title of host publication | ICTC 2020 - 11th International Conference on ICT Convergence |
Subtitle of host publication | Data, Network, and AI in the Age of Untact |
Publisher | IEEE Computer Society |
Pages | 1028-1030 |
Number of pages | 3 |
ISBN (Electronic) | 9781728167589 |
DOIs | |
State | Published - 21 Oct 2020 |
Event | 11th International Conference on Information and Communication Technology Convergence, ICTC 2020 - Jeju Island, Korea, Republic of Duration: 21 Oct 2020 → 23 Oct 2020 |
Publication series
Name | International Conference on ICT Convergence |
---|---|
Volume | 2020-October |
ISSN (Print) | 2162-1233 |
ISSN (Electronic) | 2162-1241 |
Conference
Conference | 11th International Conference on Information and Communication Technology Convergence, ICTC 2020 |
---|---|
Country/Territory | Korea, Republic of |
City | Jeju Island |
Period | 21/10/20 → 23/10/20 |
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.
Keywords
- automatic network data classification
- deep neural network
- Machine learning
- ONOS