Automatic Classification of Network Traffic Data based on Deep Learning in ONOS Platform

Jungmin Kwon; Jungjin Lee; Miseon Yu; Hyunggon Park

doi:10.1109/ICTC49870.2020.9289257

Automatic Classification of Network Traffic Data based on Deep Learning in ONOS Platform

Jungmin Kwon, Jungjin Lee, Miseon Yu, Hyunggon Park

Electronic and Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

6 Scopus citations

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	https://doi.org/10.1109/ICTC49870.2020.9289257
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

Access to Document

10.1109/ICTC49870.2020.9289257

Cite this

Kwon, J., Lee, J., Yu, M., & Park, H. (2020). Automatic Classification of Network Traffic Data based on Deep Learning in ONOS Platform. In ICTC 2020 - 11th International Conference on ICT Convergence: Data, Network, and AI in the Age of Untact (pp. 1028-1030). Article 9289257 (International Conference on ICT Convergence; Vol. 2020-October). IEEE Computer Society. https://doi.org/10.1109/ICTC49870.2020.9289257

@inproceedings{b0fd6d34ef934df4be8f1cb4b34868c3,

title = "Automatic Classification of Network Traffic Data based on Deep Learning in ONOS Platform",

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. ",

keywords = "automatic network data classification, deep neural network, Machine learning, ONOS",

author = "Jungmin Kwon and Jungjin Lee and Miseon Yu and Hyunggon Park",

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: {\textcopyright} 2020 IEEE.; 11th International Conference on Information and Communication Technology Convergence, ICTC 2020 ; Conference date: 21-10-2020 Through 23-10-2020",

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doi = "10.1109/ICTC49870.2020.9289257",

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Kwon, J, Lee, J, Yu, M & Park, H 2020, Automatic Classification of Network Traffic Data based on Deep Learning in ONOS Platform. in ICTC 2020 - 11th International Conference on ICT Convergence: Data, Network, and AI in the Age of Untact., 9289257, International Conference on ICT Convergence, vol. 2020-October, IEEE Computer Society, pp. 1028-1030, 11th International Conference on Information and Communication Technology Convergence, ICTC 2020, Jeju Island, Korea, Republic of, 21/10/20. https://doi.org/10.1109/ICTC49870.2020.9289257

Automatic Classification of Network Traffic Data based on Deep Learning in ONOS Platform. / Kwon, Jungmin; Lee, Jungjin; Yu, Miseon et al.
ICTC 2020 - 11th International Conference on ICT Convergence: Data, Network, and AI in the Age of Untact. IEEE Computer Society, 2020. p. 1028-1030 9289257 (International Conference on ICT Convergence; Vol. 2020-October).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Kwon, Jungmin

AU - Lee, Jungjin

AU - Yu, Miseon

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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.

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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

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Y2 - 21 October 2020 through 23 October 2020

ER -

Automatic Classification of Network Traffic Data based on Deep Learning in ONOS Platform

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

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Cite this