A traffic recognition based on artificial neural networks in SDN systems

Kai Wei Ke; Yu Shan Jhou; Ho Ting Wu

doi:10.1109/ICCE-Taiwan49838.2020.9258056

A traffic recognition based on artificial neural networks in SDN systems

Kai Wei Ke
, Yu Shan Jhou
, Ho Ting Wu

Department of Computer Science and Information Engineering

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

Abstract

This paper presents a traffic recognition, the basis of QoS, based on artificial neural networks in SDN systems. To the design methodology of systems, this paper applied successive semi-supervised learning and based upon Convolutional Neural Network (CNN) to enabling packet identification and determine the traffic type of a flow. The resulting reveals up to ninety-five percent of correctness of given/labelled flow recognition and a ninety-two percent of correctness for unknown flows.

Original language	English
Title of host publication	2020 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728173993
DOIs	https://doi.org/10.1109/ICCE-Taiwan49838.2020.9258056
State	Published - 28 Sep 2020
Event	7th IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020 - Taoyuan, Taiwan Duration: 28 Sep 2020 → 30 Sep 2020

Publication series

Name	2020 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020

Conference

Conference	7th IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020
Country/Territory	Taiwan
City	Taoyuan
Period	28/09/20 → 30/09/20

Bibliographical note

Publisher Copyright:
© 2020 IEEE.

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10.1109/ICCE-Taiwan49838.2020.9258056

Cite this

Ke, K. W., Jhou, Y. S., & Wu, H. T. (2020). A traffic recognition based on artificial neural networks in SDN systems. In 2020 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020 Article 9258056 (2020 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCE-Taiwan49838.2020.9258056

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abstract = "This paper presents a traffic recognition, the basis of QoS, based on artificial neural networks in SDN systems. To the design methodology of systems, this paper applied successive semi-supervised learning and based upon Convolutional Neural Network (CNN) to enabling packet identification and determine the traffic type of a flow. The resulting reveals up to ninety-five percent of correctness of given/labelled flow recognition and a ninety-two percent of correctness for unknown flows.",

author = "Ke, \{Kai Wei\} and Jhou, \{Yu Shan\} and Wu, \{Ho Ting\}",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 7th IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020 ; Conference date: 28-09-2020 Through 30-09-2020",

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Ke, KW, Jhou, YS & Wu, HT 2020, A traffic recognition based on artificial neural networks in SDN systems. in 2020 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020., 9258056, 2020 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020, Institute of Electrical and Electronics Engineers Inc., 7th IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020, Taoyuan, Taiwan, 28/09/20. https://doi.org/10.1109/ICCE-Taiwan49838.2020.9258056

A traffic recognition based on artificial neural networks in SDN systems. / Ke, Kai Wei; Jhou, Yu Shan; Wu, Ho Ting.
2020 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020. Institute of Electrical and Electronics Engineers Inc., 2020. 9258056 (2020 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020).

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

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N2 - This paper presents a traffic recognition, the basis of QoS, based on artificial neural networks in SDN systems. To the design methodology of systems, this paper applied successive semi-supervised learning and based upon Convolutional Neural Network (CNN) to enabling packet identification and determine the traffic type of a flow. The resulting reveals up to ninety-five percent of correctness of given/labelled flow recognition and a ninety-two percent of correctness for unknown flows.

AB - This paper presents a traffic recognition, the basis of QoS, based on artificial neural networks in SDN systems. To the design methodology of systems, this paper applied successive semi-supervised learning and based upon Convolutional Neural Network (CNN) to enabling packet identification and determine the traffic type of a flow. The resulting reveals up to ninety-five percent of correctness of given/labelled flow recognition and a ninety-two percent of correctness for unknown flows.

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BT - 2020 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 7th IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020

Y2 - 28 September 2020 through 30 September 2020

ER -

Ke KW, Jhou YS, Wu HT. A traffic recognition based on artificial neural networks in SDN systems. In 2020 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020. Institute of Electrical and Electronics Engineers Inc. 2020. 9258056. (2020 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020). doi: 10.1109/ICCE-Taiwan49838.2020.9258056

A traffic recognition based on artificial neural networks in SDN systems

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