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

資訊工程系

研究成果: 書籍/報告/會議論文中的章節 › 會議投稿 › 同行評審

摘要

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.

原文	English
主出版物標題	2020 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020
發行者	Institute of Electrical and Electronics Engineers Inc.
ISBN（電子）	9781728173993
DOIs	https://doi.org/10.1109/ICCE-Taiwan49838.2020.9258056
出版狀態	Published - 28 9月 2020
事件	7th IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020 - Taoyuan, Taiwan 持續時間: 28 9月 2020 → 30 9月 2020

出版系列

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

Conference

Conference	7th IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020
國家/地區	Taiwan
城市	Taoyuan
期間	28/09/20 → 30/09/20

文獻附註

Publisher Copyright:
© 2020 IEEE.

存取文件

10.1109/ICCE-Taiwan49838.2020.9258056

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Link to publication in Scopus

引用此

Ke, K. W., Jhou, Y. S., & Wu, H. T. (2020). A traffic recognition based on artificial neural networks in SDN systems. 於 2020 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020 文章 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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title = "A traffic recognition based on artificial neural networks in SDN systems",

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. 於 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).

研究成果: 書籍/報告/會議論文中的章節 › 會議投稿 › 同行評審

TY - GEN

T1 - A traffic recognition based on artificial neural networks in SDN systems

AU - Ke, Kai Wei

AU - Jhou, Yu Shan

AU - Wu, Ho Ting

PY - 2020/9/28

Y1 - 2020/9/28

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.

UR - https://www.scopus.com/pages/publications/85098465793

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AN - SCOPUS:85098465793

T3 - 2020 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020

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. 於 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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