ISSN: IJSB: 2520-4750 (Online), 2521-3040 (Print); JSR : 2708-7085 (online)

Deep learning Utilization in SDN Networks: A Review

Author (s)

Shavan Askar, Kosrat Dlshad Ahmed, Shahab Wahhab Kareem

Abstract

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The contexts of SDN or Software Defined Network deliver increased level of programmable and functionality within the network development, network configuration and development of the dynamic management in the software protocol. SDN concept also provides centralized management and development approach for the network selection, network control and data plans. In this paper, different deep learning models and technical processes for the SDN networks have been reviewed.

 Keywords: Deep Leaning, Machine Learning, Cyber Security, Internet of Things, Privacy, Cyber Security.

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Title: Deep learning Utilization in SDN Networks: A Review
Author: Shavan Askar, Kosrat Dlshad Ahmed, Shahab Wahhab Kareem
Journal Name: International Journal of Science and Business
Website: ijsab.com
ISSN: ISSN 2520-4750 (Online), ISSN 2521-3040 (Print)
DOI: https://doi.org/10.5281/zenodo.5222205
Media: Online
Volume: 5
Issue: 6
Acceptance Date: 29/05/2021
Date of Publication: 19/08/2021
PDF URL: https://ijsab.com/wp-content/uploads/754.pdf
Free download: Available
Page: 174-182
First Page: 174
Last Page: 182
Paper Type: Literature review
Current Status: Published

 

Cite This Article:

Shavan Askar, Kosrat Dlshad Ahmed, Shahab Wahhab Kareem (2021). Deep learning Utilization in SDN Networks: A Review. International Journal of Science and Business, 5(6), 174-182. doi: https://doi.org/10.5281/zenodo.5222205

Retrieved from https://ijsab.com/wp-content/uploads/754.pdf

 

About Author (s)

Shavan Askar (Corresponding Author), Assistant Professor, CEO of Arcella Teleco, Erbil Polytechnic University, Erbil, Iraq. Email: shavan.askar@epu.edu.iq.

Kosrat Dlshad Ahmed, Information System Engineering, Erbil Polytechnic University, Erbil, Iraq. Email: kosrat.ahmed@epu.edu.iq.

Shahab Wahhab Kareem, Lecturer, Erbil Polytechnic University, Erbil, Iraq.

 

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DOI: https://doi.org/10.5281/zenodo.5222205

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