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IJMLC 2022 Vol.12(6): 333-343 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2022.12.6.1120

Generative Adversarial Networks (GANs): A Survey on Network Traffic Generation

Tertsegha J. Anande and Mark S. Leeson

Abstract—Generating network traffic flows remains a critical aspect of developing cyber and network security systems. In this survey, we first consider the history of network traffic generation methods and identify the weaknesses of these. We then proceed to introduce more recent approaches based on machine learning (ML) models. In particular, we focus on Generative Adversarial Network (GAN) models, which have developed from their initial form to encompass many variants in today’s ML landscape. The use of GANs for generating traffic flows that have appeared in the literature are then presented. For each instance, we present the architecture, training methods, generated results, identified limitations and prospects for further research. We thus demonstrate that GANs are key to future developments in network traffic generation and secure cyber and network systems.

Index Terms—Generative adversarial networks, network traffic, network traffic generation, neural networks.

T. J. Anande and M. S. Leeson are with the University of Warwick, Coventry, CV4 7AL U.K. (e-mail: Tertsegha-Joseph.Anande@ warwick.ac.uk).

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Cite: Tertsegha J. Anande and Mark S. Leeson, "Generative Adversarial Networks (GANs): A Survey on Network Traffic Generation," International Journal of Machine Learning and Computing vol. 12, no. 6, pp. 333-343, 2022.

Copyright @ 2022 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

 

General Information

  • E-ISSN: 2972-368X
  • Abbreviated Title: Int. J. Mach. Learn.
  • Frequency: Quaterly
  • DOI: 10.18178/IJML
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals LibraryCNKI.
  • E-mail: ijml@ejournal.net


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