Generative Adversarial Networks (GANs) in networking: a comprehensive survey & evaluation

Navidan, Hojjat ORCID logoORCID:, Moshiri, Parisa Fard, Nabati, Mohammad, Shahbazian, Reza, Ghorashi, Seyed Ali ORCID logoORCID:, Shah-Mansouri, Vahid ORCID logoORCID: and Windridge, David ORCID logoORCID: (2021) Generative Adversarial Networks (GANs) in networking: a comprehensive survey & evaluation. Computer Networks, 194 , 108149. pp. 1-21. ISSN 1389-1286 [Article] (doi:10.1016/j.comnet.2021.108149)

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Despite the recency of their conception, Generative Adversarial Networks (GANs) constitute an extensively-researched machine learning sub-field for the creation of synthetic data through deep generative modeling. GANs have consequently been applied in a number of domains, most notably computer vision, in which they are typically used to generate or transform synthetic images. Given their relative ease of use, it is therefore natural that researchers in the field of networking (which has seen extensive application of deep learning methods) should take an interest in GAN-based approaches. The need for a comprehensive survey of such activity is therefore urgent. In this paper, we demonstrate how this branch of machine learning can benefit multiple aspects of computer and communication networks, including mobile networks, network analysis, internet of things, physical layer, and cybersecurity. In doing so, we shall provide a novel evaluation framework for comparing the performance of different models in non-image applications, applying this to a number of reference network datasets.

Item Type: Article
Keywords (uncontrolled): Generative Adversarial Networks, Deep learning, Semi-supervised learning, Computer networks, Communication networks
Research Areas: A. > School of Science and Technology > Computer Science
Item ID: 33246
Notes on copyright: © 2021. The accepted manuscript version is made available under the CC-BY-NC-ND 4.0 license.
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Depositing User: David Windridge
Date Deposited: 13 May 2021 11:17
Last Modified: 21 Apr 2023 15:56

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