Generative Adversarial Networks (GANs) in networking: A comprehensive survey & evaluation
Article
Navidan, H., Moshiri, P., Nabati, M., Shahbazian, R., Ghorashi, S., Shah-Mansouri, V. and Windridge, D. 2021. Generative Adversarial Networks (GANs) in networking: A comprehensive survey & evaluation. Computer Networks. 194, pp. 1-21. https://doi.org/10.1016/j.comnet.2021.108149
Type | Article |
---|---|
Title | Generative Adversarial Networks (GANs) in networking: A comprehensive survey & evaluation |
Authors | Navidan, H., Moshiri, P., Nabati, M., Shahbazian, R., Ghorashi, S., Shah-Mansouri, V. and Windridge, D. |
Abstract | 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. |
Keywords | Generative Adversarial Networks; Deep learning; Semi-supervised learning; Computer networks; Communication networks |
Publisher | Elsevier |
Journal | Computer Networks |
ISSN | 1389-1286 |
Electronic | 1872-7069 |
Publication dates | |
Online | 04 May 2021 |
20 Jul 2021 | |
Publication process dates | |
Deposited | 13 May 2021 |
Accepted | 12 Apr 2021 |
Submitted | 05 Dec 2020 |
Output status | Published |
Accepted author manuscript | License |
Copyright Statement | © 2021. The accepted manuscript version is made available under the CC-BY-NC-ND 4.0 license. |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.comnet.2021.108149 |
Web of Science identifier | WOS:000659129400010 |
Language | English |
First submitted version |
https://repository.mdx.ac.uk/item/895w2
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