mlsanityData to 14 Oct 2026

Generative Adversarial Networks (GANs) in Networking: A Comprehensive Survey & Evaluation

arXiv 2105.04184 ↗First posted 10 May 2021cs.NI cs.LGcited by 11

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Abstract (arXiv)

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.

Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). Builds on 61 of its 151 references that are in the map. First posted on arXiv 10 May 2021.

Builds on

61 of 151 references.

References · 61

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38 more
  1. 2017Generative Adversarial Networks: An Overviewcited by 590
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  24. 2016Texture Synthesis with Spatial Generative Adversarial Networkscited by 45
  25. 2018A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Securitycited by 42
  26. 2018IDSGAN: Generative Adversarial Networks for Attack Generation against Intrusion Detectioncited by 42
  27. 2019GIDS: GAN based Intrusion Detection System for In-Vehicle Networkcited by 42
  28. 2019GCN-GAN: A Non-linear Temporal Link Prediction Model for Weighted Dynamic Networkscited by 40
  29. 2016CSI-based Fingerprinting for Indoor Localization: A Deep Learning Approachcited by 39
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50 most cited of 61 All 61 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 2; 2022: 2; 2023: 2; 2024: 2; 2025: 1; 2026: 2.

11 papers.

  1. 2022Data Augmentation techniques in time series domain: A survey and taxonomycited by 41
  2. 2025Generative Diffusion Models for Wireless Networks: Fundamental, Architecture, and State-of-the-Artcited by 14
  3. 2022FakeNews: GAN-based generation of realistic 3D volumetric data -- A systematic review and taxonomycited by 13
  4. 2024Applications of Generative AI (GAI) for Mobile and Wireless Networking: A Surveycited by 9
  5. 2021Synthetic flow-based cryptomining attack generation through Generative Adversarial Networkscited by 4
  6. 2021Improving the quality of generative models through Smirnov transformationcited by 1
  7. 2023Generative Adversarial Networks for Malware Detection: a Surveycited by 1
  8. 2024Resource Governance in Networked Systems via Integrated Variational Autoencoders and Reinforcement Learningcited by 1
  9. 2023Conformalised data synthesiscited by 0
  10. 2026From Selection to Scheduling: Federated Geometry-Aware Correction Makes Exemplar Replay Work Better under Continual Dynamic Heterogeneitycited by 0
  11. 2026Generative AI and Federated Learning for Intrusion Detection Systems: A Surveycited by 0