mlsanityData to 7 Oct 2026

Generative Adversarial Networks for Malware Detection: a Survey

arXiv 2302.08558 ↗First posted 16 Feb 2023cs.CR cs.LGcited by 1

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Abstract (arXiv)

Since their proposal in the 2014 paper by Ian Goodfellow, there has been an explosion of research into the area of Generative Adversarial Networks. While they have been utilised in many fields, the realm of malware research is a problem space in which GANs have taken root. From balancing datasets to creating unseen examples in rare classes, GAN models offer extensive opportunities for application. This paper surveys the current research and literature for the use of Generative Adversarial Networks in the malware problem space. This is done with the hope that the reader may be able to gain an overall understanding as to what the Generative Adversarial model provides for this field, and for what areas within malware research it is best utilised. It covers the current related surveys, the different categories of GAN, and gives the outcomes of recent research into optimising GANs for different topics, as well as future directions for exploration.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 24 of its 116 references that are in the map. First posted on arXiv 16 Feb 2023.

Builds on

24 of 116 references.

References · 24

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12 more
  1. 2018BAGAN: Data Augmentation with Balancing GANcited by 90
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  11. 2021Benchmark Static API Call Datasets for Malware Family Classificationcited by 3
  12. 2019Destruction of Image Steganography using Generative Adversarial Networkscited by 2

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