# Generative Adversarial Networks for Malware Detection: a Survey

arXiv: https://arxiv.org/abs/2302.08558 · HTML: https://mlsanity.com/paper/2302.08558/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 116 references that are in the map.
- First posted on arXiv 16 Feb 2023.

## Builds on (most cited first, 24 of 24)

- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://mlsanity.com/paper/1511.06434/) (2015)
- [Least Squares Generative Adversarial Networks](https://mlsanity.com/paper/1611.04076/) (2016)
- [Striving for Simplicity: The All Convolutional Net](https://mlsanity.com/paper/1412.6806/) (2014)
- [InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets](https://mlsanity.com/paper/1606.03657/) (2016)
- [Conditional Image Synthesis With Auxiliary Classifier GANs](https://mlsanity.com/paper/1610.09585/) (2016)
- [Adversarial Feature Learning](https://mlsanity.com/paper/1605.09782/) (2016)
- [A Note on the Inception Score](https://mlsanity.com/paper/1801.01973/) (2018)
- [Pros and Cons of GAN Evaluation Measures](https://mlsanity.com/paper/1802.03446/) (2018)
- [EMBER: An Open Dataset for Training Static PE Malware Machine Learning Models](https://mlsanity.com/paper/1804.04637/) (2018)
- [BAGAN: Data Augmentation with Balancing GAN](https://mlsanity.com/paper/1803.09655/) (2018)
- [Generating Adversarial Malware Examples for Black-Box Attacks Based on GAN](https://mlsanity.com/paper/1702.05983/) (2017)
- [Microsoft Malware Classification Challenge](https://mlsanity.com/paper/1802.10135/) (2018)
- [Combating Mode Collapse in GAN training: An Empirical Analysis using Hessian Eigenvalues](https://mlsanity.com/paper/2012.09673/) (2020)
- [Semi-Adversarial Networks: Convolutional Autoencoders for Imparting Privacy to Face Images](https://mlsanity.com/paper/1712.00321/) (2017)
- [Generative Adversarial Networks (GANs): An Overview of Theoretical Model, Evaluation Metrics, and Recent Developments](https://mlsanity.com/paper/2005.13178/) (2020)
- [Generative Adversarial Networks (GANs) in Networking: A Comprehensive Survey & Evaluation](https://mlsanity.com/paper/2105.04184/) (2021)
- [MalFox: Camouflaged Adversarial Malware Example Generation Based on Conv-GANs Against Black-Box Detectors](https://mlsanity.com/paper/2011.01509/) (2020)
- [eCommerceGAN : A Generative Adversarial Network for E-commerce](https://mlsanity.com/paper/1801.03244/) (2018)
- [EVAGAN: Evasion Generative Adversarial Network for Low Data Regimes](https://mlsanity.com/paper/2109.08026/) (2021)
- [Benchmark Static API Call Datasets for Malware Family Classification](https://mlsanity.com/paper/2111.15205/) (2021)
- [Destruction of Image Steganography using Generative Adversarial Networks](https://mlsanity.com/paper/1912.10070/) (2019)

## Built on by (most cited first, 1 of 1)

- [Deep generative models as the probability transformation functions](https://mlsanity.com/paper/2506.17171/) (2025)

## 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.