# Generative Adversarial Networks and Image-Based Malware Classification

arXiv: https://arxiv.org/abs/2207.00421 · HTML: https://mlsanity.com/paper/2207.00421/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 45 references that are in the map.
- First posted on arXiv 8 Jun 2022.

## Builds on (most cited first, 14 of 14)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://mlsanity.com/paper/1511.06434/) (2015)
- [Conditional Image Synthesis With Auxiliary Classifier GANs](https://mlsanity.com/paper/1610.09585/) (2016)
- [Generating Adversarial Malware Examples for Black-Box Attacks Based on GAN](https://mlsanity.com/paper/1702.05983/) (2017)
- [CovidGAN: Data Augmentation Using Auxiliary Classifier GAN for Improved Covid-19 Detection](https://mlsanity.com/paper/2103.05094/) (2021)
- [Rebooting ACGAN: Auxiliary Classifier GANs with Stable Training](https://mlsanity.com/paper/2111.01118/) (2021)
- [Malware Classification Using Long Short-Term Memory Models](https://mlsanity.com/paper/2103.02746/) (2021)
- [Effect of Input Noise Dimension in GANs](https://mlsanity.com/paper/2004.06882/) (2020)
- [An Empirical Analysis of Image-Based Learning Techniques for Malware Classification](https://mlsanity.com/paper/2103.13827/) (2021)
- [Random Forest for Malware Classification](https://mlsanity.com/paper/1609.07770/) (2016)
- [Auxiliary-Classifier GAN for Malware Analysis](https://mlsanity.com/paper/2107.01620/) (2021)
- [Malware Classification Using Transfer Learning](https://mlsanity.com/paper/2107.13743/) (2021)

## Built on by (most cited first, 4 of 4)

- [A Natural Language Processing Approach to Malware Classification](https://mlsanity.com/paper/2307.11032/) (2023)
- [A Comparison of Selected Image Transformation Techniques for Malware Classification](https://mlsanity.com/paper/2509.10838/) (2025)
- [Adversarial Defense in Cybersecurity: A Systematic Review of GANs for Threat Detection and Mitigation](https://mlsanity.com/paper/2509.20411/) (2025)
- [Malware Classification using a Hybrid Hidden Markov Model-Convolutional Neural Network](https://mlsanity.com/paper/2412.18932/) (2024)

## Abstract (arXiv)

For efficient malware removal, determination of malware threat levels, and damage estimation, malware family classification plays a critical role. In this paper, we extract features from malware executable files and represent them as images using various approaches. We then focus on Generative Adversarial Networks (GAN) for multiclass classification and compare our GAN results to other popular machine learning techniques, including Support Vector Machine (SVM), XGBoost, and Restricted Boltzmann Machines (RBM). We find that the AC-GAN discriminator is generally competitive with other machine learning techniques. We also evaluate the utility of the GAN generative model for adversarial attacks on image-based malware detection. While AC-GAN generated images are visually impressive, we find that they are easily distinguished from real malware images using any of several learning techniques. This result indicates that our GAN generated images would be of little value in adversarial attacks.