# Auxiliary-Classifier GAN for Malware Analysis

arXiv: https://arxiv.org/abs/2107.01620 · HTML: https://mlsanity.com/paper/2107.01620/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 30 references that are in the map.
- First posted on arXiv 4 Jul 2021.

## Builds on (most cited first, 5 of 5)

- [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)
- [Imbalanced Malware Images Classification: a CNN based Approach](https://mlsanity.com/paper/1708.08042/) (2017)
- [Malware Classification Using Long Short-Term Memory Models](https://mlsanity.com/paper/2103.02746/) (2021)
- [An Empirical Analysis of Image-Based Learning Techniques for Malware Classification](https://mlsanity.com/paper/2103.13827/) (2021)

## Built on by (most cited first, 3 of 3)

- [Generative Adversarial Networks and Image-Based Malware Classification](https://mlsanity.com/paper/2207.00421/) (2022)
- [GANG-MAM: GAN based enGine for Modifying Android Malware](https://mlsanity.com/paper/2109.13297/) (2021)
- [Darknet Traffic Classification and Adversarial Attacks](https://mlsanity.com/paper/2206.06371/) (2022)

## Abstract (arXiv)

Generative adversarial networks (GAN) are a class of powerful machine learning techniques, where both a generative and discriminative model are trained simultaneously. GANs have been used, for example, to successfully generate "deep fake" images. A recent trend in malware research consists of treating executables as images and employing image-based analysis techniques. In this research, we generate fake malware images using auxiliary classifier GANs (AC-GAN), and we consider the effectiveness of various techniques for classifying the resulting images. Our results indicate that the resulting multiclass classification problem is challenging, yet we can obtain strong results when restricting the problem to distinguishing between real and fake samples. While the AC-GAN generated images often appear to be very similar to real malware images, we conclude that from a deep learning perspective, the AC-GAN generated samples do not rise to the level of deep fake malware images.