# MalFox: Camouflaged Adversarial Malware Example Generation Based on Conv-GANs Against Black-Box Detectors

arXiv: https://arxiv.org/abs/2011.01509 · HTML: https://mlsanity.com/paper/2011.01509/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Nov 2020.

## Built on by (most cited first, 7 of 7)

- [Adversarial Attacks against Windows PE Malware Detection: A Survey of the State-of-the-Art](https://mlsanity.com/paper/2112.12310/) (2021)
- [MERLIN -- Malware Evasion with Reinforcement LearnINg](https://mlsanity.com/paper/2203.12980/) (2022)
- [A Survey of Malware Detection Using Deep Learning](https://mlsanity.com/paper/2407.19153/) (2024)
- [Generative Adversarial Networks for Malware Detection: a Survey](https://mlsanity.com/paper/2302.08558/) (2023)
- [Adversarial Defense in Cybersecurity: A Systematic Review of GANs for Threat Detection and Mitigation](https://mlsanity.com/paper/2509.20411/) (2025)
- [On the Effectiveness of Adversarial Samples against Ensemble Learning-based Windows PE Malware Detectors](https://mlsanity.com/paper/2309.13841/) (2023)
- [A Lean Transformer Model for Dynamic Malware Analysis and Detection](https://mlsanity.com/paper/2408.02313/) (2024)

## Abstract (arXiv)

Deep learning is a thriving field currently stuffed with many practical applications and active research topics. It allows computers to learn from experience and to understand the world in terms of a hierarchy of concepts, with each being defined through its relations to simpler concepts. Relying on the strong capabilities of deep learning, we propose a convolutional generative adversarial network-based (Conv-GAN) framework titled MalFox, targeting adversarial malware example generation against third-party black-box malware detectors. Motivated by the rival game between malware authors and malware detectors, MalFox adopts a confrontational approach to produce perturbation paths, with each formed by up to three methods (namely Obfusmal, Stealmal, and Hollowmal) to generate adversarial malware examples. To demonstrate the effectiveness of MalFox, we collect a large dataset consisting of both malware and benignware programs, and investigate the performance of MalFox in terms of accuracy, detection rate, and evasive rate of the generated adversarial malware examples. Our evaluation indicates that the accuracy can be as high as 99.0% which significantly outperforms the other 12 well-known learning models. Furthermore, the detection rate is dramatically decreased by 56.8% on average, and the average evasive rate is noticeably improved by up to 56.2%.