# ATMPA: Attacking Machine Learning-based Malware Visualization Detection Methods via Adversarial Examples

arXiv: https://arxiv.org/abs/1808.01546 · HTML: https://mlsanity.com/paper/1808.01546/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Aug 2018.

## Built on by (most cited first, 5 of 5)

- [Adversarial Attacks against Windows PE Malware Detection: A Survey of the State-of-the-Art](https://mlsanity.com/paper/2112.12310/) (2021)
- [On the Security Risks of ML-based Malware Detection Systems: A Survey](https://mlsanity.com/paper/2505.10903/) (2025)
- [FGAM:Fast Adversarial Malware Generation Method Based on Gradient Sign](https://mlsanity.com/paper/2305.12770/) (2023)
- [ATWM: Defense against adversarial malware based on adversarial training](https://mlsanity.com/paper/2307.05095/) (2023)
- [On the Effectiveness of Adversarial Samples against Ensemble Learning-based Windows PE Malware Detectors](https://mlsanity.com/paper/2309.13841/) (2023)

## Abstract (arXiv)

Since the threat of malicious software (malware) has become increasingly serious, automatic malware detection techniques have received increasing attention, where machine learning (ML)-based visualization detection methods become more and more popular. In this paper, we demonstrate that the state-of-the-art ML-based visualization detection methods are vulnerable to Adversarial Example (AE) attacks. We develop a novel Adversarial Texture Malware Perturbation Attack (ATMPA) method based on the gradient descent and L-norm optimization method, where attackers can introduce some tiny perturbations on the transformed dataset such that ML-based malware detection methods will completely fail. The experimental results on the MS BIG malware dataset show that a small interference can reduce the accuracy rate down to 0% for several ML-based detection methods, and the rate of transferability is 74.1% on average.