# Malware Evasion Attack and Defense

arXiv: https://arxiv.org/abs/1904.05747 · HTML: https://mlsanity.com/paper/1904.05747/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Apr 2019.

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

- [MERLIN -- Malware Evasion with Reinforcement LearnINg](https://mlsanity.com/paper/2203.12980/) (2022)
- [Flashy Backdoor: Real-world Environment Backdoor Attack on SNNs with DVS Cameras](https://mlsanity.com/paper/2411.03022/) (2024)
- [Stealing and Evading Malware Classifiers and Antivirus at Low False Positive Conditions](https://mlsanity.com/paper/2204.06241/) (2022)
- [On the Effectiveness of Adversarial Training on Malware Classifiers](https://mlsanity.com/paper/2412.18218/) (2024)

## Abstract (arXiv)

Machine learning (ML) classifiers are vulnerable to adversarial examples. An adversarial example is an input sample which is slightly modified to induce misclassification in an ML classifier. In this work, we investigate white-box and grey-box evasion attacks to an ML-based malware detector and conduct performance evaluations in a real-world setting. We compare the defense approaches in mitigating the attacks. We propose a framework for deploying grey-box and black-box attacks to malware detection systems.