# MDEA: Malware Detection with Evolutionary Adversarial Learning

arXiv: https://arxiv.org/abs/2002.03331 · HTML: https://mlsanity.com/paper/2002.03331/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Feb 2020.

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

- [Adversarial Attacks against Windows PE Malware Detection: A Survey of the State-of-the-Art](https://mlsanity.com/paper/2112.12310/) (2021)
- [A Comparison of Adversarial Learning Techniques for Malware Detection](https://mlsanity.com/paper/2308.09958/) (2023)
- [PBP: Post-training Backdoor Purification for Malware Classifiers](https://mlsanity.com/paper/2412.03441/) (2024)
- [On the Effectiveness of Adversarial Training on Malware Classifiers](https://mlsanity.com/paper/2412.18218/) (2024)

## Abstract (arXiv)

Malware detection have used machine learning to detect malware in programs. These applications take in raw or processed binary data to neural network models to classify as benign or malicious files. Even though this approach has proven effective against dynamic changes, such as encrypting, obfuscating and packing techniques, it is vulnerable to specific evasion attacks where that small changes in the input data cause misclassification at test time. This paper proposes a new approach: MDEA, an Adversarial Malware Detection model uses evolutionary optimization to create attack samples to make the network robust against evasion attacks. By retraining the model with the evolved malware samples, its performance improves a significant margin.