https://github.com/matouskozak/exe-scanner

A lightweight plugin that improves malware classifiers' robustness against adversarial attacks on Windows executables (EXEmples). Based on the research paper "Updating Windows Malware Detectors: Balancing Robustness and Regression against Adversarial EXEmples" (2025).
https://github.com/matouskozak/exe-scanner

adversarial-machine-learning malware-detection security windows

Last synced: about 1 month ago
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A lightweight plugin that improves malware classifiers' robustness against adversarial attacks on Windows executables (EXEmples). Based on the research paper "Updating Windows Malware Detectors: Balancing Robustness and Regression against Adversarial EXEmples" (2025).

• Host: GitHub
• URL: https://github.com/matouskozak/exe-scanner
• Owner: matouskozak
• License: other
• Created: 2024-01-27T17:37:21.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2026-02-19T10:57:43.000Z (4 months ago)
• Last Synced: 2026-02-19T15:21:02.863Z (4 months ago)
• Topics: adversarial-machine-learning, malware-detection, security, windows
• Language: Jupyter Notebook
• Homepage:
• Size: 14 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.txt

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README

          
# EXE-scanner

EXE-scanner, lightweight plugin ready to be deployed with main malware classifiers to increase robustness against adversarial EXEmples. See the provided [tutorial](https://github.com/matouskozak/EXE-scanner/blob/main/tutorial.ipynb) on how to train and use your own EXE-scanner.

![image](https://github.com/matouskozak/EXE-scanner/assets/55735845/47e470f3-f8cd-4060-b298-a2f3c0f535ca)

## Download

Download dataset and pretrained models from here: https://kaggle.com/datasets/fca93b34e3d3ed8936fb76cc06b4a7a94f9f296eebd675de2fab682857e24232

## Citing

If you use this work, pleace cite the following [paper](https://arxiv.org/abs/2405.02646):

```

@article{kozak2025updating,

  title={Updating Windows Malware Detectors: Balancing Robustness and Regression against Adversarial EXEmples},

  author={Kozak, Matous and Demetrio, Luca and Trizna, Dmitrijs and Roli, Fabio},

  journal={Computers \& Security},

  volume={155},

  pages={104466},

  year={2025},

  publisher={Elsevier},

  doi={https://doi.org/10.1016/j.cose.2025.104466}

}

```