{"id":21810944,"url":"https://github.com/matouskozak/exe-scanner","last_synced_at":"2026-04-27T08:31:48.641Z","repository":{"id":236309685,"uuid":"749139012","full_name":"matouskozak/EXE-scanner","owner":"matouskozak","description":"A lightweight plugin that improves malware classifiers' robustness against adversarial attacks on Windows executables (EXEmples). 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See the provided [tutorial](https://github.com/matouskozak/EXE-scanner/blob/main/tutorial.ipynb) on how to train and use your own EXE-scanner.\n\n![image](https://github.com/matouskozak/EXE-scanner/assets/55735845/47e470f3-f8cd-4060-b298-a2f3c0f535ca)\n\n\n## Download\nDownload dataset and pretrained models from here: https://kaggle.com/datasets/fca93b34e3d3ed8936fb76cc06b4a7a94f9f296eebd675de2fab682857e24232\n\n## Citing\nIf you use this work, pleace cite the following [paper](https://arxiv.org/abs/2405.02646):\n```\n@article{kozak2025updating,\n  title={Updating Windows Malware Detectors: Balancing Robustness and Regression against Adversarial EXEmples},\n  author={Kozak, Matous and Demetrio, Luca and Trizna, Dmitrijs and Roli, Fabio},\n  journal={Computers \\\u0026 Security},\n  volume={155},\n  pages={104466},\n  year={2025},\n  publisher={Elsevier},\n  doi={https://doi.org/10.1016/j.cose.2025.104466}\n}\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmatouskozak%2Fexe-scanner","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmatouskozak%2Fexe-scanner","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmatouskozak%2Fexe-scanner/lists"}