README.md

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license: mit
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# MADAR: Efficient Continual Learning for Malware Analysis with Diversity-Aware Replay

This dataset is released in support of the paper:

> **MADAR: Efficient Continual Learning for Malware Analysis with Diversity-Aware Replay**  
> Mohammad Saidur Rahman, Scott Coull, Qi Yu, Matthew Wright
> This paper is published at the Conference on Applied Machine Learning for Information (CAMLIS) 2025.
> 
<!-- > arXiv preprint [arXiv:2502.05760](https://arxiv.org/abs/2502.05760), 2025
 -->
MADAR is a benchmark suite for evaluating continual learning methods in malware classification. It includes realistic data distribution shifts and supports scenarios such as Domain-Incremental Learning (Domain-IL) and Class-Incremental Learning (Class-IL). The dataset includes curated samples from two primary sources:

- **EMBER-Domain**: Derived from the EMBER dataset of Windows PE files.
- **AZ-Domain**: Derived from the AndroZoo dataset of Android APKs.

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## Dataset Sources

### EMBER-Domain

Curated from the EMBER dataset:

> Hyrum S. Anderson and Phil Roth  
> *Ember: An open dataset for training static PE malware machine learning models*  
> arXiv preprint [arXiv:1804.04637](https://arxiv.org/abs/1804.04637), 2018

### AZ-Domain

Curated from the AndroZoo dataset:

> Kevin Allix, Tegawendé F. Bissyandé, Jacques Klein, Yves Le Traon  
> *AndroZoo: Collecting Millions of Android Apps for the Research Community*  
> International Conference on Mining Software Repositories (MSR), 2016

> Marco Alecci, Pedro Jesús Ruiz Jiménez, Kevin Allix, Tegawendé F. Bissyandé, Jacques Klein  
> *AndroZoo: A Retrospective with a Glimpse into the Future*  
> International Conference on Mining Software Repositories (MSR), 2024

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## License

This dataset is released under the MIT License.

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## Citation

If you use MADAR in your work, please cite:

```bibtex
@InProceedings{pmlr-v299-rahman25a,
  title = 	 {MADAR: Efficient Continual Learning for Malware Analysis with Distribution-Aware Replay},
  author =       {Rahman, Mohammad Saidur and Coull, Scott and Yu, Qi and Wright, Matthew},
  booktitle = 	 {Proceedings of the 2025 Conference on Applied Machine Learning for Information Security},
  pages = 	 {265--291},
  year = 	 {2025},
  editor = 	 {Raff, Edward and Rudd, Ethan M.},
  volume = 	 {299},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {22--24 Oct},
  publisher =    {PMLR},
  url = 	 {https://proceedings.mlr.press/v299/rahman25a.html},
}