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license: mit
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---
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license: mit
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---
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# MADAR: Efficient Continual Learning for Malware Analysis with Diversity-Aware Replay
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This dataset is released in support of the paper:
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> **MADAR: Efficient Continual Learning for Malware Analysis with Diversity-Aware Replay**
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> Mohammad Saidur Rahman, Scott Coull, Qi Yu, Matthew Wright
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> arXiv preprint [arXiv:2502.05760](https://arxiv.org/abs/2502.05760), 2025
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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:
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- **EMBER-Domain**: Derived from the EMBER dataset of Windows PE files.
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- **AZ-Domain**: Derived from the AndroZoo dataset of Android APKs.
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---
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## Dataset Sources
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### EMBER-Domain
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Curated from the EMBER dataset:
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> Hyrum S. Anderson and Phil Roth
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> *Ember: An open dataset for training static PE malware machine learning models*
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> arXiv preprint [arXiv:1804.04637](https://arxiv.org/abs/1804.04637), 2018
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### AZ-Domain
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Curated from the AndroZoo dataset:
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> Kevin Allix, Tegawendé F. Bissyandé, Jacques Klein, Yves Le Traon
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> *AndroZoo: Collecting Millions of Android Apps for the Research Community*
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> International Conference on Mining Software Repositories (MSR), 2016
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> Marco Alecci, Pedro Jesús Ruiz Jiménez, Kevin Allix, Tegawendé F. Bissyandé, Jacques Klein
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> *AndroZoo: A Retrospective with a Glimpse into the Future*
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> International Conference on Mining Software Repositories (MSR), 2024
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---
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## License
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This dataset is released under the MIT License.
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---
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## Citation
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If you use MADAR in your work, please cite:
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```bibtex
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@article{rahman2025madar,
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title={MADAR: Efficient Continual Learning for Malware Analysis with Diversity-Aware Replay},
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author={Rahman, Mohammad Saidur and Coull, Scott and Yu, Qi and Wright, Matthew},
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journal={arXiv preprint arXiv:2502.05760},
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year={2025}
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}
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