Datasets:
The dataset viewer is not available for this subset.
Exception: SplitsNotFoundError
Message: The split names could not be parsed from the dataset config.
Traceback: Traceback (most recent call last):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 299, in get_dataset_config_info
for split_generator in builder._split_generators(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 83, in _split_generators
raise ValueError(
ValueError: The TAR archives of the dataset should be in WebDataset format, but the files in the archive don't share the same prefix or the same types.
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 65, in compute_split_names_from_streaming_response
for split in get_dataset_split_names(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 353, in get_dataset_split_names
info = get_dataset_config_info(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 304, in get_dataset_config_info
raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
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
arXiv preprint arXiv: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.
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, 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
License
This dataset is released under the MIT License.
Citation
If you use MADAR in your work, please cite:
@article{rahman2025madar,
title={MADAR: Efficient Continual Learning for Malware Analysis with Diversity-Aware Replay},
author={Rahman, Mohammad Saidur and Coull, Scott and Yu, Qi and Wright, Matthew},
journal={arXiv preprint arXiv:2502.05760},
year={2025}
}
- Downloads last month
- 0