arXiv Open Access 2025

MH-1M: A 1.34 Million-Sample Comprehensive Multi-Feature Android Malware Dataset for Machine Learning, Deep Learning, Large Language Models, and Threat Intelligence Research

Hendrio Braganca Diego Kreutz Vanderson Rocha Joner Assolin and Eduardo Feitosa

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We present MH-1M, one of the most comprehensive and up-to-date datasets for advanced Android malware research. The dataset comprises 1,340,515 applications, encompassing a wide range of features and extensive metadata. To ensure accurate malware classification, we employ the VirusTotal API, integrating multiple detection engines for comprehensive and reliable assessment. Our GitHub, Figshare, and Harvard Dataverse repositories provide open access to the processed dataset and its extensive supplementary metadata, totaling more than 400 GB of data and including the outputs of the feature extraction pipeline as well as the corresponding VirusTotal reports. Our findings underscore the MH-1M dataset's invaluable role in understanding the evolving landscape of malware.

Topik & Kata Kunci

cs.CR cs.AI cs.LG cs.PF

Penulis (5)

Hendrio Braganca

Diego Kreutz

Vanderson Rocha

Joner Assolin

and Eduardo Feitosa

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Braganca, H., Kreutz, D., Rocha, V., Assolin, J., Feitosa, a.E. (2025). MH-1M: A 1.34 Million-Sample Comprehensive Multi-Feature Android Malware Dataset for Machine Learning, Deep Learning, Large Language Models, and Threat Intelligence Research. https://arxiv.org/abs/2511.00342

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Tahun Terbit: 2025
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓