MH-1M: A 1.34 Million-Sample Multi-Feature Android Malware Dataset with Rich Metadata

We present MH-1M, one up-to-date dataset 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 Figshare, Harvard Dataverse and GitHub 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 role in understanding the evolving landscape of malware.

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Publication Details

Journal
Scientific Data
Published
2025-12-31
DOI
https://doi.org/10.1038/s41597-025-06469-5
Citations
2
Primary Topic
Advanced Malware Detection Techniques
Type
article
Field-Weighted Citation Impact
2.97

Funders

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article

MH-1M: A 1.34 Million-Sample Multi-Feature Android Malware Dataset with Rich Metadata

Eduardo Luzeiro Feitosa, Hendrio Bragança, Joner Assolin, Vanderson Rocha et al.
2 citations
Scientific Data
Advanced Malware Detection Techniques
2.97
article

MH-1M: A 1.34 Million-Sample Multi-Feature Android Malware Dataset with Rich Metadata

Eduardo Luzeiro Feitosa, Hendrio Bragança, Joner Assolin, Vanderson Rocha, Diego Kreutz
article en
2 citations

Abstract

We present MH-1M, one up-to-date dataset 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 Figshare, Harvard Dataverse and GitHub 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 role in understanding the evolving landscape of malware.

Scientific DataVol. 13(1)
Universidade Federal do Amazonas (BR), Universidade Federal do Pampa (BR)
Universidade Federal do Amazonas, Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul, Fundação de Amparo à Pesquisa do Estado do Amazonas
Openalex Percentile: Top 7%
Advanced Malware Detection Techniques
2.97
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