MH-1M: One of The Most Comprehensive and Up-to-Date Dataset for Advanced Android Malware Detection

We introduce MH-1M, one of the most comprehensive and up-to-date dataset for advanced Android malware research. This dataset includes 1,340,515 applications, covering diverse features and extensive sets of metadata. For precise malware assessment, we utilize the VirusTotal API, integrating multiple detection methods to ensure reliable outcomes. Our GitHub repository offers users access to the processed dataset and associated metadata, totaling over 400GB. This includes comprehensive outputs from the feature extraction process and VirusTotal metadata files. Our findings underscore the important role of the MH-1M dataset as an invaluable resource for understanding the evolving landscape of malware.

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Published
2024-09-16
DOI
https://doi.org/10.5753/sbseg.2024.241632
Primary Topic
Advanced Malware Detection Techniques
Type
article
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article

MH-1M: One of The Most Comprehensive and Up-to-Date Dataset for Advanced Android Malware Detection

Hendrio Bragança, Vanderson Rocha, Eduardo Feitosa, Joner Assolin et al.
Advanced Malware Detection Techniques
article

MH-1M: One of The Most Comprehensive and Up-to-Date Dataset for Advanced Android Malware Detection

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

Abstract

We introduce MH-1M, one of the most comprehensive and up-to-date dataset for advanced Android malware research. This dataset includes 1,340,515 applications, covering diverse features and extensive sets of metadata. For precise malware assessment, we utilize the VirusTotal API, integrating multiple detection methods to ensure reliable outcomes. Our GitHub repository offers users access to the processed dataset and associated metadata, totaling over 400GB. This includes comprehensive outputs from the feature extraction process and VirusTotal metadata files. Our findings underscore the important role of the MH-1M dataset as an invaluable resource for understanding the evolving landscape of malware.

Universidade Federal do Pampa (BR)
Industry, innovation and infrastructure
Openalex Percentile: Top 82%
Advanced Malware Detection Techniques
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