Scholarlink

<p>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.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月31日

查看全文
  • Hendrio Bragança,
  • Diego Kreutz,
  • Vanderson Rocha,
  • Joner Assolin,
  • Eduardo Feitosa

摘要

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.