构建134万样本安卓恶意软件数据集，支持机器学习与威胁情报研究。
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
- 整合134万安卓应用，涵盖多维度特征与元数据。
- 通过VirusTotal多引擎检测确保分类准确性。
- 开源400GB+数据，适合安全研究与大模型训练。
我们提出MH-1M，一个全面且最新的安卓恶意软件研究数据集，包含1,340,515个应用程序，涵盖广泛特征和丰富元数据。为确保恶意软件分类的准确性，我们采用VirusTotal API，结合多个检测引擎进行综合评估。相关数据通过GitHub、Figshare及Harvard Dataverse公开获取，总数据量超过400 GB，包含特征提取流水线输出及对应的VirusTotal报告。研究结果表明，该数据集在理解恶意软件演化态势方面具有重要价值。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。