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

TL;DR

MH-1M is a large, comprehensive Android malware dataset with over 1.3 million samples, extensive features, and metadata, designed to advance machine learning and threat intelligence research.

Contribution

The paper introduces MH-1M, a large-scale, multi-feature Android malware dataset with open access, supporting diverse research in malware detection and analysis.

Findings

The dataset enables improved malware classification accuracy.

Open access facilitates collaborative research and benchmarking.

Supports advanced machine learning and deep learning applications.

Abstract

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.