mlsanityData to 7 Oct 2026

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

arXiv 2511.00342 ↗First posted 1 Nov 2025cs.CR cs.AI cs.LGcited by 0

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Abstract (arXiv)

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.

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References · 11

  1. 2021Tabular Data: Deep Learning is Not All You Needcited by 530
  2. 2023When Do Neural Nets Outperform Boosted Trees on Tabular Data?cited by 218
  3. 2015A Random Forest Guided Tourcited by 168
  4. 2016MaMaDroid: Detecting Android Malware by Building Markov Chains of Behavioral Modelscited by 29
  5. 2025TIF: Learning Temporal Invariance in Android Malware Detectorscited by 4
  6. 2022Dataset Bias in Android Malware Detectioncited by 3
  7. 2025X-Factor: Quality Is a Dataset-Intrinsic Propertycited by 3
  8. 2024Enhancing Phishing Detection through Feature Importance Analysis and Explainable AI: A Comparative Study of CatBoost, XGBoost, and EBM Modelscited by 2
  9. 2022A Longitudinal Study of Cryptographic API: a Decade of Android Malwarecited by 1
  10. 2023Reassessing feature-based Android malware detection in a contemporary contextcited by 1
  11. 2023On building machine learning pipelines for Android malware detection: a procedural survey of practices, challenges and opportunitiescited by 1

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