Binary-30K: A Heterogeneous Dataset for Deep Learning in Binary Analysis and Malware Detection

TL;DR

Binary-30K is a comprehensive, heterogeneous dataset of nearly 30,000 binaries across multiple platforms and architectures, designed to facilitate deep learning research in binary analysis and malware detection.

Contribution

It introduces the first multi-platform, multi-architecture binary dataset with pre-computed tokenization and structural metadata, supporting advanced sequence-based models and benchmarking.

Findings

Enables research on platform-invariant malware detection

Supports cross-target transfer learning

Facilitates long-context binary understanding

Abstract

Deep learning research for binary analysis faces a critical infrastructure gap. Today, existing datasets target single platforms, require specialized tooling, or provide only hand-engineered features incompatible with modern neural architectures; no single dataset supports accessible research and pedagogy on realistic use cases. To solve this, we introduce Binary-30K, the first heterogeneous binary dataset designed for sequence-based models like transformers. Critically, Binary-30K covers Windows, Linux, macOS, and Android across 15+ CPU architectures. With 29,793 binaries and approximately 26.93% malware representation, Binary-30K enables research on platform-invariant detection, cross-target transfer learning, and long-context binary understanding. The dataset provides pre-computed byte-level BPE tokenization alongside comprehensive structural metadata, supporting both sequence modeling and structure-aware approaches. Platform-first stratified sampling ensures representative coverage across operating systems and architectures, while distribution via Hugging Face with official train/validation/test splits enables reproducible benchmarking. The dataset is publicly available at https://huggingface.co/datasets/mjbommar/binary-30k, providing an accessible resource for researchers, practitioners, and students alike.