首个支持跨平台的二进制分析数据集，助力恶意软件检测研究。
Binary-30K: A Heterogeneous Dataset for Deep Learning in Binary Analysis and Malware Detection
- 构建覆盖多平台的异构二进制数据集，支持序列模型训练。
- 含29,793个二进制文件，恶意样本占比约26.93%，支持跨架构检测。
- 提供预处理字节级token化和结构元数据，适合教学与可复现研究。
深度学习在二进制分析领域的研究面临关键基础设施缺失问题。现有数据集通常仅针对单一平台，需专用工具，或仅提供人工特征，难以适配现代神经网络架构；尚无数据集能支持真实场景下的研究与教学。为此，我们提出Binary-30K，首个专为基于序列的模型（如Transformer）设计的异构二进制数据集。该数据集覆盖Windows、Linux、macOS及Android，涵盖15+种CPU架构。包含29,793个二进制文件，恶意样本占比约26.93%，支持平台无关检测、跨目标迁移学习及长上下文二进制理解。数据集提供预计算的字节级BPE分词及全面的结构元数据，兼顾序列建模与结构感知方法。采用平台优先的分层采样策略，确保操作系统与架构的代表性覆盖。通过Hugging Face发布，配备官方训练/验证/测试划分，支持可复现基准测试。数据集公开于https://huggingface.co/datasets/mjbommar/binary-30k，可供研究人员、从业者及学生使用。
原文摘要 · Abstract (English)
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.
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