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

arXiv: https://arxiv.org/abs/2511.22095 · HTML: https://mlsanity.com/paper/2511.22095/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 32 references that are in the map.
- First posted on arXiv 27 Nov 2025.

## Builds on (most cited first, 11 of 11)

- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality](https://mlsanity.com/paper/2405.21060/) (2024)
- [Jamba: A Hybrid Transformer-Mamba Language Model](https://mlsanity.com/paper/2403.19887/) (2024)
- [EMBER: An Open Dataset for Training Static PE Malware Machine Learning Models](https://mlsanity.com/paper/1804.04637/) (2018)
- [Malware Detection by Eating a Whole EXE](https://mlsanity.com/paper/1710.09435/) (2017)
- [SOREL-20M: A Large Scale Benchmark Dataset for Malicious PE Detection](https://mlsanity.com/paper/2012.07634/) (2020)
- [EMBER2024 -- A Benchmark Dataset for Holistic Evaluation of Malware Classifiers](https://mlsanity.com/paper/2506.05074/) (2025)
- [Assemblage: Automatic Binary Dataset Construction for Machine Learning](https://mlsanity.com/paper/2405.03991/) (2024)
- [LAMDA: A Longitudinal Android Malware Benchmark for Concept Drift Analysis](https://mlsanity.com/paper/2505.18551/) (2025)
- [Malware Detection Using Frequency Domain-Based Image Visualization and Deep Learning](https://mlsanity.com/paper/2101.10578/) (2021)
- [Binary BPE: A Family of Cross-Platform Tokenizers for Binary Analysis](https://mlsanity.com/paper/2511.17573/) (2025)

## Built on by (most cited first, 1 of 1)

- [MimeLens: Position-Agnostic Content-Type Detection for Binary Fragments](https://mlsanity.com/paper/2606.04171/) (2026)

## Abstract (arXiv)

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.