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

2025-11-27 04:33:16
Michael J. Bommarito II
arXiv_AI
           
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 this https URL, providing an accessible resource for researchers, practitioners, and students alike.

Abstract (translated)

URL

https://arxiv.org/abs/2511.22095

PDF

https://arxiv.org/pdf/2511.22095.pdf

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