构建多维度代码数据集SBAN，助力大模型理解软件行为与检测恶意代码。
SBAN: A Framework & Multi-Dimensional Dataset for Large Language Model Pre-Training and Software Code Mining
- 构建包含二进制、汇编、源码与自然语言的四层多模态数据集。
- 覆盖超300万样本，含290万正常程序与67.2万恶意软件。
- 适用于代码理解、漏洞检测、跨表示学习等智能开发任务。
本文提出SBAN（源码、二进制、汇编与自然语言描述），一个大规模多维度数据集，旨在推动大语言模型在软件代码分析中的预训练与评估。SBAN包含超过300万个样本，其中290万为良性程序，67.2万为恶意软件，每个样本均以二进制代码、汇编指令、自然语言描述和源代码四种互补形式呈现。该多模态结构支持跨表示学习、软件语义理解及自动化恶意代码检测。除安全应用外，还适用于代码翻译、解释等异构数据处理任务。特别适合用于训练深度模型，如Transformer和各类大语言模型架构。通过连接低层机器表示与高层人类语义，SBAN为构建能推理代码逻辑的智能系统提供坚实基础。我们相信该数据集将为挖掘软件行为、提升安全分析能力以及增强大模型在代码挖掘中的预训练与微调性能开辟新机遇。
原文摘要 · Abstract (English)
This paper introduces SBAN (Source code, Binary, Assembly, and Natural Language Description), a large-scale, multi-dimensional dataset designed to advance the pre-training and evaluation of large language models (LLMs) for software code analysis. SBAN comprises more than 3 million samples, including 2.9 million benign and 672,000 malware respectively, each represented across four complementary layers: binary code, assembly instructions, natural language descriptions, and source code. This unique multimodal structure enables research on cross-representation learning, semantic understanding of software, and automated malware detection. Beyond security applications, SBAN supports broader tasks such as code translation, code explanation, and other software mining tasks involving heterogeneous data. It is particularly suited for scalable training of deep models, including transformers and other LLM architectures. By bridging low-level machine representations and high-level human semantics, SBAN provides a robust foundation for building intelligent systems that reason about code. We believe that this dataset opens new opportunities for mining software behavior, improving security analytics, and enhancing LLM capabilities in pre-training and fine-tuning tasks for software code mining.
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