DynaSynth：面向非结构化网络安全语料的大语言模型微调数据动态生成代理工具

关永健; 朴乘锴; 王布宏; 赵博夫; 李思琦; 赵正阳

doi:10.11959/j.issn.2096-109x.25249

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DynaSynth：面向非结构化网络安全语料的大语言模型微调数据动态生成代理工具

学术论文 | 更新时间：2026-05-11

- DynaSynth：面向非结构化网络安全语料的大语言模型微调数据动态生成代理工具
- DynaSynth: a large language model fine-tuning data dynamic synthesis agent tool for unstructured cybersecurity corpus
- 网络与信息安全学报 2026年12卷第2期页码：156-168
- 作者机构：
  
  空军工程大学信息与导航学院，陕西西安 710077
- 作者简介：
  
  [ "关永健（1997- ），男，空军工程大学信息与导航学院博士生，主要研究方向为网络空间安全与大语言模型。" ]
  [ "朴乘锴（1991− ），男，空军工程大学信息与导航学院讲师，主要研究方向为网络空间安全与大语言模型安全。" ]
  [ "王布宏（1975- ），男，空军工程大学信息与导航学院教授，主要研究方向为空管信息系统安全和大语言模型。" ]
  [ "赵博夫（1986- ），男，空军工程大学信息与导航学院博士生，主要研究方向为网络空间安全。" ]
  [ "李思琦（2002- ），女，空军工程大学信息与导航学院硕士生，主要研究方向为大语言模型时间序列异常检测。" ]
  [ "赵正阳（1997- ），男，空军工程大学信息与导航学院博士生，主要研究方向为对抗学习与深度学习应用。" ]
- 基金信息：
  
  国家自然科学基金资助项目(62472437)
- DOI：10.11959/j.issn.2096-109x.25249
  中图分类号： TP393
- 收稿：2025-12-27，
  
  修回：2026-03-29，
  
  录用：2026-03-30，
  
  纸质出版：2026-04-25
- 稿件说明：
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关永健, 朴乘锴, 王布宏, 等. DynaSynth：面向非结构化网络安全语料的大语言模型微调数据动态生成代理工具[J]. 网络与信息安全学报, 2026, 12(2): 156-168.

Guan Yongjian, Piao Chengkai, Wang Buhong, et al. DynaSynth: a large language model fine-tuning data dynamic synthesis agent tool for unstructured cybersecurity corpus[J]. Chinese Journal of Network and Information Security, 2026, 12(2): 156-168.
关永健, 朴乘锴, 王布宏, 等. DynaSynth：面向非结构化网络安全语料的大语言模型微调数据动态生成代理工具[J]. 网络与信息安全学报, 2026, 12(2): 156-168. DOI： 10.11959/j.issn.2096-109x.25249.

Guan Yongjian, Piao Chengkai, Wang Buhong, et al. DynaSynth: a large language model fine-tuning data dynamic synthesis agent tool for unstructured cybersecurity corpus[J]. Chinese Journal of Network and Information Security, 2026, 12(2): 156-168. DOI： 10.11959/j.issn.2096-109x.25249.

摘要

随着模型训练技术和数据规模的迅速发展，大语言模型已经能够胜任各种通用任务，但在网络安全垂直领域，大语言模型的发展仍受数据瓶颈的制约。为解决这一难题，提出了面向非结构化语料的大语言模型微调数据动态生成代理工具DynaSynth。该工具首先利用向量嵌入辅助模型将原始文本切割为文本块，并转化为可检索增强的向量数据；然后通过对文档进行受众－体裁分析，指导生成具有多样化问答对风格且兼具通用性的数据。在此过程中，用户可以通过可视化界面实时查看生成的数据，并借助提示工程对生成策略进行优化调整，确保生成内容精准契合实际需求。实验结果表明，使用DynaSynth生成的微调数据能够显著提高大语言模型在垂直领域任务中的性能。该工具不仅具备高效的数据生成能力，还能够有效探索数据的多样化生成路径。

Abstract

With the rapid development of model training technologies and data scale

large language models have been able to handle various general tasks. However

in the vertical field of cybersecurity

the development of large language models is still constrained by data bottlenecks. To solve this problem

a data dynamic generation agent tool named DynaSynth for large language models based on unstructured text corpora was proposed. This tool first uses vector embeddings to assist the model in splitting the original text into blocks and converting them into retrievable enhanced vector data; then

through audience and genre analysis of the documents

it guides the generation of data with diverse question-answer pair styles and universality. During this process

users can view the generated data in real time through a visual interface and optimize the generation strategy by leveraging prompt engineering to ensure that the generated content precisely meets the actual needs. Experimental results show that the fine-tuning data generated by DynaSynth can significantly improve the performance of large language models in vertical domain tasks. This tool not only has an efficient data generation capability

but also effectively explores the diverse generation paths of data.

关键词

Keywords

references

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