基于软提示微调和强化学习的网络安全命名实体识别方法研究

田泽庶; 刘春雨; 张云婷; 张嘉宇; 孟超; 张宏莉

doi:10.11959/j.issn.1000-436x.2024183

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基于软提示微调和强化学习的网络安全命名实体识别方法研究

学术论文 | 更新时间：2024-11-14

- 基于软提示微调和强化学习的网络安全命名实体识别方法研究
- Research on named entity recognition method in cybersecurity based on soft prompt tuning and reinforcement learning
- 通信学报 2024年45卷第10期页码：1-16
- 作者机构：
  
  哈尔滨工业大学计算学部，黑龙江哈尔滨 150001
- 作者简介：
  
  [ "田泽庶（1997- ），男，黑龙江哈尔滨人，哈尔滨工业大学博士生，主要研究方向为信息抽取、知识图谱构建等。" ]
  刘春雨（1994- ），女，黑龙江哈尔滨人，哈尔滨工业大学博士生，主要研究方向为城市计算、服务计算等。
  张云婷（1997- ），女，黑龙江哈尔滨人，哈尔滨工业大学博士生，主要研究方向为网络与信息安全、对抗文本生成等。
  张嘉宇（1997- ），男，山西太原人，哈尔滨工业大学博士生，主要研究方向为知识图谱构建、社交立场分析等。
  孟超（1991- ），男，河南鹿邑人，哈尔滨工业大学博士生，主要研究方向为社交网络分析、社交立场分析等。
  张宏莉（1973- ），女，吉林榆树人，博士，哈尔滨工业大学教授、博士生导师，主要研究方向为社交网络分析、网络与信息安全等。zhanghongli@hit.edu.cn
- 基金信息：
  
  国家重点研发计划基金资助项目(2016QY03D0501;2017YFB0803304);黑龙江省自然科学基金资助项目(LH2023F018)
- DOI：10.11959/j.issn.1000-436x.2024183
  中图分类号： TP389.1
- 收稿：2024-07-04，
  
  修回：2024-09-29，
  
  纸质出版：2024-10-25
- 稿件说明：
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田泽庶,刘春雨,张云婷等.基于软提示微调和强化学习的网络安全命名实体识别方法研究[J].通信学报,2024,45(10):1-16.

TIAN Zeshu,LIU Chunyu,ZHANG Yunting,et al.Research on named entity recognition method in cybersecurity based on soft prompt tuning and reinforcement learning[J].Journal on Communications,2024,45(10):1-16.
田泽庶,刘春雨,张云婷等.基于软提示微调和强化学习的网络安全命名实体识别方法研究[J].通信学报,2024,45(10):1-16. DOI： 10.11959/j.issn.1000-436x.2024183.

TIAN Zeshu,LIU Chunyu,ZHANG Yunting,et al.Research on named entity recognition method in cybersecurity based on soft prompt tuning and reinforcement learning[J].Journal on Communications,2024,45(10):1-16. DOI： 10.11959/j.issn.1000-436x.2024183.

摘要

随着网络技术的迅猛发展，新型网络安全威胁不断涌现，网络安全命名实体识别重要性日益增加。针对现有基于大语言模型的命名实体识别方法在网络安全领域识别准确率差的问题，提出了一种结合软提示微调和强化学习的网络安全命名实体识别方法。通过结合软提示微调技术，针对网络安全领域的复杂性，精细调整大语言模型的识别能力，提升模型对网络安全命名实体的识别准确率，同时优化训练效率。此外，提出了基于强化学习的网络安全实体筛选器，可以有效去除训练集中的低质量标注，从而提升识别准确率。在2个开源基准网络安全实体识别数据集上评估了所提方法，实验结果表明，所提方法的F1值优于现有最佳的网络安全命名实体识别方法。

Abstract

As network technology rapidly advanced

new cybersecurity threats constantly emerged

increasing the importance of cybersecurity named entity recognition. To address the problem of poor recognition accuracy in named entity recognition methods based on large language models in the cybersecurity domain

a novel cybersecurity named entity recognition method that combined soft prompt tuning and reinforcement learning was proposed. By integrating the soft prompt tuning technique

the method precisely adjusted the recognition capabilities of large language models to handle the complexity of the cybersecurity domain

improving recognition accuracy for cybersecurity named entities while optimizing training efficiency. Additionally

a reinforcement learning-based instance filter was proposed

which effectively removed low-quality annotations from the training set

further enhancing recognition accuracy. The proposed method was evaluated on two benchmark cybersecurity NER datasets

with experimental results demonstrating superior performance in F1 score compared to state-of-the-art cybersecurity NER methods.

关键词

Keywords

references

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