基于提示问答数据增强的小样本网络安全事件检测方法

汤萌萌; 郭渊博; 张晗; 白庆春; 陈庆礼; 张博闻

doi:10.11959/j.issn.1000-436x.2024105

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基于提示问答数据增强的小样本网络安全事件检测方法

学术论文 | 更新时间：2024-09-10

- 基于提示问答数据增强的小样本网络安全事件检测方法
- Few-shot cybersecurity event detection method by data augmentation with prompting question answering
- 通信学报 2024年45卷第8期页码：62-74
- 作者机构：
  
  1.信息工程大学密码工程学院,河南郑州 450001
  2.海南大学网络空间安全学院,海南海口 570100
  3.郑州大学网络空间安全学院,河南郑州 450001
  4.上海开放大学上海开放远程教育工程技术研究中心,上海 200082
  5.郑州浪潮数据技术有限公司,河南郑州 450001
- 作者简介：
  
  [ "汤萌萌（1989- ），女，河南信阳人，信息工程大学博士生，主要研究方向为信息安全、网络安全事件抽取。" ]
  [ "郭渊博（1975- ），男，陕西周至人，博士，海南大学教授、博士生导师，主要研究方向为网络防御、数据挖掘、机器学习和人工智能安全等。" ]
  [ "张晗（1985- ），女，河南项城人，郑州大学讲师，主要研究方向为自然语言处理、信息安全。" ]
  [ "白庆春（1990- ），女，上海人，上海开放大学助理研究员，主要研究方向为自然语言处理、机器学习、教育大数据分析。" ]
  [ "陈庆礼（1998- ），男，河南新乡人，信息工程大学硕士生，主要研究方向为人工智能安全。" ]
  [ "张博闻（1998- ），男，河南郑州人，郑州浪潮数据技术有限公司工程师，主要研究方向为计算机通信、网络安全等。" ]
- 基金信息：
  
  国家自然科学基金资助项目(62276091;62307028);河南省重大公益专项基金资助项目;Major Public Welfare Project of Henan Province(201300311200);上海市自然科学基金资助项目(23ZR1441800);上海市启明星项目扬帆专项基金资助项目(23YF1426100)
- DOI：10.11959/j.issn.1000-436x.2024105
  中图分类号： TP391
- 收稿：2024-02-05，
  
  修回：2024-05-14，
  
  纸质出版：2024-08-25
- 稿件说明：
移动端阅览
汤萌萌,郭渊博,张晗等.基于提示问答数据增强的小样本网络安全事件检测方法[J].通信学报,2024,45(08):62-74.

TANG Mengmeng,GUO Yuanbo,ZHANG Han,et al.Few-shot cybersecurity event detection method by data augmentation with prompting question answering[J].Journal on Communications,2024,45(08):62-74.
汤萌萌,郭渊博,张晗等.基于提示问答数据增强的小样本网络安全事件检测方法[J].通信学报,2024,45(08):62-74. DOI： 10.11959/j.issn.1000-436x.2024105.

TANG Mengmeng,GUO Yuanbo,ZHANG Han,et al.Few-shot cybersecurity event detection method by data augmentation with prompting question answering[J].Journal on Communications,2024,45(08):62-74. DOI： 10.11959/j.issn.1000-436x.2024105.

摘要

针对网络安全领域的事件识别标注数据较为匮乏且场景和语义复杂，难以构建准确的事件识别模型的问题，提出了一种基于提示问答数据增强的小样本网络安全事件检测方法。首先利用提示信息获取事件表示知识，并结合标签词映射网络安全事件类型，从未标注的文本中生成新的数据来扩充训练数据；然后使用生成的高置信度的伪标注实例和原始数据来微调模型，以增强模型对网络安全事件的语义理解能力；最后在2个网络安全领域数据集上进行了实验验证。结果表明，与其他基线方法相比，所提方法在低资源网络安全事件检测任务上具有很强的优越性。

Abstract

The cybersecurity field lacks sufficient annotated data for event recognition

and the scenarios and semantics are complex

making it difficult to construct accurate event recognition models. A few-shot cybersecurity event detection method by data augmentation with prompting question answering was proposed. Firstly

event representation knowledge was obtained using prompt information and combined with label words to map cybersecurity event types. New data was generated from unlabeled text to expand the training data. Then

the generated high-confidence pseudo-annotated instances and raw data were used to fine-tune the model to enhance its semantic understanding of cybersecurity events. Experimental verification was conducted on two datasets in cybersecurity. The result showes that the proposed method’s substantial superiority in low-resource network security event detection tasks compared to other baseline methods.

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references

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