融合对抗主动学习的网络安全知识三元组抽取

李涛; 郭渊博; 琚安康

doi:10.11959/j.issn.1000-436x.2020174

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融合对抗主动学习的网络安全知识三元组抽取

学术论文 | 更新时间：2024-06-05

- 融合对抗主动学习的网络安全知识三元组抽取
- Knowledge triple extraction in cybersecurity with adversarial active learning
- 通信学报 2020年41卷第10期页码：80-91
- 作者机构：
  
  信息工程大学密码工程学院，河南郑州 450001
- 作者简介：
  
  [ "李涛（1992- ），男，甘肃甘谷人，信息工程大学博士生，主要研究方向为网络威胁语义建模" ]
  [ "郭渊博（1975- ），男，陕西周至人，博士，信息工程大学教授、博士生导师，主要研究方向为大数据安全、态势感知" ]
  [ "琚安康（1995- ），男，河南辉县人，信息工程大学博士生，主要研究方向为多步攻击检测、异构安全数据融合" ]
- 基金信息：
  
  国家自然科学基金资助项目(61501515)
- DOI：10.11959/j.issn.1000-436x.2020174
  中图分类号： TP391
- 网络出版日期：2020-10，
  
  纸质出版日期：2020-10-25
- 稿件说明：
移动端阅览
李涛, 郭渊博, 琚安康. 融合对抗主动学习的网络安全知识三元组抽取[J]. 通信学报, 2020,41(10):80-91.

Tao LI, Yuanbo GUO, Ankang JU. Knowledge triple extraction in cybersecurity with adversarial active learning[J]. Journal on communications, 2020, 41(10): 80-91.
李涛, 郭渊博, 琚安康. 融合对抗主动学习的网络安全知识三元组抽取[J]. 通信学报, 2020,41(10):80-91. DOI： 10.11959/j.issn.1000-436x.2020174.

Tao LI, Yuanbo GUO, Ankang JU. Knowledge triple extraction in cybersecurity with adversarial active learning[J]. Journal on communications, 2020, 41(10): 80-91. DOI： 10.11959/j.issn.1000-436x.2020174.

摘要

针对当前网络安全领域知识获取中所依赖的流水线模式存在实体识别错误的传播，未考虑实体识别与关系抽取任务间的联系，以及模型训练缺乏标签语料的问题，提出一种融合对抗主动学习的端到端网络安全知识三元组抽取方法。首先，将实体识别与关系抽取通过联合标注策略建模为序列标注任务；然后，设计融合动态注意力机制的BiLSTM-LSTM模型实现实体与关系的联合抽取，并形成三元组；最后，基于对抗网络训练一个判别器模型，增量地筛选出高质量的待标注数据进行标注，并通过迭代训练不断提升联合抽取模型的性能。通过实验表明，所提方案中实体-关系联合抽取模型优于现有的网络安全知识抽取方案，并验证了对抗主动学习方法的有效性。

Abstract

Aiming at the problem that using pipeline methods for extracting cybersecurity knowledge triples may cause the errors propagation of entity recognition and did not consider the correlation between entity recognition and relation extraction

and training triple extraction model lacked labeled corpora

an end-to-end cybersecurity knowledge triple extraction method with adversarial active learning was proposed.For knowledge triple extraction

the conventional entity recognition and relation extraction were modelled as sequence labeling task through joint labeling strategy firstly.And then

a BiLSTM-LSTM-based model with dynamic attention mechanism was designed to jointly extract entities and relations

forming triples.Finally

with adversarial learning framework

a discriminator was trained to incrementally select high-quality samples for labeling

and the performance of the joint extraction model was continuously enhanced by iterative retraining.Experiments show that the proposed joint extraction model outperforms the existing cybersecurity knowledge triple extraction methods

and demonstrate the effectiveness of proposed adversarial active learning scheme.

关键词

Keywords

references

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