融合Focal Loss的网络威胁情报实体抽取

郭渊博; 李勇飞; 陈庆礼; 方晨; 胡阳阳

doi:10.11959/j.issn.1000-436x.2022132

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融合Focal Loss的网络威胁情报实体抽取

学术论文 | 更新时间：2024-06-05

- 融合Focal Loss的网络威胁情报实体抽取
- Fusion of Focal Loss’s cyber threat intelligence entity extraction
- 通信学报 2022年43卷第7期页码：85-92
- 作者机构：
  
  1. 信息工程大学密码工程学院，河南郑州 450001
  2. 加利福尼亚大学河滨分校，河滨 CA92521
- 作者简介：
  
  [ "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，主要研究方向为网络防御、数据挖掘、机器学习和人工智能安全等" ]
  [ "李勇飞（1998- ），男，河南开封人，信息工程大学硕士生，主要研究方向为威胁情报实体抽取及关系抽取等" ]
  [ "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" ]
  [ "方晨（1993- ），男，安徽宿松人，博士，信息工程大学讲师，主要研究方向为机器学习、隐私安全" ]
  [ "胡阳阳（1990- ），男，江苏南京人，加利福尼亚大学河滨分校博士生，主要研究方向为机器学习" ]
- 基金信息：
  
  国家自然科学基金资助项目(61501515);国家自然科学基金资助项目(61601515)
- DOI：10.11959/j.issn.1000-436x.2022132
  中图分类号： TP391
- 网络出版日期：2022-06，
  
  纸质出版日期：2022-07-25
- 稿件说明：
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郭渊博, 李勇飞, 陈庆礼, 等. 融合Focal Loss的网络威胁情报实体抽取[J]. 通信学报, 2022,43(7):85-92.

Yuanbo GUO, Yongfei LI, Qingli CHEN, et al. Fusion of Focal Loss’s cyber threat intelligence entity extraction[J]. Journal on communications, 2022, 43(7): 85-92.
郭渊博, 李勇飞, 陈庆礼, 等. 融合Focal Loss的网络威胁情报实体抽取[J]. 通信学报, 2022,43(7):85-92. DOI： 10.11959/j.issn.1000-436x.2022132.

Yuanbo GUO, Yongfei LI, Qingli CHEN, et al. Fusion of Focal Loss’s cyber threat intelligence entity extraction[J]. Journal on communications, 2022, 43(7): 85-92. DOI： 10.11959/j.issn.1000-436x.2022132.

摘要

网络威胁情报（CTI）蕴含丰富的威胁行为知识，及时分析处理威胁情报能够促进网络攻防由被动防御向主动防御的转变。当前多数威胁情报以自然语言文本的形式存在，包含大量非结构化数据，需要利用实体抽取方法将其转换为结构化数据以便后续处理。然而，由于威胁情报中包含大量漏洞名称、恶意软件、APT组织等专业词汇，且实体分布极不平衡，导致通用领域的实体抽取方法应用于威胁情报时受到极大限制。为此，提出一种融合Focal Loss的实体抽取模型，通过引入平衡因子和调制系数改进交叉熵损失函数，平衡样本分布。此外，针对威胁情报结构复杂且来源广泛，包含大量专业词汇的问题，在模型中增加单词和字符特征，有效改善了威胁情报中的OOV问题。实验结果表明，相较于现有主流模型BiLSTM和BiLSTM-CRF，所提模型在F1分数上分别提高了7.07%和4.79%，验证了引入Focal Loss和字符特征的有效性。

Abstract

Cyber threat intelligence contains a wealth of knowledge of threat behavior.Timely analysis and process of threat intelligence can promote the transformation of defense from passive to active.Nowadays

most threat intelligence that exists in the form of natural language texts contains a large amount of unstructured data

which needs to be converted into structured data for subsequent processing using entity extraction methods.However

since threat intelligence contains numerous terminology such as vulnerability names

malware and APT organizations

and the distribution of entities are extremely unbalanced

the performance of extraction methods in general field are severely limited when applied to threat intelligence.Therefore

an entity extraction model integrated with Focal Loss was proposed

which improved the cross-entropy loss function and balanced sample distribution by introducing balance factor and modulation coefficient.In addition

for the problem that threat intelligence had a complex structure and a wide range of sources

and contained a large number of professional words

token and character features were added to the model

which effectively improved OOV (out of vocabulary) problem in threat intelligence.Experiment results show that compared with existing mainstream model BiLSTM and BiLSTM-CRF

the F1 scores of the proposed model is increased by 7.07% and 4.79% respectively

which verifies the effectiveness of introducing Focal Loss and character features.

关键词

Keywords

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