情报杂志2026，Vol.45Issue(4)：75-83,9.DOI:10.3969/j.issn.1002-1965.2026.04.010
融合语义与结构特征的威胁情报文本攻击意图识别方法
Threat Intelligence Text Attack Intent Recognition Method Integrating Semantic and Structural Features
摘要
Abstract
[Purpose]Threat intelligence texts provide important data support for security decision-making and intelligence analysis by tracking and analyzing key attack techniques and tactics.How to accurately identify attack intentions and tactical features from complex and unstructured text has become a key issue in promoting automated intelligence analysis and knowledge system construction.[Method]To this end,the study proposes a semantic structural fusion modeling method for threat intelligence texts.Firstly,to address the issues of se-vere unstructured threat intelligence text and sparse label distribution,a feature generation module centered on topic semantic modeling and context clustering was designed to enhance the separability of text expression.On this basis,the BERT pre-trained model is used to extract the contextual semantic vector representation of the text,and dimension level semantic interaction is achieved by introducing cross feature enhancement structure;Finally,to address the inherent issue of imbalanced class distribution in threat intelligence data,a class balanced Focal loss function is used to optimize the model training process.[Result/Conclusion]The experimental results show that the method exhibits excellent performance on real threat intelligence datasets,especially in multi label recognition tasks,with the highest F0.5 value reaching 92.63%,demonstrating good label modeling ability and practical application value.关键词
威胁情报/威胁情报识别/文本特征构建/上下文建模/多标签学习/深度融合模型Key words
threat intelligence/threat intelligence recognition/text feature construction/contextual modeling/multi-label learning/deep fusion model分类
社会科学引用本文复制引用
秦振凯,农熏衣,罗起宁,臧志栋,李秀霞,于小川..融合语义与结构特征的威胁情报文本攻击意图识别方法[J].情报杂志,2026,45(4):75-83,9.基金项目
国家社会科学基金项目"跨学科知识元迁移组合与学术创新机会发现研究"(编号:22BTQ061) （编号:22BTQ061）
广西哲学社会科学研究项目"人工智能背景下金融情报感知技术及其应用研究"(编号:24TQF007) （编号:24TQF007）
广西重点研发计划项目"基于大模型的公安案件线索智能分析与处理关键技术研究与应用"(编号:桂科AB22035034) （编号:桂科AB22035034）
广西警察学院校级课题"基于多任务学习的案件情报挖掘技术研究"(编号:2024KYYB04)研究成果. （编号:2024KYYB04）
