Predicting inter-state cyberattacks with graph-text fusion using graph neural networks and large language models
文献类型：期刊论文
| 作者 | Dong, Jiping(); Hao, Mengmeng(); Ding, Fangyu(); Chen, Shuai(); Wu, Jiajie; Zhuo, Jun() |
| 刊名 | ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
 |
| 出版日期 | 2026-02-01 |
| 卷号 | 165页码:113465 |
| 关键词 | Cyberattack prediction Graph neural network Large language model Multimodal fusion Bilateral relations |
| ISSN号 | 0952-1976 |
| DOI | 10.1016/j.engappai.2025.113465 |
| 产权排序 | 1 |
| 文献子类 | Article |
| 英文摘要 | Accurate forecasting of inter-state cyberattacks is crucial for the timely prevention of security risks. However, the dynamic evolution of international relations and the heterogeneity of multi-source data make this task significant challenging in both data integration and model design. To address these issues, we propose GeoDyG-LLM (Geopolitical Dynamic Graph-Large Language Model), a unified multimodal framework for geopolitically grounded cyberattack prediction. The framework models inter-state interactions by integrating dynamic graph neural networks (GNNs) with large language models (LLMs), jointly capturing the temporal, structural, and semantic dependencies from historical cyberattack records and news events. Methodologically, the framework (i) employs a dynamic multi-view GNN with a learnable projector, whose node embeddings are layer-wise injected into the Transformer layers of the LLM; (ii) leverages LLM-based semantic refinement to construct a high-quality multimodal dataset from raw data sources; and (iii) adopts a geopolitically-aware negative sampling strategy to generate informative and balanced training pairs. Experimental results show that GeoDyG-LLM (8B) substantially outperforms baselines, achieving an F1 score of 0.888 on cyberattack prediction. Moreover, the fine-tuned model generates coherent natural-language explanations aligned with historical and contextual evidence, enhancing interpretability and supporting practical geopolitical cyber risk analysis. |
| URL标识 | 查看原文 |
| WOS研究方向 | Automation & Control Systems ; Computer Science ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:001638065800002 |
| 出版者 | PERGAMON-ELSEVIER SCIENCE LTD |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/219756]  |
| 专题 | 生态系统网络观测与模拟院重点实验室_外文论文 |
| 通讯作者 | Wu, Jiajie |
| 作者单位 | Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, 11A Datun Rd, Beijing 100101, Peoples R China; Univ Chinese Acad Sci, Coll Resources & Environm, 1 Yanqihu East Rd, Beijing 101408, Peoples R China |
| 推荐引用方式 GB/T 7714 | Dong, Jiping,Hao, Mengmeng,Ding, Fangyu,et al. Predicting inter-state cyberattacks with graph-text fusion using graph neural networks and large language models[J]. ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE,2026,165:113465. |
| APA | Dong, Jiping,Hao, Mengmeng,Ding, Fangyu,Chen, Shuai,Wu, Jiajie,&Zhuo, Jun.(2026).Predicting inter-state cyberattacks with graph-text fusion using graph neural networks and large language models.ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE,165,113465. |
| MLA | Dong, Jiping,et al."Predicting inter-state cyberattacks with graph-text fusion using graph neural networks and large language models".ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 165(2026):113465. |
入库方式： OAI收割
来源：地理科学与资源研究所
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