Dynamic Vulnerability Knowledge Graph Construction via Multi-Source Data Fusion and Large Language Model Reasoning
Guardado en:
| Udgivet i: | Electronics vol. 14, no. 12 (2025), p. 2334-2360 |
|---|---|
| Hovedforfatter: | |
| Andre forfattere: | , , , , , |
| Udgivet: |
MDPI AG
|
| Fag: | |
| Online adgang: | Citation/Abstract Full Text + Graphics Full Text - PDF |
| Tags: |
Ingen Tags, Vær først til at tagge denne postø!
|
MARC
| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3223907961 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2079-9292 | ||
| 024 | 7 | |a 10.3390/electronics14122334 |2 doi | |
| 035 | |a 3223907961 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231458 |2 nlm | ||
| 100 | 1 | |a Liu Ruitong |u School of Cyber Engineering, Xidian University, Xi’an 710126, China; 22009200603@stu.xidian.edu.cn (R.L.); 23009201024@stu.xidian.edu.cn (Z.D.); 23009200774@stu.xidian.edu.cn (J.H.); clpeng@xidian.edu.cn (C.P.) | |
| 245 | 1 | |a Dynamic Vulnerability Knowledge Graph Construction via Multi-Source Data Fusion and Large Language Model Reasoning | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a With the increasing number of network security threats and the frequent occurrence of software vulnerability attacks, the effective management and large-scale retrieval of vulnerability data have become urgent needs. Existing vulnerability information is scattered across heterogeneous sources and is difficult to integrate, which in turn makes it hard for security analysts to quickly retrieve and analyze relevant security knowledge. To address this problem, this paper proposes a method to construct a vulnerability knowledge graph by integrating multi-source vulnerability data, combining graph embedding technology with large language model reasoning to aggregate, infer, and enrich vulnerability knowledge. Experiments demonstrated that our domain-tuned Bidirectional Long Short-Term Memory–Conditional Random Field (BiLSTM-CRF) named entity recognition (NER), enhanced with a cybersecurity dictionary, achieved a 90.1% F1-score for entity extraction. For link prediction, a hybrid Graph Attention Network fused with GPT-3 reasoning boosted Hits1 by 0.137, Hits3 by 0.116, and Hits10 by 0.101 over the baseline. These results confirm that our approach markedly enhanced entity identification and relationship inference, yielding a more complete and dynamically updatable cybersecurity knowledge graph. | |
| 653 | |a Language | ||
| 653 | |a Forgery | ||
| 653 | |a Dictionaries | ||
| 653 | |a Accuracy | ||
| 653 | |a Large language models | ||
| 653 | |a Graphs | ||
| 653 | |a Conditional random fields | ||
| 653 | |a Ontology | ||
| 653 | |a Neural networks | ||
| 653 | |a Reasoning | ||
| 653 | |a Cybersecurity | ||
| 653 | |a Software reliability | ||
| 653 | |a Databases | ||
| 653 | |a Data collection | ||
| 653 | |a Data integration | ||
| 653 | |a Natural language processing | ||
| 653 | |a Network analysis | ||
| 653 | |a Knowledge representation | ||
| 653 | |a Semantics | ||
| 700 | 1 | |a Xie Yaxuan |u School of Cyber Engineering, Xidian University, Xi’an 710126, China; 22009200603@stu.xidian.edu.cn (R.L.); 23009201024@stu.xidian.edu.cn (Z.D.); 23009200774@stu.xidian.edu.cn (J.H.); clpeng@xidian.edu.cn (C.P.) | |
| 700 | 1 | |a Dang Zexu |u School of Cyber Engineering, Xidian University, Xi’an 710126, China; 22009200603@stu.xidian.edu.cn (R.L.); 23009201024@stu.xidian.edu.cn (Z.D.); 23009200774@stu.xidian.edu.cn (J.H.); clpeng@xidian.edu.cn (C.P.) | |
| 700 | 1 | |a Hao Jinyi |u School of Cyber Engineering, Xidian University, Xi’an 710126, China; 22009200603@stu.xidian.edu.cn (R.L.); 23009201024@stu.xidian.edu.cn (Z.D.); 23009200774@stu.xidian.edu.cn (J.H.); clpeng@xidian.edu.cn (C.P.) | |
| 700 | 1 | |a Quan Xiaowen |u Yuanjiang Shengbang Safety Technology Group Co., Beijing 100085, China; qxw19@mails.tsinghua.edu.cn | |
| 700 | 1 | |a Xiao Yongcai |u State Grid Jiangxi Electric Power Research Institute, Nanchang 330052, China; dky_xiaoyc@jx.sgcc.com.cn | |
| 700 | 1 | |a Peng Chunlei |u School of Cyber Engineering, Xidian University, Xi’an 710126, China; 22009200603@stu.xidian.edu.cn (R.L.); 23009201024@stu.xidian.edu.cn (Z.D.); 23009200774@stu.xidian.edu.cn (J.H.); clpeng@xidian.edu.cn (C.P.) | |
| 773 | 0 | |t Electronics |g vol. 14, no. 12 (2025), p. 2334-2360 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3223907961/abstract/embedded/IZYTEZ3DIR4FRXA2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3223907961/fulltextwithgraphics/embedded/IZYTEZ3DIR4FRXA2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3223907961/fulltextPDF/embedded/IZYTEZ3DIR4FRXA2?source=fedsrch |