Journal of Computer Applications ›› 2025, Vol. 45 ›› Issue (1): 24-31.DOI: 10.11772/j.issn.1001-9081.2024010136
• Artificial intelligence • Previous Articles Next Articles
Zidong CHENG1, Peng LI1,2(), Feng ZHU1
Received:
2024-02-06
Revised:
2024-03-27
Accepted:
2024-03-27
Online:
2024-05-09
Published:
2025-01-10
Contact:
Peng LI
About author:
CHENG Zidong, born in 1998, M. S. candidate. His research interests include internet of things security, knowledge graph construction.Supported by:
通讯作者:
李鹏
作者简介:
程子栋(1998—),男,安徽滁州人,硕士研究生,主要研究方向:物联网安全、知识图谱构建;基金资助:
CLC Number:
Zidong CHENG, Peng LI, Feng ZHU. Potential relation mining in internet of things threat intelligence knowledge graph[J]. Journal of Computer Applications, 2025, 45(1): 24-31.
程子栋, 李鹏, 朱枫. 物联网威胁情报知识图谱中潜在关系的挖掘[J]. 《计算机应用》唯一官方网站, 2025, 45(1): 24-31.
Add to citation manager EndNote|Ris|BibTeX
URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2024010136
节点/关系类型 | 节点/关系数 | |
---|---|---|
训练集 | 测试集 | |
设备 | 1 085 | 465 |
软件 | 3 640 | 1 561 |
操作系统 | 853 | 366 |
工具 | 56 | 24 |
弱点 | 95 | 40 |
漏洞 | 2 584 | 1 107 |
威胁执行者 | 95 | 40 |
攻击模式 | 391 | 168 |
恶意软件 | 379 | 163 |
威胁活动 | 219 | 94 |
缓解措施 | 675 | 289 |
指示器 | 598 | 256 |
has | 6 295 | 2 698 |
target | 5 694 | 2 440 |
use | 5 782 | 2 478 |
exploit | 8 497 | 3 641 |
related-to | 5 178 | 2 219 |
Tab. 1 Nodes and relations distribution of ITIKG
节点/关系类型 | 节点/关系数 | |
---|---|---|
训练集 | 测试集 | |
设备 | 1 085 | 465 |
软件 | 3 640 | 1 561 |
操作系统 | 853 | 366 |
工具 | 56 | 24 |
弱点 | 95 | 40 |
漏洞 | 2 584 | 1 107 |
威胁执行者 | 95 | 40 |
攻击模式 | 391 | 168 |
恶意软件 | 379 | 163 |
威胁活动 | 219 | 94 |
缓解措施 | 675 | 289 |
指示器 | 598 | 256 |
has | 6 295 | 2 698 |
target | 5 694 | 2 440 |
use | 5 782 | 2 478 |
exploit | 8 497 | 3 641 |
related-to | 5 178 | 2 219 |
模型 | has | target | use | exploit | related-to | 所有关系 | ||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
AUC | F1 | AUC | F1 | AUC | F1 | AUC | F1 | AUC | F1 | AUC | F1 | |
text-enhanced GAT | 0.732 | 0.715 | 0.678 | 0.603 | 0.742 | 0.713 | 0.694 | 0.655 | 0.761 | 0.761 | 0.721 | 0.689 |
HetGNN | 0.767 | 0.744 | 0.736 | 0.711 | 0.772 | 0.739 | 0.727 | 0.699 | 0.767 | 0.764 | 0.754 | 0.731 |
metapath2vec | 0.712 | 0.677 | 0.701 | 0.595 | 0.737 | 0.690 | 0.744 | 0.694 | 0.788 | 0.780 | 0.736 | 0.687 |
MMKRL | 0.779 | 0.736 | 0.746 | 0.726 | 0.788 | 0.759 | 0.775 | 0.756 | 0.792 | 0.786 | 0.776 | 0.753 |
ComplexGCN | 0.762 | 0.751 | 0.729 | 0.712 | 0.784 | 0.758 | 0.715 | 0.708 | 0.776 | 0.769 | 0.753 | 0.740 |
DM-HGNN | 0.829 | 0.811 | 0.835 | 0.806 | 0.824 | 0.801 | 0.825 | 0.803 | 0.834 | 0.816 | 0.829 | 0.807 |
Tab. 2 Experimental results of different models
模型 | has | target | use | exploit | related-to | 所有关系 | ||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
AUC | F1 | AUC | F1 | AUC | F1 | AUC | F1 | AUC | F1 | AUC | F1 | |
text-enhanced GAT | 0.732 | 0.715 | 0.678 | 0.603 | 0.742 | 0.713 | 0.694 | 0.655 | 0.761 | 0.761 | 0.721 | 0.689 |
HetGNN | 0.767 | 0.744 | 0.736 | 0.711 | 0.772 | 0.739 | 0.727 | 0.699 | 0.767 | 0.764 | 0.754 | 0.731 |
metapath2vec | 0.712 | 0.677 | 0.701 | 0.595 | 0.737 | 0.690 | 0.744 | 0.694 | 0.788 | 0.780 | 0.736 | 0.687 |
MMKRL | 0.779 | 0.736 | 0.746 | 0.726 | 0.788 | 0.759 | 0.775 | 0.756 | 0.792 | 0.786 | 0.776 | 0.753 |
ComplexGCN | 0.762 | 0.751 | 0.729 | 0.712 | 0.784 | 0.758 | 0.715 | 0.708 | 0.776 | 0.769 | 0.753 | 0.740 |
DM-HGNN | 0.829 | 0.811 | 0.835 | 0.806 | 0.824 | 0.801 | 0.825 | 0.803 | 0.834 | 0.816 | 0.829 | 0.807 |
1 | 官赛萍,靳小龙,贾岩涛,等.面向知识图谱的知识推理研究进展[J].软件学报, 2018, 29(10): 2966-2994. |
GUAN S P, JIN X L, JIA Y T, et al. Knowledge reasoning over knowledge graph: a survey [J]. Journal of Software, 2018, 29(10): 2966-2994. | |
2 | FANG Y, LU W, LIU X, et al. CircularE: a complex space circular correlation relational model for link prediction in knowledge graph embedding [J]. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2023, 31: 3162-3175. |
3 | LI M, WANG Y, ZHANG D, et al. Link prediction in knowledge graphs: a hierarchy-constrained approach [J]. IEEE Transactions on Big Data, 2022, 8(3): 630-643. |
4 | WANG J, WANG B, GAO J, et al. TDN: triplet distributor network for knowledge graph completion [J]. IEEE Transactions on Knowledge and Data Engineering, 2023, 35(12): 13002-13014. |
5 | LU X, WANG L, JIANG Z, et al. MMKRL: a robust embedding approach for multi-modal knowledge graph representation learning [J]. Applied Intelligence, 2022, 52(7): 7480-7497. |
6 | DONG Y, CHAWLA N V, SWAMI A. metapath2vec: scalable representation learning for heterogeneous networks [C]// Proceedings of the 23rd ACM SIGKDD International Conference on knowledge Discovery and Data Mining. New York: ACM, 2017: 135-144. |
7 | KIPF T N, WELLING M. Semi-supervised classification with graph convolutional networks [EB/OL]. [2023-09-20]. . |
8 | VELIČKOVIĆ P, CUCURULL G, CASANOVA A, et al. Graph attention networks [EB/OL]. [2023-10-20]. . |
9 | HAMILTON W L, YING Z, LESKOVEC J. Inductive representation learning on large graphs [C]// Proceedings of the 31st International Conference on Neural Information Processing Systems. Red Hook: Curran Associates Inc., 2017: 1025-1035. |
10 | HOCHREITER S, SCHMIDHUBER J. Long short-term memory [J]. Neural Computation, 1997, 9(8): 1735-1780. |
11 | SCHLICHTKRULL M, KIPF T N, BLOEM P, et al. Modeling relational data with graph convolutional networks [C]// Proceedings of the 2018 European Semantic Web Conference, LNCS 10843. Cham: Springer, 2018: 593-607. |
12 | ZHANG C, SONG D, HUANG C, et al. Heterogeneous graph neural network [C]// Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2019: 793-803. |
13 | ZEB A, SAIF S, CHEN J, et al. Complex graph convolutional network for link prediction in knowledge graphs [J]. Expert Systems with Applications, 2022, 200: No.116796. |
14 | YANG J, YANG L T, WANG H, et al. Tensor graph attention network for knowledge reasoning in Internet of Things [J]. IEEE Internet of Things Journal, 2022, 9(12): 9128-9137. |
15 | YU L, SUN L, DU B, et al. Heterogeneous graph representation learning with relation awareness [J]. IEEE Transactions on Knowledge and Data Engineering, 2023, 35(6): 5935-5947. |
16 | YUAN L, BAI Y, XING Z, et al. Predicting entity relations across different security databases by using graph attention network [C]// IEEE 45th Annual Computers, Software, and Applications Conference. Piscataway: IEEE, 2021: 834-843. |
17 | ANSARIZADEH F, TAY D B, THIRUVADY D, et al. Deterministic sampling in heterogeneous graph neural networks [J]. Pattern Recognition Letters, 2023, 172: 74-81. |
18 | KATZ L. A new status index derived from sociometric analysis [J]. Psychometrika, 1953, 18(1): 39-43. |
19 | FREEMAN L C. A set of measures of centrality based on betweenness [J]. Sociometry, 1977, 40(1): 35-41. |
20 | NIST Computer Security Division, Information Technology Laboratory. National vulnerability database [DB/OL]. [2023-05-08]. . |
21 | MITRE. Common weakness enumeration [DB/OL]. [2023-05-08]. . |
22 | MITRE. Adversarial tactics, techniques, and common knowledge [DB/OL]. [2023-05-08]. . |
23 | MITRE. Common vulnerabilities and exposures [EB/OL]. [2023-05-08]. . |
24 | MITRE. Common attack pattern enumerations and classifications [EB/OL]. [2023-05-18]. . |
25 | MITRE. Structured Threat Information eXpression — STIX[EB/OL]. [2023-05-08]. . |
26 | SOLÁ L, ROMANCE M, CRIADO R, et al. Eigenvector centrality of nodes in multiplex networks [J]. Chaos, 2013, 23(3): No.033131. |
27 | MA N, GUAN J, ZHAO Y. Bringing PageRank to the citation analysis [J]. Information Processing and Management, 2008, 44 (2): 800-810. |
28 | GROVER A, LESKOVEC J. node2vec: scalable feature learning for networks [C]// Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2016: 855-864. |
29 | DEVLIN J, CHANG M W, LEE K, et al. BERT: pre-training of deep bidirectional transformers for language understanding [C]// Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1(Long and Short Papers). Stroudsburg: ACL, 2019: 4171-4186. |
30 | PEROZZI B, AL-RFOU R, SKIENA S. DeepWalk: online learning of social representations [C]// Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2014: 701-710. |
[1] | Xueqiang LYU, Tao WANG, Xindong YOU, Ge XU. HTLR: named entity recognition framework with hierarchical fusion of multi-knowledge [J]. Journal of Computer Applications, 2025, 45(1): 40-47. |
[2] | Rui LI, Guanfeng LI, Dezhou HU, Wenxin GAO. Knowledge graph multi-hop reasoning model fusing path and subgraph features [J]. Journal of Computer Applications, 2025, 45(1): 32-39. |
[3] | Wenbo ZHAO, Zitong MA, Zhe YANG. Link prediction model based on directed hypergraph adaptive convolution [J]. Journal of Computer Applications, 2025, 45(1): 15-23. |
[4] | Tingjie TANG, Jiajin HUANG, Jin QIN. Session-based recommendation with graph auxiliary learning [J]. Journal of Computer Applications, 2024, 44(9): 2711-2718. |
[5] | Guixiang XUE, Hui WANG, Weifeng ZHOU, Yu LIU, Yan LI. Port traffic flow prediction based on knowledge graph and spatio-temporal diffusion graph convolutional network [J]. Journal of Computer Applications, 2024, 44(9): 2952-2957. |
[6] | Jie WU, Ansi ZHANG, Maodong WU, Yizong ZHANG, Congbao WANG. Overview of research and application of knowledge graph in equipment fault diagnosis [J]. Journal of Computer Applications, 2024, 44(9): 2651-2659. |
[7] | Yubo ZHAO, Liping ZHANG, Sheng YAN, Min HOU, Mao GAO. Relation extraction between discipline knowledge entities based on improved piecewise convolutional neural network and knowledge distillation [J]. Journal of Computer Applications, 2024, 44(8): 2421-2429. |
[8] | Jianjing LI, Guanfeng LI, Feizhou QIN, Weijun LI. Multi-relation approximate reasoning model based on uncertain knowledge graph embedding [J]. Journal of Computer Applications, 2024, 44(6): 1751-1759. |
[9] | Youren YU, Yangsen ZHANG, Yuru JIANG, Gaijuan HUANG. Chinese named entity recognition model incorporating multi-granularity linguistic knowledge and hierarchical information [J]. Journal of Computer Applications, 2024, 44(6): 1706-1712. |
[10] | Xinrui LIN, Xiaofei WANG, Yan ZHU. Academic anomaly citation group detection based on local extended community detection [J]. Journal of Computer Applications, 2024, 44(6): 1855-1861. |
[11] | Jie GUO, Jiayu LIN, Zuhong LIANG, Xiaobo LUO, Haitao SUN. Recommendation method based on knowledge‑awareness and cross-level contrastive learning [J]. Journal of Computer Applications, 2024, 44(4): 1121-1127. |
[12] | Xiaoyan ZHAO, Yan KUANG, Menghan WANG, Peiyan YUAN. Device-to-device content sharing mechanism based on knowledge graph [J]. Journal of Computer Applications, 2024, 44(4): 995-1001. |
[13] | Dapeng XU, Xinmin HOU. Feature selection method for graph neural network based on network architecture design [J]. Journal of Computer Applications, 2024, 44(3): 663-670. |
[14] | Linqin WANG, Te ZHANG, Zhihong XU, Yongfeng DONG, Guowei YANG. Fusing entity semantic and structural information for knowledge graph reasoning [J]. Journal of Computer Applications, 2024, 44(11): 3371-3378. |
[15] | Wenjuan JIANG, Yi GUO, Jiaojiao FU. Reasoning question answering model of complex temporal knowledge graph with graph attention [J]. Journal of Computer Applications, 2024, 44(10): 3047-3057. |
Viewed | ||||||
Full text |
|
|||||
Abstract |
|
|||||