{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T04:21:04Z","timestamp":1774930864596,"version":"3.50.1"},"reference-count":117,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2025,3,7]],"date-time":"2025-03-07T00:00:00Z","timestamp":1741305600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National Key Research and Development Program of China","award":["2023YFB3107200"],"award-info":[{"award-number":["2023YFB3107200"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>Inter-state cyberattacks are increasingly becoming a major hidden threat to national security and global order. However, current prediction models are often constrained by single-source data due to insufficient consideration of complex influencing factors, resulting in limitations in understanding and predicting cyberattacks. To address this issue, we comprehensively consider multiple data sources including cyberattacks, bilateral interactions, armed conflicts, international trade, and national attributes, and propose an interpretable multimodal data fusion framework for predicting cyberattacks among countries. On one hand, we design a dynamic multi-view graph neural network model incorporating temporal interaction attention and multi-view attention, which effectively captures time-varying dynamic features and the importance of node representations from various modalities. Our proposed model exhibits greater performance in comparison to many cutting-edge models, achieving an F1 score of 0.838. On the other hand, our interpretability analysis reveals unique characteristics of national cyberattack behavior. For example, countries with different income levels show varying preferences for data sources, reflecting their different strategic focuses in cyberspace. This unveils the factors and regional differences that affect cyberattack prediction, enhancing the transparency and credibility of the proposed model.<\/jats:p>","DOI":"10.3390\/bdcc9030063","type":"journal-article","created":{"date-parts":[[2025,3,7]],"date-time":"2025-03-07T07:05:46Z","timestamp":1741331146000},"page":"63","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A Novel Multimodal Data Fusion Framework: Enhancing Prediction and Understanding of Inter-State Cyberattacks"],"prefix":"10.3390","volume":"9","author":[{"given":"Jiping","family":"Dong","sequence":"first","affiliation":[{"name":"Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"Laboratory of Cyberspace Geography, Chinese Academy of Sciences, The Ministry of Public Security of the People\u2019s Republic of China, Beijing 100101, China"},{"name":"College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 101408, China"}]},{"given":"Mengmeng","family":"Hao","sequence":"additional","affiliation":[{"name":"Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"Laboratory of Cyberspace Geography, Chinese Academy of Sciences, The Ministry of Public Security of the People\u2019s Republic of China, Beijing 100101, China"},{"name":"College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 101408, China"}]},{"given":"Fangyu","family":"Ding","sequence":"additional","affiliation":[{"name":"Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"Laboratory of Cyberspace Geography, Chinese Academy of Sciences, The Ministry of Public Security of the People\u2019s Republic of China, Beijing 100101, China"},{"name":"College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 101408, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3623-1532","authenticated-orcid":false,"given":"Shuai","family":"Chen","sequence":"additional","affiliation":[{"name":"Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"Laboratory of Cyberspace Geography, Chinese Academy of Sciences, The Ministry of Public Security of the People\u2019s Republic of China, Beijing 100101, China"},{"name":"College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 101408, China"}]},{"given":"Jiajie","family":"Wu","sequence":"additional","affiliation":[{"name":"Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"Laboratory of Cyberspace Geography, Chinese Academy of Sciences, The Ministry of Public Security of the People\u2019s Republic of China, Beijing 100101, China"},{"name":"College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 101408, China"}]},{"given":"Jun","family":"Zhuo","sequence":"additional","affiliation":[{"name":"Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"Laboratory of Cyberspace Geography, Chinese Academy of Sciences, The Ministry of Public Security of the People\u2019s Republic of China, Beijing 100101, China"},{"name":"College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 101408, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4154-5969","authenticated-orcid":false,"given":"Dong","family":"Jiang","sequence":"additional","affiliation":[{"name":"Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"Laboratory of Cyberspace Geography, Chinese Academy of Sciences, The Ministry of Public Security of the People\u2019s Republic of China, Beijing 100101, China"},{"name":"College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 101408, China"}]}],"member":"1968","published-online":{"date-parts":[[2025,3,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"161","DOI":"10.15575\/politicon.v3i2.12660","article-title":"The Implication of Cyberspace Towards State Geopolitics","volume":"3","author":"Ramadhan","year":"2021","journal-title":"Politicon J. Ilmu Polit."},{"key":"ref_2","unstructured":"Vanberghen, C. (2024, July 03). Cyberspace and the 21st Century Arms Race. Available online: https:\/\/digitalsociety.eui.eu\/publication\/cyberspace-and-the-21st-century-arms-race\/."},{"key":"ref_3","unstructured":"ASD (2024, July 03). ASD Cyber Threat Report 2022\u20132023, Available online: https:\/\/www.cyber.gov.au\/about-us\/view-all-content\/reports-and-statistics\/asd-cyber-threat-report-july-2022-june-2023."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1093\/jogss\/ogw006","article-title":"Repression, Education, and Politically Motivated Cyberattacks","volume":"1","author":"Asal","year":"2016","journal-title":"J. Glob. Secur. Stud."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1080\/23738871.2022.2041060","article-title":"Democracy and Cyberconflict: How Regime Type Affects State-Sponsored Cyberattacks","volume":"7","author":"Li","year":"2022","journal-title":"J. Cyber Policy"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1057\/s41599-023-01560-x","article-title":"Exploring the Global Geography of Cybercrime and Its Driving Forces","volume":"10","author":"Chen","year":"2023","journal-title":"Humanit. Soc. Sci. Commun."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Kumar, S., and Carley, K.M. (2016, January 28\u201330). Understanding DDoS Cyber-Attacks Using Social Media Analytics. Proceedings of the 2016 IEEE Conference on Intelligence and Security Informatics (ISI), Tucson, AZ, USA.","DOI":"10.1109\/ISI.2016.7745480"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Ben Fredj, O., Mihoub, A., Krichen, M., Cheikhrouhou, O., and Derhab, A. (2020, January 4\u20136). CyberSecurity Attack Prediction: A Deep Learning Approach. Proceedings of the 13th International Conference on Security of Information and Networks, Istanbul, Turkey.","DOI":"10.1145\/3433174.3433614"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"102696","DOI":"10.1016\/j.cose.2022.102696","article-title":"Joint Prediction on Security Event and Time Interval through Deep Learning","volume":"117","author":"Wu","year":"2022","journal-title":"Comput. Secur."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Chen, Y.Z., Huang, Z.G., Xu, S., and Lai, Y.C. (2015). Spatiotemporal Patterns and Predictability of Cyberattacks. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0131501"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Werner, G., Yang, S., and McConky, K. (2017, January 4\u20136). Time Series Forecasting of Cyber Attack Intensity. Proceedings of the 12th Annual Conference on Cyber and Information Security Research, Oak Ridge, TN, USA.","DOI":"10.1145\/3064814.3064831"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2856","DOI":"10.1109\/TIFS.2018.2834227","article-title":"Modeling and Predicting Cyber Hacking Breaches","volume":"13","author":"Xu","year":"2017","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_13","unstructured":"Maness, R.C., Valeriano, B., Jensen, B., Hedgecock, K., and Macias, J. (2024, March 16). The Dyadic Cyber Incident and Campaign Data (DCID). Available online: https:\/\/drryanmaness.wixsite.com\/cyberconflict\/cyber-conflict-dataset."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1109\/MTS.2011.940293","article-title":"Dimensions of Cyber-Attacks: Cultural, Social, Economic, and Political","volume":"30","author":"Gandhi","year":"2011","journal-title":"IEEE Technol. Soc. Mag."},{"key":"ref_15","first-page":"1","article-title":"Representation Learning for Dynamic Graphs: A Survey","volume":"21","author":"Kazemi","year":"2020","journal-title":"J. Mach. Learn. Res."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3483595","article-title":"A Survey on Embedding Dynamic Graphs","volume":"55","author":"Barros","year":"2021","journal-title":"ACM Comput. Surv."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Yu, B., Yin, H., and Zhu, Z. (2018, January 13\u201319). Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. Proceedings of the 27th International Joint Conference on Artificial Intelligence, Stockholm, Sweden.","DOI":"10.24963\/ijcai.2018\/505"},{"key":"ref_18","first-page":"5363","article-title":"EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs","volume":"34","author":"Pareja","year":"2019","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"ref_19","unstructured":"Behrouz, A., and Seltzer, M. (2022). Anomaly Detection in Multiplex Dynamic Networks: From Blockchain Security to Brain Disease Prediction. arXiv."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Li, L., Duan, L., Wang, J., Xie, G., He, C., Chen, Z., and Deng, S. (2022, January 11\u201313). Transformer-Based Representation Learning on Temporal Heterogeneous Graphs. Proceedings of the Asia-Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint International Conference on Web and Big Data, Nanjing, China.","DOI":"10.1007\/978-3-031-25198-6_29"},{"key":"ref_21","first-page":"111","article-title":"Factors That Motivate State-Sponsored Cyberattacks","volume":"6","author":"Hunter","year":"2021","journal-title":"Cyber Def. Rev."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Kumar, S., and Carley, K.M. (2016, January 28\u201330). Approaches to Understanding the Motivations behind Cyber Attacks. Proceedings of the 2016 IEEE Conference on Intelligence and Security Informatics (ISI), Tucson, AZ, USA.","DOI":"10.1109\/ISI.2016.7745496"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1083","DOI":"10.1177\/0022343320964549","article-title":"International Trade and Cyber Conflict: Decomposing the Effect of Trade on State-Sponsored Cyber Attacks","volume":"58","author":"Akoto","year":"2021","journal-title":"J. Peace Res."},{"key":"ref_24","first-page":"5784674","article-title":"Identifying Key Relationships between Nation-State Cyberattacks and Geopolitical and Economic Factors: A Model","volume":"2022","author":"Ramos","year":"2022","journal-title":"Secur. Commun. Netw."},{"key":"ref_25","unstructured":"Deng, S., and Ning, Y. (2021). A Survey on Societal Event Forecasting with Deep Learning. arXiv."},{"key":"ref_26","first-page":"5714","article-title":"An Improved Deep Belief Neural Network Based Civil Unrest Event Forecasting in Twitter","volume":"53","author":"Iyda","year":"2023","journal-title":"Appl. Intell."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"800","DOI":"10.1080\/03050629.2022.2036987","article-title":"Conflict Forecasting with Event Data and Spatio-Temporal Graph Convolutional Networks","volume":"48","author":"Brandt","year":"2022","journal-title":"Int. Interact."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1285","DOI":"10.1038\/s41562-020-00994-6","article-title":"Assessing the Risks of \u2018Infodemics\u2019 in Response to COVID-19 Epidemics","volume":"4","author":"Gallotti","year":"2020","journal-title":"Nat. Hum. Behav."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Wang, Z., and Zhang, Y. (2017, January 19\u201325). DDoS Event Forecasting Using Twitter Data. Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, Melbourne, Australia.","DOI":"10.24963\/ijcai.2017\/580"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Deb, A., Lerman, K., and Ferrara, E. (2018). Predicting Cyber-Events by Leveraging Hacker Sentiment. Information, 9.","DOI":"10.3390\/info9110280"},{"key":"ref_31","unstructured":"Pechi, D. (2024, June 24). Predicting Cyber-Attacks Using Neural Language Models of Sociopolitical Events. Available online: https:\/\/danpechi.github.io\/Dan%20Pechi%20Thesis.pdf."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Lakha, B., Duran, J., Serra, E., and Spezzano, F. (2023, January 9\u201313). Prediction of Future Nation-initiated Cyberattacks from News-based Political Event Graph. Proceedings of the 2023 IEEE 10th International Conference on Data Science and Advanced Analytics (DSAA), Thessaloniki, Greece.","DOI":"10.1109\/DSAA60987.2023.10302510"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1109\/TNNLS.2020.2978386","article-title":"A Comprehensive Survey on Graph Neural Networks","volume":"32","author":"Wu","year":"2021","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_34","first-page":"1","article-title":"Graph Neural Networks in Recommender Systems: A Survey","volume":"55","author":"Wu","year":"2022","journal-title":"ACM Comput. Surv."},{"key":"ref_35","unstructured":"Kipf, T.N., and Welling, M. (2017). Semi-Supervised Classification with Graph Convolutional Networks. arXiv."},{"key":"ref_36","unstructured":"Hamilton, W.L., Ying, R., and Leskovec, J. (2017, January 4\u20139). Inductive Representation Learning on Large Graphs. Proceedings of the 31st International Conference on Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_37","unstructured":"Veli\u010dkovi\u0107, P., Cucurull, G., Casanova, A., Romero, A., Li\u00f2, P., and Bengio, Y. (May, January 30). Graph Attention Networks. Proceedings of the International Conference on Learning Representations, Vancouver, BC, Canada."},{"key":"ref_38","unstructured":"Xu, D., Ruan, C., Korpeoglu, E., Kumar, S., and Achan, K. (2020). Inductive Representation Learning on Temporal Graphs. arXiv."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Sankar, A., Wu, Y., Gou, L., Zhang, W., and Yang, H. (2020, January 3\u20137). Dysat: Deep neural representation learning on dynamic graphs via self-attention networks. Proceedings of the 13th International Conference on Web Search and Data Mining, Houston, TX, USA.","DOI":"10.1145\/3336191.3371845"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3441450","article-title":"Multi-View Collaborative Network Embedding","volume":"15","author":"Ata","year":"2021","journal-title":"ACM Trans. Knowl. Discov. Data"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1007\/s10462-024-10990-1","article-title":"Graph Neural Networks for Multi-View Learning: A Taxonomic Review","volume":"57","author":"Xiao","year":"2024","journal-title":"Artif. Intell. Rev."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Xue, H., Yang, L., Jiang, W., Wei, Y., Hu, Y., and Lin, Y. (2020). Modeling Dynamic Heterogeneous Network for Link Prediction Using Hierarchical Attention with Temporal RNN. Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2020. Lecture Notes in Computer Science, Springer.","DOI":"10.1007\/978-3-030-67658-2_17"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Paw\u0142owski, M., Wr\u00f3blewska, A., and Sysko-Roma\u0144czuk, S. (2023). Effective Techniques for Multimodal Data Fusion: A Comparative Analysis. Sensors, 23.","DOI":"10.3390\/s23052381"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3674501","article-title":"Deep Multimodal Data Fusion","volume":"56","author":"Zhao","year":"2024","journal-title":"ACM Comput. Surv."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1449","DOI":"10.1109\/JPROC.2015.2460697","article-title":"Multimodal Data Fusion: An Overview of Methods, Challenges, and Prospects","volume":"103","author":"Lahat","year":"2015","journal-title":"Proc. IEEE"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"e1415","DOI":"10.1002\/widm.1415","article-title":"Multimodal Sentimental Analysis for Social Media Applications: A Comprehensive Review","volume":"11","author":"Chandrasekaran","year":"2021","journal-title":"Wiley Interdiscip. Rev. Data Min. Knowl. Discov."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"424","DOI":"10.1016\/j.inffus.2022.09.025","article-title":"Multimodal Sentiment Analysis: A Systematic Review of History, Datasets, Multimodal Fusion Methods, Applications, Challenges and Future Directions","volume":"91","author":"Gandhi","year":"2023","journal-title":"Inf. Fusion"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1027","DOI":"10.1007\/s11280-020-00850-7","article-title":"Deep Fusion of Multimodal Features for Social Media Retweet Time Prediction","volume":"24","author":"Yin","year":"2021","journal-title":"World Wide Web"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"112011","DOI":"10.1016\/j.asoc.2024.112011","article-title":"Sentiment Analysis of Social Media Comments Based on Multimodal Attention Fusion Network","volume":"164","author":"Liu","year":"2024","journal-title":"Appl. Soft Comput."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"101490","DOI":"10.1016\/j.csl.2023.101490","article-title":"Social Media Popularity Prediction with Multimodal Hierarchical Fusion Model","volume":"80","author":"Wang","year":"2023","journal-title":"Comput. Speech Lang."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s00778-024-00878-5","article-title":"A Survey of Multimodal Event Detection Based on Data Fusion","volume":"34","author":"Mondal","year":"2025","journal-title":"VLDB J."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Zou, Z., Gan, H., Huang, Q., Cai, T., and Cao, K. (2021). Disaster Image Classification by Fusing Multimodal Social Media Data. ISPRS Int. J. Geo-Inf., 10.","DOI":"10.3390\/ijgi10100636"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1007\/s42979-021-00971-4","article-title":"Multi-Source Multimodal Data and Deep Learning for Disaster Response: A Systematic Review","volume":"3","author":"Algiriyage","year":"2022","journal-title":"SN Comput. Sci."},{"key":"ref_54","first-page":"102926","article-title":"Deep Learning in Multimodal Remote Sensing Data Fusion: A Comprehensive Review","volume":"112","author":"Li","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1514","DOI":"10.1111\/risa.14241","article-title":"Spatiotemporal Multi-graph Convolutional Network-based Provincial-day-level Terrorism Risk Prediction","volume":"44","author":"Luo","year":"2024","journal-title":"Risk Anal."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"e18895","DOI":"10.1016\/j.heliyon.2023.e18895","article-title":"An Integrated Deep-Learning and Multi-Level Framework for Understanding the Behavior of Terrorist Groups","volume":"9","author":"Jiang","year":"2023","journal-title":"Heliyon"},{"key":"ref_57","unstructured":"Arbor, N. (2024, June 24). Digital Attack Map: Top Daily DDoS Attacks Worldwid. Available online: https:\/\/www.digitalattackmap.com\/."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Nejjari, N., Lahlou, S., Fadi, O., Zkik, K., Oudani, M., and Benbrahim, H. (2021, January 6\u20139). Conflict Spectrum: An Empirical Study of Geopolitical Cyber Threats from a Social Network Perspective. Proceedings of the 2021 Eighth International Conference on Social Network Analysis, Management and Security (SNAMS), Gandia, Spain.","DOI":"10.1109\/SNAMS53716.2021.9732155"},{"key":"ref_59","unstructured":"Boschee, E., Lautenschlager, J., O\u2019Brien, S., Shellman, S., Starz, J., and Ward, M. (2024, June 24). ICEWS Coded Event Data. Available online: https:\/\/dataverse.harvard.edu\/dataset.xhtml?persistentId=doi:10.7910\/DVN\/28075."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1177\/0022002717737138","article-title":"Invisible Digital Front: Can Cyber Attacks Shape Battlefield Events?","volume":"63","author":"Kostyuk","year":"2019","journal-title":"J. Confl. Resolut."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Willett, M. (2022). The Cyber Dimension of the Russia\u2013Ukraine War. Survival: October\u2013November 2022, Routledge.","DOI":"10.1080\/00396338.2022.2126193"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"673","DOI":"10.1177\/00223433241262912","article-title":"Organized violence 1989\u20132023, and the prevalence of organized crime groups","volume":"61","author":"Davies","year":"2024","journal-title":"J. Peace Res."},{"key":"ref_63","unstructured":"Brett, N. (March, January 28). Economic Information Warfare: Classifying Cyber-attacks against Commodity Value Chains. Proceedings of the ICCWS 2019 14th International Conference on Cyber Warfare and Security: ICCWS 2019, Stellenbosch, South Africa."},{"key":"ref_64","unstructured":"Conte, M., Cotterlaz, P., and Mayer, T. (2024, May 13). The CEPII Gravity Database. Available online: https:\/\/ideas.repec.org\/p\/cii\/cepidt\/2022-05.html."},{"key":"ref_65","unstructured":"Valev, N. (2024, May 11). TheGlobalEconomy.com: Learning Resources and Data on the World Economy. Available online: https:\/\/www.theglobaleconomy.com\/."},{"key":"ref_66","unstructured":"Mezzour, G., Carley, L.R., and Carley, K.M. (2024, July 04). Global Mapping of Cyber Attacks. Available online: https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=2729302."},{"key":"ref_67","unstructured":"Levesque, F.L., Fernandez, J.M., and Somayaji, A. (2016, January 13\u201314). National-Level Risk Assessment: A Multi-Country Study of Malware Infections. Proceedings of the Workshop on the Economics of Information Security (WEIS), Berkeley, CA, USA."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Shi, Y., Huang, Z., Feng, S., Zhong, H., Wang, W., and Sun, Y. (2021). Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classification. arXiv.","DOI":"10.24963\/ijcai.2021\/214"},{"key":"ref_69","unstructured":"Dwivedi, V.P., and Bresson, X. (2021). A Generalization of Transformer Networks to Graphs. arXiv."},{"key":"ref_70","unstructured":"Li, M., Ye, Z., Zhao, H., Xiao, Y., and Cao, S. Detecting Social Robots Based on Multi-view Graph Transformer. Proceedings of the CCF Conference on Big Data. Communications in Computer and Information Science."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Wei, X., Zhang, T., Li, Y., Zhang, Y., and Wu, F. (2020, January 13\u201319). Multi-Modality Cross Attention Network for Image and Sentence Matching. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01095"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"109218","DOI":"10.1016\/j.engappai.2024.109218","article-title":"A new chiller fault diagnosis method under the imbalanced data environment via combining an improved generative adversarial network with an enhanced deep extreme learning machine","volume":"137","author":"Yang","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Lou, Y., Caruana, R., and Gehrke, J. (2012, January 12\u201316). Intelligible Models for Classification and Regression. Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Beijing China.","DOI":"10.1145\/2339530.2339556"},{"key":"ref_74","unstructured":"Nori, H., Jenkins, S., Koch, P., and Caruana, R. (2019). InterpretML: A Unified Framework for Machine Learning Interpretability. arXiv."},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Alahmadi, R., Almujibah, H., Alotaibi, S., Alsharif, M., and Bakri, M. (2023). Explainable Boosting Machine: A Contemporary Glass-Box Model to Analyze Work Zone-Related Road Traffic Crashes. Safety, 9.","DOI":"10.3390\/safety9040083"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1080\/00220670209598786","article-title":"An Introduction to Logistic Regression Analysis and Reporting","volume":"96","author":"Peng","year":"2002","journal-title":"J. Educ. Res."},{"key":"ref_77","unstructured":"Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., and Liu, T.Y. (2017). LightGBM: A Highly Efficient Gradient Boosting Decision Tree. Proceedings of the Advances in Neural Information Processing Systems, Curran Associates, Inc."},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Grover, A., and Leskovec, J. (2016, January 13\u201317). node2vec: Scalable feature learning for networks. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939754"},{"key":"ref_79","unstructured":"Ribeiro, L.F., Saverese, P.H., and Figueiredo, D.R. (2017, January 13\u201317). struc2vec: Learning node representations from structural identity. Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, NS, Canada."},{"key":"ref_80","first-page":"14501","article-title":"Recipe for a General, Powerful, Scalable Graph Transformer","volume":"35","author":"Galkin","year":"2022","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_81","unstructured":"Tailor, S.A., Opolka, F., Lio, P., and Lane, N.D. (2022, January 25\u201329). Adaptive Filters for Low-Latency and Memory-Efficient Graph Neural Networks. Proceedings of the International Conference on Learning Representations, Virtual."},{"key":"ref_82","unstructured":"Brody, S., Alon, U., and Yahav, E. (2022, January 25\u201329). How Attentive are Graph Attention Networks?. Proceedings of the International Conference on Learning Representations, Virtual."},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Yu, L., Sun, L., Du, B., Liu, C., Xiong, H., and Lv, W. (2020, January 6\u201310). Predicting Temporal Sets with Deep Neural Networks. Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, New York, NY, USA.","DOI":"10.1145\/3394486.3403152"},{"key":"ref_84","doi-asserted-by":"crossref","unstructured":"Bai, J., Zhu, J., Song, Y., Zhao, L., Hou, Z., Du, R., and Li, H. (2021). A3T-GCN: Attention Temporal Graph Convolutional Network for Traffic Forecasting. ISPRS Int. J. Geo-Inf., 10.","DOI":"10.3390\/ijgi10070485"},{"key":"ref_85","first-page":"4838","article-title":"Transfer Graph Neural Networks for Pandemic Forecasting","volume":"35","author":"Panagopoulos","year":"2021","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2023\/8342104","article-title":"Deep Learning with Graph Convolutional Networks: An Overview and Latest Applications in Computational Intelligence","volume":"2023","author":"Bhatti","year":"2023","journal-title":"Int. J. Intell. Syst."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"106207","DOI":"10.1016\/j.neunet.2024.106207","article-title":"A Comprehensive Survey on Deep Graph Representation Learning","volume":"173","author":"Ju","year":"2024","journal-title":"Neural Netw."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"775","DOI":"10.1038\/d41586-022-00753-9","article-title":"Where Is Russia\u2019s Cyberwar? Researchers Decipher Its Strategy","volume":"603","author":"Gibney","year":"2022","journal-title":"Nature"},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.socnet.2022.08.003","article-title":"Assessing Harmfulness and Vulnerability in Global Bipartite Networks of Terrorist-Target Relationships","volume":"72","author":"Spelta","year":"2023","journal-title":"Soc. Netw."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1177\/1043986217699100","article-title":"Exploring the Subculture of Ideologically Motivated Cyber-Attackers","volume":"33","author":"Holt","year":"2017","journal-title":"J. Contemp. Crim. Justice"},{"key":"ref_91","first-page":"101","article-title":"Cyber Security and International Conflicts: An Analysis of State-Sponsored Cyber Attacks","volume":"8","author":"Azubuike","year":"2023","journal-title":"Nnamdi Azikiwe J. Political Sci."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"414","DOI":"10.1080\/01495933.2018.1526568","article-title":"Arms Race \u201cin Cyberspace\u201d\u2014A Case Study of Iran and Israel","volume":"37","year":"2018","journal-title":"Comp. Strategy"},{"key":"ref_93","first-page":"599","article-title":"Cyber Security As A New Strategic Issue in the Middle East: A Case Study of Persian Gulf and North African Countries","volume":"14","author":"Dehnavi","year":"2023","journal-title":"Riv. Ital. Di Filos. Anal. Jr."},{"key":"ref_94","unstructured":"Sumari, A.D.W., Gunawan, D., and Munthaha, F. (2014, January 24\u201325). Cyberspace operations as multiplier power in asymmetric conflict. Proceedings of the International Conference on Cyber Warfare and Security. Academic Conferences International Limited, West Lafayette, IN, USA."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"71","DOI":"10.5038\/1944-0472.17.4.2268","article-title":"Asymmetry in the Digital Age: Cyber Deterrence Strategies for Small States","volume":"17","author":"Li","year":"2024","journal-title":"J. Strateg. Secur."},{"key":"ref_96","unstructured":"Kausch, K. (2024, July 04). Cheap Havoc: How Cyber-Geopolitics Will Destabilize the Middle East. Available online: https:\/\/www.jstor.org\/stable\/resrep18776."},{"key":"ref_97","unstructured":"Bendiek, A., and Metzger, T. (2015). Deterrence theory in the cyber-century. INFORMATIK 2015, Gesellschaft f\u00fcr Informatik eV."},{"key":"ref_98","first-page":"90","article-title":"The Strategic Promise of Offensive Cyber Operations","volume":"12","author":"Smeets","year":"2018","journal-title":"Strateg. Stud. Q."},{"key":"ref_99","doi-asserted-by":"crossref","unstructured":"Huskaj, G., Blix, F., and Axelsson, S. (2024, January 27\u201328). A Theory of Offensive Cyberspace Operations and Its Policy and Strategy Implications. Proceedings of the 23rd European Conference on Cyber Warfare and Security, Jyvaskyla, Finland.","DOI":"10.34190\/eccws.23.1.2391"},{"key":"ref_100","first-page":"4556","article-title":"Huawei Versus the United States? The Geopolitics of Exterritorial Internet Infrastructure","volume":"14","author":"Tang","year":"2020","journal-title":"Int. J. Commun."},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1080\/01296612.2023.2246721","article-title":"\u201cThe Russia-Ukraine War\u201d or \u201cThe US-Russia War\u201d? Thematic Analysis of Global Times\u2019 Coverage of the Russia-Ukraine War","volume":"51","author":"Ran","year":"2024","journal-title":"Media Asia"},{"key":"ref_102","unstructured":"XinHuaNet (2024, July 04). The United States\u2019 Global Confrontation and Conflict: 1979\u20132020. Available online: http:\/\/www.xinhuanet.com\/2021-12\/13\/c_1128158270.htm."},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"77","DOI":"10.35467\/sdq\/131787","article-title":"Game of Proxies\u2014Towards a New Model of Warfare: Experiences from the CAR, Libya, Mali, Syria, and Ukraine","volume":"31","author":"Kozera","year":"2020","journal-title":"Secur. Def. Q."},{"key":"ref_104","unstructured":"Gold, J. (2024, July 04). The Five Eyes and Offensive Cyber Capabilities: Building a \u2018Cyber Deterrence Initiative\u2019. Available online: https:\/\/ccdcoe.org\/library\/publications\/the-five-eyes-and-offensive-cyber-capabilities-building-a-cyber-deterrence-initiative\/."},{"key":"ref_105","unstructured":"BBC (2024, July 04). US \u2018Launched Cyber-Attack on Iran Weapons Systems\u2019. Available online: https:\/\/www.bbc.com\/news\/world-us-canada-48735097."},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"162","DOI":"10.53477\/2668-5094-21-11","article-title":"Old Methods in the New Framework: Strategy of Grey Zones in Hybrid Warfare","volume":"1","author":"Sykulski","year":"2021","journal-title":"Strateg. XXI Natl. Def. Coll."},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"281","DOI":"10.12797\/Politeja.21.2024.92.13","article-title":"Rethinking Future Conflicts","volume":"21","author":"Dziwisz","year":"2024","journal-title":"Politeja"},{"key":"ref_108","first-page":"36","article-title":"In search of a European Russia strategy","volume":"44","year":"2020","journal-title":"Atl. Perspect."},{"key":"ref_109","doi-asserted-by":"crossref","unstructured":"Jensen, B., Valeriano, B., and Maness, R. (2020). Fancy Bears and Digital Trolls: Cyber Strategy with a Russian Twist. Military Strategy in the 21st Century, Routledge.","DOI":"10.4324\/9781003009207-4"},{"key":"ref_110","first-page":"26","article-title":"Geopolitics of the Russia-Ukraine War and Russian cyber attacks on Ukraine-Georgia and expected threats","volume":"10","author":"Guchua","year":"2022","journal-title":"Ukr. Policymaker"},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"499","DOI":"10.1016\/j.icte.2021.12.003","article-title":"Cyberattack detection model using community detection and text analysis on social media","volume":"8","author":"Park","year":"2022","journal-title":"ICT Express"},{"key":"ref_112","doi-asserted-by":"crossref","unstructured":"Abusaqer, M., Benaoumeur Senouci, M., and Magel, K. (2023, January 20\u201323). Twitter User Sentiments Analysis: Health System Cyberattacks Case Study. Proceedings of the 2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), Bali, Indonesia.","DOI":"10.1109\/ICAIIC57133.2023.10067026"},{"key":"ref_113","unstructured":"Serrano, S., and Smith, N.A. (August, January 28). Is Attention Interpretable?. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, Florence, Italy."},{"key":"ref_114","unstructured":"Jain, S., and Wallace, B.C. (2019, January 2\u20137). Attention is not Explanation. 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), Minneapolis, MN, USA."},{"key":"ref_115","unstructured":"Zhao, W.X., Zhou, K., Li, J., Tang, T., Wang, X., Hou, Y., Min, Y., Zhang, B., Zhang, J., and Dong, Z. (2023). A survey of large language models. arXiv."},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1145\/3655103.3655110","article-title":"Exploring the Potential of Large Language Models (LLMs)in Learning on Graphs","volume":"25","author":"Chen","year":"2024","journal-title":"SIGKDD Explor. Newsl."},{"key":"ref_117","doi-asserted-by":"crossref","first-page":"8622","DOI":"10.1109\/TKDE.2024.3469578","article-title":"Large Language Models on Graphs: A Comprehensive Survey","volume":"36","author":"Jin","year":"2024","journal-title":"IEEE Trans. Knowl. Data Eng."}],"container-title":["Big Data and Cognitive Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-2289\/9\/3\/63\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T16:48:56Z","timestamp":1760028536000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-2289\/9\/3\/63"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,7]]},"references-count":117,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2025,3]]}},"alternative-id":["bdcc9030063"],"URL":"https:\/\/doi.org\/10.3390\/bdcc9030063","relation":{},"ISSN":["2504-2289"],"issn-type":[{"value":"2504-2289","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,7]]}}}