A Bibliometric Analysis of Maritime Cybersecurity: Trends, Themes, and Collaboration Patterns

Abstract

The rapid development and increasing strategic importance of the maritime cyber security field, particularly over the last decade, form the starting point of this study. The aim of this study is to present the current status of the field with objective data. For this purpose, 607 records indexed under the keywords "Cyber Security" and "Maritime" in the Web of Science Core Collection were examined. Data were collected in the "Full Record and Cited References" format, and descriptive statistics and VOSviewer-based network analysis were applied together. The findings indicate a significant increase in publications after 2016; the acceleration of citation accumulation between 2019 and 2023 signals the field's transition to a mature phase. While the collaboration structure is concentrated around China and the US, it is supported by a strong cluster in Europe. Turkey's visibility is increasing, but there is room for development in terms of network depth. Four themes emerge in the keyword co-occurrence network: navigation and ship systems security (AIS/ECDIS, GNSS spoofing), maritime IoT and communication networks (ICS/SCADA, satcom), machine and deep learning-based anomaly detection, and risk, resilience, and governance. The study's contribution is its systematic mapping of the field within an interdisciplinary framework, jointly assessing country, institutional, and conceptual networks within the context of funding, publishing ecosystems, and citation dynamics. This assessment concretizes research priorities and emphasizes zero-trust principles with multilayered defenses in practice. The results point to a research agenda based on international data and testbed collaborations, providing a practical roadmap for policy and practice.

Keywords

• Maritime
• Cybersecurity
• Bibliometric Analysis
• Academic Landscape
• Multidisciplinary Research
• Research Collaboration Networks

Denizcilik Siber Güvenliği Üzerine Bibliyometrik Bir Analiz: Eğilimler, Temalar ve İşbirliği Örüntüleri

Abstract

Deniz siber güvenlik alanının, özellikle son on yılda hızla gelişmesi ve stratejik öneminin artması, bu çalışmanın başlangıç noktasını oluşturmaktadır. Bu çalışmanın amacı, alanın mevcut durumunu objektif verilerle ortaya koymaktır. Bu amaçla, Web of Science Core Collection'da "Siber Güvenlik" ve "Denizcilik" anahtar kelimeleri altında indekslenen 607 kayıt incelenmiştir. Veriler "Tam Kayıt ve Atıf Yapılan Referanslar" formatında toplanmış ve betimsel istatistikler ile VOSviewer tabanlı ağ analizi birlikte uygulanmıştır. Bulgular, 2016'dan sonra yayınlarda önemli bir artış olduğunu göstermektedir; 2019-2023 yılları arasında atıf birikiminin hızlanması, alanın olgun bir aşamaya geçişini işaret etmektedir. İş birliği yapısı Çin ve ABD etrafında yoğunlaşırken, Avrupa'daki güçlü bir kümelenme tarafından desteklenmektedir. Türkiye'nin görünürlüğü artmakta, ancak ağ derinliği açısından gelişmeye açık alanlar bulunmaktadır. Anahtar kelime eş-oluşum ağı'nda dört tema ortaya çıkmaktadır: navigasyon ve gemi sistemleri güvenliği (AIS/ECDIS, GNSS sahteciliği), denizcilik IoT ve iletişim ağları (ICS/SCADA, satcom), makine ve derin öğrenme tabanlı anomali tespiti ve risk, dayanıklılık ve yönetişim. Çalışmanın katkısı, disiplinler arası bir çerçevede alanın sistematik bir haritasını çıkarması ve fonlama, yayın ekosistemleri ve atıf dinamikleri bağlamında ülke, kurumsal ve kavramsal ağları birlikte değerlendirmesidir. Bu değerlendirme, araştırma önceliklerini somutlaştırır ve uygulamada çok katmanlı savunmalarla sıfır güven ilkelerini vurgular. Sonuçlar, uluslararası verilere ve test ortamı iş birliklerine dayalı bir araştırma gündemine işaret ederek politika ve uygulama için pratik bir yol haritası sunmaktadır.

Keywords

• Denizcilik
• Siber Güvenlik
• Bibliyometrik Analiz
• Akademik Manzara
• Çok Disiplinli Araştırma
• Araştırma İş Birliği Ağları.

References

1. [1] Rivkin, B. S. (2023). Maritime cybersecurity: Navigational aspect. Gyroscopy and Navigation, 14(4), 386–400. https://doi.org/10.1134/S207510872470010X
2. [2] Perkovič, M., Gucma, L., & Feuerstack, S. (2024). Maritime security and risk assessments. Journal of Marine Science and Engineering, 12(6), 988. https://doi.org/10.3390/jmse12060988
3. [3] Oruc, A., Gkioulos, V., & Katsikas, S. (2022). Towards a cyber-physical range for the integrated navigation system (INS). Journal of Marine Science and Engineering, 10(1), 107. https://doi.org/10.3390/jmse10010107
4. [4] Avcı, İ., Koca, M., & Atasoy, M. (2021). Windows tabanlı uygulamalarda SQL enjeksiyon siber saldırı senaryosu ve güvenlik önlemleri. European Journal of Science and Technology, 28, 213–219. https://doi.org/10.31590/ejosat.995697
5. [5] L., J. (1981). A brief history of the use of marine radar. Journal of Navigation, 28(3), 199–205. https://doi.org/10.1002/j.2161-4296.1981.tb00767.x
6. [6] Katsikas, S. K., Kavallieratos, G., & Amro, A. (2024). Future trends in maritime cybersecurity. In Computer and Information Security Handbook (pp. 1663–1678). Elsevier. https://doi.org/10.1016/B978-0-443-13223-0.00104-1
7. [7] Koca, M., Avcı, İ., & Al-Hayani, M. A. S. (2023). Classification of malicious URLs using naive Bayes and genetic algorithm. Sakarya University Journal of Computer and Information Sciences, 6(2), 80–90. https://doi.org/10.35377/saucis...1273536
8. [8] Bolat, P., Yüksel, G., & Uçar, S. (2016, May). A study for understanding cyber security awareness among Turkish seafarers. In Proceedings of the Second Global Conference on Innovation in Marine Technology and the Future of Maritime Transportation.

9. [9] Butun, I., Osterberg, P., & Song, H. (2020). Security of the Internet of Things: Vulnerabilities, attacks, and countermeasures. IEEE Communications Surveys & Tutorials, 22(1), 616–644. https://doi.org/10.1109/COMST.2019.2953364
10. [10] Koca, M., & Çiftçi, S. (2025). A comprehensive bibliometric analysis of big data and cyber security: Intellectual structure, trends, and global collaborations. Knowledge and Information Systems. https://doi.org/10.1007/S10115-025-02531-1
11. [11] Zhang, J. Z., Srivastava, P. R., Sharma, D., & Eachempati, P. (2021). Big data analytics and machine learning: A retrospective overview and bibliometric analysis. Expert Systems with Applications, 184. https://doi.org/10.1016/j.eswa.2021.115561
12. [12] Dao, S. D., Abhary, K., & Marian, R. (2017). A bibliometric analysis of genetic algorithms throughout the history. Computers & Industrial Engineering, 110, 395–403. https://doi.org/10.1016/j.cie.2017.06.009
13. [13] Martínez-López, F. J., Merigó, J. M., Gázquez-Abad, J. C., & Ruiz-Real, J. L. (2020). Industrial marketing management: Bibliometric overview since its foundation. Industrial Marketing Management, 84, 19–38. https://doi.org/10.1016/j.indmarman.2019.07.014
14. [14] Soo, D. B., Doku, R., & Garuba, M. (2021). Cybersecurity in big data era: From securing big data to data-driven security. IEEE Transactions on Services Computing, 14(6), 2055–2072. https://doi.org/10.1109/TSC.2019.2907247
15. [15] Sood, S. K., Rawat, K. S., & Kumar, D. (2023). Scientometric analysis of ICT-assisted intelligent control systems response to COVID-19 pandemic. Neural Computing and Applications, 35(26), 18829–18849. https://doi.org/10.1007/S00521-023-08788-3
16. [16] Kumari, S., Li, X., Wu, F., Das, A. K., Choo, K. K. R., & Shen, J. (2017). Design of a provably secure biometrics-based multi-cloud-server authentication scheme. Future Generation Computer Systems, 68, 320–330. https://doi.org/10.1016/j.future.2016.10.004
17. [17] van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523–538. https://doi.org/10.1007/s11192-009-0146-3
18. [18] Centre for Science and Technology Studies, Leiden University. (2019). VOSviewer: Visualizing scientific landscapes. VOSviewer. https://www.vosviewer.com/
19. [19] Hou, X., Pal, D., Kumar, T. K. A., Thomas, J. P., & Liu, H. (2016, April). Privacy preserving rack-based dynamic workload balancing for Hadoop MapReduce. In Proceedings of IEEE BigDataSecurity (pp. 30–35). IEEE. https://doi.org/10.1109/BigDataSecurity-HPSC-IDS.2016.40
20. [20] Koca, M., Aydin, M. A., Sertbaş, A., & Zaim, A. H. (2021). A new distributed anomaly detection approach for log IDS management based on deep learning. Turkish Journal of Electrical Engineering and Computer Sciences, 29(5), 2486–2501. https://doi.org/10.3906/elk-2102-89
21. [21] Musleh, A. S., Yao, G., & Muyeen, S. M. (2019). Blockchain applications in smart grid: Review and frameworks. IEEE Access, 7, 86746–86757. https://doi.org/10.1109/ACCESS.2019.2920682
22. [22] Bhatti, J., & Humphreys, T. (2017). Hostile control of ships via false GPS signals: Demonstration and detection. Journal of Navigation, 64(1), 51–66. https://doi.org/10.1002/navi.183
23. [23] Li, H., Han, D., & Tang, M. (2020). A privacy-preserving charging scheme for electric vehicles using blockchain and fog computing. IEEE Systems Journal, 15(3), 3189–3200. https://doi.org/10.1109/JSYST.2020.3009447
24. [24] Gao, S., et al. (2023). A 3D model encryption scheme based on a cascaded chaotic system. Signal Processing, 202. https://doi.org/10.1016/j.sigpro.2022.108745
25. [25] Yang, Y., Xiao, Y., & Li, T. (2022). Attacks on formation control for multiagent systems. IEEE Transactions on Cybernetics, 52(12), 12805–12817. https://doi.org/10.1109/TCYB.2021.3089375
26. [26] Wang, M., Wang, X., Zhao, T., Zhang, C., Xia, Z., & Yao, N. (2021). Spatiotemporal chaos in improved cross coupled map lattice and its application in a bit-level image encryption scheme. Information Sciences, 544, 1–24. https://doi.org/10.1016/j.ins.2020.07.051
27. [27] Li, Q., et al. (2022). Concealed attack for robust watermarking based on generative model and perceptual loss. IEEE Transactions on Circuits and Systems for Video Technology, 32(8), 5695–5706. https://doi.org/10.1109/TCSVT.2021.3138795
28. [28] Inayat, U., Zia, M. F., Mahmood, S., Khalid, H. M., & Benbouzid, M. (2022). Learning-based methods for cyber attacks detection in IoT systems: Methods, analysis, and future prospects. Electronics, 11(9). https://doi.org/10.3390/electronics11091502
29. [29] Li, Q., Wang, X., Wang, X., Ma, B., Wang, C., & Shi, Y. (2021). An encrypted coverless information hiding method based on generative models. Information Sciences, 553, 19–30. https://doi.org/10.1016/j.ins.2020.12.002
30. [30] Bolbot, V., Theotokatos, G., Boulougouris, E., & Vassalos, D. (2020). A novel cyber-risk assessment method for ship systems. Safety Science, 131. https://doi.org/10.1016/j.ssci.2020.104908
31. [31] Tam, K., & Jones, K. (2019). MaCRA: A model-based framework for maritime cyber-risk assessment. WMU Journal of Maritime Affairs, 18(1), 129–163. https://doi.org/10.1007/s13437-019-00162-2
32. [32] Gao, S., et al. (2023). Asynchronous updating Boolean network encryption algorithm. IEEE Transactions on Circuits and Systems for Video Technology, 33(8), 4388–4400. https://doi.org/10.1109/TCSVT.2023.3237136
33. [33] Wolsing, K., Roepert, L., Bauer, J., & Wehrle, K. (2022). Anomaly detection in maritime AIS tracks: A review of recent approaches. Journal of Marine Science and Engineering, 10(1). https://doi.org/10.3390/jmse10010112
34. [34] Ashraf, I., et al. (2023). A survey on cyber security threats in IoT-enabled maritime industry. IEEE Transactions on Intelligent Transportation Systems, 24(2), 2677–2690. https://doi.org/10.1109/TITS.2022.3164678
35. [35] Ben Farah, M. A., et al. (2022). Cyber security in the maritime industry: A systematic survey of recent advances and future trends. Information, 13(1), 22. https://doi.org/10.3390/info13010022