AI-driven cybersecurity in critical infrastructure: A bibliometric analysis (2015–2025)
DOI:
https://doi.org/10.55630/mem.2026.55.126-133Keywords:
Artificial intelligence, critical infrastructure, OT/ICS/SCADA, cybersecurity, anomaly detection, federated learning, false data injection, bibliometric analysisAbstract
This paper presents a bibliometric analysis of 1,862 research documents (2015–2025) focused on the application of Artificial Intelligence (AI) for Critical Infrastructure (CI) cybersecurity. Data from Scopus and Web of Science were processed using the Bibliometrix R-package. The study quantifies publication dynamics and maps thematic and sector-specific patterns in AI methods applied to OT/ICS/SCADA cybersecurity after 2020. Sector-specific analysis reveals distinct technological profiles: the Energy sector remains anchored in SCADA security and False Data Injection detection; the Transport sector shows a shift toward Federated Learning for connected mobility; and the Water sector highlights a niche but growing focus on Explainable AI (XAI) and IoT. These findings map the transition from general infrastructure protection to domain-specific AI countermeasures.
References
Aminu, M., Akinsanya, A., Oyedokun, O., and Akinwande, O. T. (2024). A review of advanced cyber threat detection techniques in critical infrastructure: Evolution, current state, and future directions. International Journal of Computer Applications Technology and Research, 13(8), 74–87. https://www.irejournals. com/paper-details/1706103.
Aria, M., and Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975. https://doi.org/10.1016/j.joi.2017.08.007.
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., et al. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, n71. https://doi.org/10.1136/bmj.n71.
Zolanvari, M., Teixeira, M. A., Gupta, L., Khan, K. M., and Jain, R. (2019). Machine learning-based network vulnerability analysis of industrial internet of things. IEEE Internet of Things Journal, 6(4), 6822–6834. https://doi.org/10.1109/JIOT.2019.2912020.
