A new AI solution to maritime cybersecurity risk prediction
A new AI solution to maritime cybersecurity risk prediction
The digitalisation of maritime systems, including ships, ports, and operational networks, has significantly increased their exposure to cyber threats and risks. These risks can disrupt critical infrastructure and cause global repercussions, requiring new solutions to improve maritime cybersecurity risk prediction. This study aims to develop a new AI solution with limited data to enable cybersecurity risk prediction. It utilises Large Language Models (LLMs) for prompt-based zero-shot learning, enabling accurate classification of text and extraction of key cyber risk factors. A comprehensive dataset spanning 2001 to 2020 was developed, introducing new risk factors critical for assessing cyber threats that are yet to appear in any state-of-the-art studies in the field. This extracted dataset was integrated into a Bayesian Network (BN) model to identify probabilistic relationships and predict potential cybersecurity risks. The hybrid approach is among the pioneers of using new AI technologies for text mining to enrich risk data and realising multiple source data fusion for improved risk prediction, hence making significant theoretical contributions to safety sciences. By leveraging the advanced capabilities of LLMs alongside probabilistic modelling, the study has shown its methodological novelty through a scalable, adaptive methodology that can enhance risk predictive accuracy and strengthen general and maritime systems against evolving cyber risks in specific. From an applied research perspective, it provides an in-depth analysis of maritime cybersecurity within the context of the fast growth of maritime digitalisation and brings significant managerial insights into practice. Such insights are invaluable for stakeholders, enabling them to identify vulnerabilities, anticipate threats, and prioritise resources effectively. This integrated framework equips policymakers with the tools needed for proactive decision-making, supporting the development of targeted cybersecurity strategies to minimise operational disruptions.
Zhao, Yunfeng
f1db7538-6b13-4d50-8b34-ad39038fb598
Li, Huanhuan
5e806b21-10a7-465c-9db3-32e466ae42f1
Yang, Zaili
e8ff5fad-c312-4643-8e8b-55d2697fed3a
15 June 2025
Zhao, Yunfeng
f1db7538-6b13-4d50-8b34-ad39038fb598
Li, Huanhuan
5e806b21-10a7-465c-9db3-32e466ae42f1
Yang, Zaili
e8ff5fad-c312-4643-8e8b-55d2697fed3a
Zhao, Yunfeng, Li, Huanhuan and Yang, Zaili
(2025)
A new AI solution to maritime cybersecurity risk prediction.
In Proceedings of the 35th European Safety and Reliability Conference (ESREL 2025) and the 33rd Society for Risk Analysis Europe Conference (SRA-E 2025).
8 pp
.
(doi:10.3850/978-981-94-3281-3_esrel-sra-e2025-p0401-cd).
Record type:
Conference or Workshop Item
(Paper)
Abstract
The digitalisation of maritime systems, including ships, ports, and operational networks, has significantly increased their exposure to cyber threats and risks. These risks can disrupt critical infrastructure and cause global repercussions, requiring new solutions to improve maritime cybersecurity risk prediction. This study aims to develop a new AI solution with limited data to enable cybersecurity risk prediction. It utilises Large Language Models (LLMs) for prompt-based zero-shot learning, enabling accurate classification of text and extraction of key cyber risk factors. A comprehensive dataset spanning 2001 to 2020 was developed, introducing new risk factors critical for assessing cyber threats that are yet to appear in any state-of-the-art studies in the field. This extracted dataset was integrated into a Bayesian Network (BN) model to identify probabilistic relationships and predict potential cybersecurity risks. The hybrid approach is among the pioneers of using new AI technologies for text mining to enrich risk data and realising multiple source data fusion for improved risk prediction, hence making significant theoretical contributions to safety sciences. By leveraging the advanced capabilities of LLMs alongside probabilistic modelling, the study has shown its methodological novelty through a scalable, adaptive methodology that can enhance risk predictive accuracy and strengthen general and maritime systems against evolving cyber risks in specific. From an applied research perspective, it provides an in-depth analysis of maritime cybersecurity within the context of the fast growth of maritime digitalisation and brings significant managerial insights into practice. Such insights are invaluable for stakeholders, enabling them to identify vulnerabilities, anticipate threats, and prioritise resources effectively. This integrated framework equips policymakers with the tools needed for proactive decision-making, supporting the development of targeted cybersecurity strategies to minimise operational disruptions.
Text
ESREL-SRA-E2025-P0401
- Accepted Manuscript
Available under License Other.
More information
Accepted/In Press date: 15 May 2025
Published date: 15 June 2025
Venue - Dates:
Society for Risk Analysis - Europe - Conference 2025, Stavanger University, Stavanger, Norway, 2025-06-15 - 2025-06-19
Identifiers
Local EPrints ID: 511418
URI: http://eprints.soton.ac.uk/id/eprint/511418
PURE UUID: dcbd8aec-2090-42ad-88dc-92b7e5ab19ef
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Date deposited: 14 May 2026 16:34
Last modified: 21 May 2026 02:13
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Author:
Yunfeng Zhao
Author:
Huanhuan Li
Author:
Zaili Yang
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