Integrating prompt-based zero-shot learning into Bayesian network for maritime cybersecurity risk analysis

Zhao, Y, Li, H ORCID: 0000-0002-4293-4763, Nguyen, TT ORCID: 0000-0002-3268-1790, Matthews, C ORCID: 0000-0002-4126-6484 and Yang, Z ORCID: 0000-0003-1385-493X (2026) Integrating prompt-based zero-shot learning into Bayesian network for maritime cybersecurity risk analysis. Transportation Research Part E Logistics and Transportation Review, 216. ISSN 1366-5545

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Abstract

The increasing digitalisation of maritime ecosystems, including vessels, ports, offshore facilities, and shore-based organisations, has expanded the attack surface of maritime operations and increased exposure to cyber threats. Cyber incidents affecting these interconnected systems may propagate across global logistics networks, highlighting the need for systematic cybersecurity risk analysis in maritime transport. However, existing studies often rely on expert judgement or limited datasets, which restrict scalability and hinder comprehensive identification of Risk Influential Factors (RIFs). This study develops a data-driven framework that integrates Large Language Model (LLM)-assisted text classification with Bayesian Network (BN) modelling to analyse maritime cybersecurity risks. A structured taxonomy of RIFs is defined to capture temporal, regional, operational, and technical attributes of cyber incidents. LLM-based prompt engineering is then employed to automatically extract these factors from unstructured incident reports. The reliability of the automated classification process is evaluated through manual annotation on a sampled subset of incident reports and comparative experiments across multiple LLMs. The BN model is further assessed using repeated cross-validation to evaluate predictive performance and robustness. The results reveal key dependencies among cyber incident characteristics and consequence types, providing probabilistic insights into maritime cyber risk patterns. The proposed framework offers a scalable approach for analysing cyber risks in maritime transport systems and supports risk-informed cybersecurity management.

Item Type:	Article
Uncontrolled Keywords:	Maritime cybersecurity; Large language model; Zero-shot learning; Prompt engineering; Bayesian network; 3509 Transportation, Logistics and Supply Chains; 35 Commerce, Management, Tourism and Services; Networking and Information Technology R&D (NITRD); 0102 Applied Mathematics; 0103 Numerical and Computational Mathematics; 1507 Transportation and Freight Services; Logistics & Transportation; 3509 Transportation, logistics and supply chains
Subjects:	H Social Sciences > HE Transportation and Communications Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > TA Engineering (General). Civil engineering (General) V Naval Science > VM Naval architecture. Shipbuilding. Marine engineering
Divisions:	Engineering and Built Environment
Publisher:	Elsevier
Date of acceptance:	28 July 2026
Date of first compliant Open Access:	21 September 2026
Date Deposited:	21 Sep 2026 12:22
Last Modified:	21 Sep 2026 12:22
DOI or ID number:	10.1016/j.tre.2026.105134
URI:	https://researchonline.ljmu.ac.uk/id/eprint/29479

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