This study aims to develop a novel and fully data-driven approach to analyse the maritime accidents risk influential factors (RIFs) by integrating Association Rule Mining (ARM) and Complex Network (CN) modelling. Firstly, a comprehensive dataset comprising 21,206 maritime accident records from Marine Accident Investigation Branch and Transportation Safety Board is collected and processed to serve as the foundational data source supporting the development of the new approach. Secondly, a novel Combined Association Rule Mining method is proposed to extract the interconnections among RIFs, with the mined results mapped into a CN framework. Finally, two importance ranking algorithms, namely the PageRank-Information-Entropy algorithm and edge betweenness centrality, are applied to identify the key RIFs and their information transmission paths. By simulating deliberate and random attacks within networks, a robustness analysis is conducted to further explore the evolution of RIFs. The findings reveal that ship-related factors demonstrate greater centrality and connectivity, exerting a more substantial influence on information propagation within the network structure. The robustness analysis illustrates that strategic node and edge removals are effective in preventing risk propagation. It therefore makes contributions to the development of a theoretical basis for stakeholders to develop cost-effective preventive measures against specific RIFs, ultimately enhancing maritime safety.

The global threat of deliberate attacks on chemical, process, and energy facilities underscores the urgent need to enhance Security Vulnerability/Risk Assessment (SVA/SRA) approaches. Traditional assessments often use historical data and Exploratory Data Analysis (EDA) to identify reference scenarios. However, EDA lacks a standardized approach to identify and rank the incident chains. A novel methodology based on Bayesian Networks (BN), named BAS2E, was developed to support the systematic identification of reference scenarios from past event datasets. The methodology is based on the development of a static quantified BN, that accurately reflects the causal relationships in incident chains, focusing specifically on those between threats, attack methods, and physical damage scenarios. The BN is quantified by statistical information from the analysis of the incident records and employs the Noisy-OR gate model to manage data gaps in the conditional probability tables (CPTs) specification. The application of the BN sensitivity analysis provides quantification of the reciprocal influence between nodes using a specific derivative-based parameter, allowing for the systematic ranking of the most impactful incident chains to be included as reference scenarios in SVA/SRA. The methodology is demonstrated through its application to a dataset of 109 security incidents that occurred in the offshore Oil&Gas sector.