Abstract：
Maritime terrorist accidents have a significant low-frequency-high-consequence feature and, thus, require new research to address the associated inherent uncertainty and the scarce literature in the field. This article aims to develop a novel method for maritime security risk analysis. It employs real accident data from maritime terrorist attacks over the past two decades to train a data-driven Bayesian network (DDBN) model. The findings help pinpoint key contributing factors, scrutinize their interdependencies, ascertain the probability of different terrorist scenarios, and describe their impact on different manifestations of maritime terrorism. The established DDBN model undergoes a thorough verification and validation process employing various techniques, such as sensitivity, metrics, and comparative analyses. Additionally, it is tested against recent real-world cases to demonstrate its effectiveness in both retrospective and prospective risk propagation, encompassing both diagnostic and predictive capabilities. These findings provide valuable insights for the various stakeholders, including companies and government bodies, fostering comprehension of maritime terrorism and potentially fortifying preventive measures and emergency management.
摘要：
海上恐怖事故具有显著的低频率高后果特征,因此，需要新的研究来解决相关的固有不确定性和该领域稀缺的文献。本文旨在开发一种新的海上安全风险分析方法。它利用过去二十年来海上恐怖袭击的真实事故数据来训练数据驱动的贝叶斯网络（DDBN）模型。这些发现有助于查明关键的促成因素，仔细检查它们的相互依存关系，确定不同恐怖情景的可能性，并描述它们对海上恐怖主义的不同表现形式的影响。建立的DDBN模型经过了全面的验证和验证过程，采用了各种技术，比如灵敏度，指标、和比较分析。此外,对最近的真实案例进行了测试，以证明其在回顾性和前瞻性风险传播中的有效性，包括诊断和预测能力。这些发现为各种利益相关者提供了宝贵的见解，包括公司和政府机构，促进对海上恐怖主义的理解，并可能加强预防措施和应急管理。
