Data-driven risk mitigation of Southeast Asian maritime security

Muhammad Ilham Fahreza, Enna Hirata, Kazuhiko Ishiguro and Kevin X. Li

Maritime Business Review, 2026, vol. 11, issue 4, 339-358

Abstract: PurposeThe aim of this research is to develop a comprehensive, data-driven framework for analyzing and mitigating piracy and armed robbery incidents in high-risk maritime corridors, particularly the Strait of Malacca. The study integrates Bayesian network (BN) analysis with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to objectively identify key risk factors, simulate various scenarios and rank effective strategies for reducing piracy incidents.Design/methodology/approachThe study employs a combined BN-TOPSIS approach. First, a tree-augmented BN model is constructed using influential risk factors related to piracy attacks, extracted from historical incident data, to determine probabilistic dependencies. Sensitivity analyses then identify the mutual information values that are used as objective weights for the criteria in the TOPSIS multi-criteria decision-making model. TOPSIS is then applied to systematically evaluate and rank potential intervention strategies under multiple simulated scenarios.FindingsThe integrated BN-TOPSIS framework effectively identifies critical factors, such as “ship area boarded” and “crew response,” as high-impact variables. Among the proposed solutions, ship-level interventions, such as improved crew readiness and physical ship security, are significantly more effective at reducing successful attacks than broader external coordination measures, such as joint patrols. The model confirms that, despite current preventive measures, the probability of successful piracy remains high (approximately 57%), highlighting the urgent need for strategic improvements.Research limitations/implicationsThe model presently focuses on technical and operational factors extracted from incident reports, omitting broader socio-political and economic dimensions influencing piracy risks. Additionally, it is tailored to Southeast Asian maritime conditions and requires customization before applying to other piracy-prone regions. Future work should incorporate macro-level variables and adapt the framework for generalized geographic contexts.Practical implicationsThis integrated BN-TOPSIS framework provides policymakers and maritime security stakeholders with a robust, evidence-based tool for prioritizing anti-piracy measures. The quantitative insights promote the effective allocation of resources toward ship-specific defenses and crew training, enabling faster, data-supported decision-making. Streamlining reporting systems and enhancing joint task forces can complement these core interventions, further strengthening maritime safety and resilience in critical trade routes.Originality/valueBy combining the probabilistic modeling capabilities of BN with the objective ranking abilities of TOPSIS, the approach overcomes the limitations of each method when used individually. Unlike previous approaches that relied heavily on subjective expert judgment, this framework uses real-world incident data and a quantitative method to provide an unbiased evaluation of risk and strategy. It also enables scenario-based testing to prioritize interventions quantitatively, which is a novel approach in maritime security research.

Keywords: Piracy; Armed robbery; Bayesian network; TOPSIS; Maritime security; Strategy evaluation; Risk mitigation (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:eme:mabrpp:mabr-08-2025-0078

DOI: 10.1108/MABR-08-2025-0078

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