A Systematic Review of Smart City Infrastructure Threat Modelling Methodologies: A Bayesian Focused Review

Wright, Marc, Chizari, Hassan ORCID: 0000-0002-6253-1822 and Viana, Thiago ORCID: 0000-0001-9380-4611 (2022) A Systematic Review of Smart City Infrastructure Threat Modelling Methodologies: A Bayesian Focused Review. Sustainability, 14 (16). Art 10368. doi:10.3390/su141610368

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Smart city infrastructure and the related theme of critical national infrastructure have attracted growing interest in recent years in academic literature, notably how cyber-security can be effectively applied within the environment, which involves using cyber-physical systems. These operate cross-domain and have massively improved functionality and complexity, especially in threat modelling cyber-security analysis—the disparity between current cyber-security proficiency and the requirements for an effective cyber-security systems implementation. Analysing risk across the entire analysed system can be associated with many different cyber security methods for overall cyber risk analysis or identifying vulnerability for individually modelled objects. One method for performing risk analysis proposed in the literature is by applying Bayesian-based threat modelling methodologies. This paper performs a systematic literature review of Bayesian networks and unique alternative methodologies for smart city infrastructure analysis and related critical national infrastructures. A comparative analysis of the different methodological approaches, considering the many intricacies, metrics, and methods behind them, with suggestions made for future research in the field of cyber-physical threat modelling for smart city infrastructure.

Item Type: Article
Article Type: Article
Uncontrolled Keywords: Smart City Infrastructure; Critical National Infrastructure; Cyber-Physical Systems; Bayesian Networks; Systematic Literature Review; Threat Modelling
Subjects: Q Science > QA Mathematics > QA76 Computer software
Divisions: Schools and Research Institutes > School of Business, Computing and Social Sciences
Research Priority Areas: Applied Business & Technology
Depositing User: Thiago Viana
Date Deposited: 01 Sep 2022 11:36
Last Modified: 31 Oct 2023 11:20
URI: https://eprints.glos.ac.uk/id/eprint/11481

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