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

Author

Listed:
  • Marc Wright

    (School of Computing and Engineering, University of Gloucestershire, Cheltenham GL50 2RH, UK)

  • Hassan Chizari

    (School of Computing and Engineering, University of Gloucestershire, Cheltenham GL50 2RH, UK)

  • Thiago Viana

    (School of Computing and Engineering, University of Gloucestershire, Cheltenham GL50 2RH, UK)

Abstract

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.

Suggested Citation

  • Marc Wright & Hassan Chizari & Thiago Viana, 2022. "A Systematic Review of Smart City Infrastructure Threat Modelling Methodologies: A Bayesian Focused Review," Sustainability, MDPI, vol. 14(16), pages 1-27, August.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:16:p:10368-:d:893111
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