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Dynamic Risk Assessment in Cybersecurity: A Systematic Literature Review

Author

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  • Pavlos Cheimonidis

    (Department of Computer Science, International Hellenic University, 654 04 Kavala, Greece)

  • Konstantinos Rantos

    (Department of Computer Science, International Hellenic University, 654 04 Kavala, Greece)

Abstract

Traditional information security risk assessment (RA) methodologies and standards, adopted by information security management systems and frameworks as a foundation stone towards robust environments, face many difficulties in modern environments where the threat landscape changes rapidly and new vulnerabilities are being discovered. In order to overcome this problem, dynamic risk assessment (DRA) models have been proposed to continuously and dynamically assess risks to organisational operations in (near) real time. The aim of this work is to analyse the current state of DRA models that have been proposed for cybersecurity, through a systematic literature review. The screening process led us to study 50 DRA models, categorised based on the respective primary analysis methods they used. The study provides insights into the key characteristics of these models, including the maturity level of the examined models, the domain or application area in which these models flourish, and the information they utilise in order to produce results. The aim of this work is to answer critical research questions regarding the development of dynamic risk assessment methodologies and provide insights on the already developed methods as well as future research directions.

Suggested Citation

  • Pavlos Cheimonidis & Konstantinos Rantos, 2023. "Dynamic Risk Assessment in Cybersecurity: A Systematic Literature Review," Future Internet, MDPI, vol. 15(10), pages 1-25, September.
    • Handle: RePEc:gam:jftint:v:15:y:2023:i:10:p:324-:d:1250121
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    References listed on IDEAS

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    1. Qianxiang Zhu & Yuanqing Qin & Chunjie Zhou & Weiwei Gao, 2018. "Extended multilevel flow model-based dynamic risk assessment for cybersecurity protection in industrial production systems," International Journal of Distributed Sensor Networks, , vol. 14(6), pages 15501477187, June.
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    Cited by:

    1. Davide Cerotti & Daniele Codetta Raiteri & Giovanna Dondossola & Lavinia Egidi & Giuliana Franceschinis & Luigi Portinale & Davide Savarro & Roberta Terruggia, 2024. "SecuriDN: A Modeling Tool Supporting the Early Detection of Cyberattacks to Smart Energy Systems," Energies, MDPI, vol. 17(16), pages 1-30, August.

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