IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v253y2025ics0951832024006100.html
   My bibliography  Save this article

A dynamic importance function for accidental scenarios generation by RESTART in the computational risk assessment of cyber-physical infrastructures

Author

Listed:
  • Futalef, Juan-Pablo
  • Di Maio, Francesco
  • Zio, Enrico

Abstract

The Computational Risk Assessment (CRA) of Cyber-Physical Systems (CPSs) calls for the analysis of accidental scenarios emerging from the complexities and interdependencies typical of CPSs. Generating these scenarios via crude Monte Carlo Simulation (MCS) is impractical due to the high computational demand of simulation codes of CPSs, considering the combinatorial number of possible scenarios. In this paper, we tailor the use of Repetitive Simulation Trials After Reaching Thresholds (RESTART), a rare-event simulation method of literature, to efficiently generate relevant accidental scenarios. The tailored RESTART is guided by a dynamic Importance Function (IF) originally introduced here to dynamically characterize the relevance of the scenarios with reference to the current topology of the CPS and the susceptibility of its components. Two case studies of increasing complexity are considered: a single power grid and a CPS consisting of an Integrated Power and Telecommunication (IP&TLC) infrastructure. Results show that RESTART mines out more relevant scenarios than crude MCS for a number of different IFs based on vulnerability metrics of literature, and thus particularly efficiently when the novel IF is adopted.

Suggested Citation

  • Futalef, Juan-Pablo & Di Maio, Francesco & Zio, Enrico, 2025. "A dynamic importance function for accidental scenarios generation by RESTART in the computational risk assessment of cyber-physical infrastructures," Reliability Engineering and System Safety, Elsevier, vol. 253(C).
    • Handle: RePEc:eee:reensy:v:253:y:2025:i:c:s0951832024006100
    DOI: 10.1016/j.ress.2024.110538
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832024006100
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2024.110538?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Enrico Zio, 2013. "The Monte Carlo Simulation Method for System Reliability and Risk Analysis," Springer Series in Reliability Engineering, Springer, edition 127, number 978-1-4471-4588-2, April.
    2. Au, Siu-Kui & Patelli, Edoardo, 2016. "Rare event simulation in finite-infinite dimensional space," Reliability Engineering and System Safety, Elsevier, vol. 148(C), pages 67-77.
    3. Zio, Enrico & Piccinelli, Roberta & Delfanti, Maurizio & Olivieri, Valeria & Pozzi, Mauro, 2012. "Application of the load flow and random flow models for the analysis of power transmission networks," Reliability Engineering and System Safety, Elsevier, vol. 103(C), pages 102-109.
    4. Di Maio, Francesco & Pettorossi, Chiara & Zio, Enrico, 2023. "Entropy-driven Monte Carlo simulation method for approximating the survival signature of complex infrastructures," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    5. Eusgeld, Irene & Kröger, Wolfgang & Sansavini, Giovanni & Schläpfer, Markus & Zio, Enrico, 2009. "The role of network theory and object-oriented modeling within a framework for the vulnerability analysis of critical infrastructures," Reliability Engineering and System Safety, Elsevier, vol. 94(5), pages 954-963.
    6. Francesco Di Maio & Chiara Pettorossi & Enrico Zio, 2023. "Entropy-driven Monte Carlo simulation method for approximating the survival signature of complex infrastructures," Post-Print hal-04103855, HAL.
    7. R. Piccinelli & G. Sansavini & R. Lucchetti & E. Zio, 2017. "A General Framework for the Assessment of Power System Vulnerability to Malicious Attacks," Risk Analysis, John Wiley & Sons, vol. 37(11), pages 2182-2190, November.
    8. Zio, E. & Pedroni, N., 2010. "An optimized Line Sampling method for the estimation of the failure probability of nuclear passive systems," Reliability Engineering and System Safety, Elsevier, vol. 95(12), pages 1300-1313.
    9. Pieter-Tjerk de Boer & Dirk Kroese & Shie Mannor & Reuven Rubinstein, 2005. "A Tutorial on the Cross-Entropy Method," Annals of Operations Research, Springer, vol. 134(1), pages 19-67, February.
    10. Abedi, Amin & Gaudard, Ludovic & Romerio, Franco, 2019. "Review of major approaches to analyze vulnerability in power system," Reliability Engineering and System Safety, Elsevier, vol. 183(C), pages 153-172.
    11. Paul Glasserman & Philip Heidelberger & Perwez Shahabuddin & Tim Zajic, 1999. "Multilevel Splitting for Estimating Rare Event Probabilities," Operations Research, INFORMS, vol. 47(4), pages 585-600, August.
    12. Turati, Pietro & Pedroni, Nicola & Zio, Enrico, 2016. "Advanced RESTART method for the estimation of the probability of failure of highly reliable hybrid dynamic systems," Reliability Engineering and System Safety, Elsevier, vol. 154(C), pages 117-126.
    13. Cadini, Francesco & Agliardi, Gian Luca & Zio, Enrico, 2017. "Estimation of rare event probabilities in power transmission networks subject to cascading failures," Reliability Engineering and System Safety, Elsevier, vol. 158(C), pages 9-20.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Rocchetta, Roberto, 2022. "Enhancing the resilience of critical infrastructures: Statistical analysis of power grid spectral clustering and post-contingency vulnerability metrics," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).
    2. Turati, Pietro & Pedroni, Nicola & Zio, Enrico, 2016. "Advanced RESTART method for the estimation of the probability of failure of highly reliable hybrid dynamic systems," Reliability Engineering and System Safety, Elsevier, vol. 154(C), pages 117-126.
    3. Chan, Jianpeng & Papaioannou, Iason & Straub, Daniel, 2024. "Bayesian improved cross entropy method with categorical mixture models for network reliability assessment," Reliability Engineering and System Safety, Elsevier, vol. 252(C).
    4. Sperstad, Iver Bakken & Kjølle, Gerd H. & Gjerde, Oddbjørn, 2020. "A comprehensive framework for vulnerability analysis of extraordinary events in power systems," Reliability Engineering and System Safety, Elsevier, vol. 196(C).
    5. Chu, Xiaolei & Wang, Ziqi, 2025. "Maximum entropy-based modeling of community-level hazard responses for civil infrastructures," Reliability Engineering and System Safety, Elsevier, vol. 254(PA).
    6. Tito Homem-de-Mello, 2007. "A Study on the Cross-Entropy Method for Rare-Event Probability Estimation," INFORMS Journal on Computing, INFORMS, vol. 19(3), pages 381-394, August.
    7. Gjorgiev, Blazhe & Sansavini, Giovanni, 2022. "Identifying and assessing power system vulnerabilities to transmission asset outages via cascading failure analysis," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    8. Kong Fah Tee & Andrew Utomi Ebenuwa, 2019. "Combination of line sampling and important sampling for reliability assessment of buried pipelines," Journal of Risk and Reliability, , vol. 233(2), pages 139-150, April.
    9. Su, Huai & Zio, Enrico & Zhang, Jinjun & Li, Xueyi, 2018. "A systematic framework of vulnerability analysis of a natural gas pipeline network," Reliability Engineering and System Safety, Elsevier, vol. 175(C), pages 79-91.
    10. Chen, Weidong & Xu, Chunlong & Shi, Yaqin & Ma, Jingxin & Lu, Shengzhuo, 2019. "A hybrid Kriging-based reliability method for small failure probabilities," Reliability Engineering and System Safety, Elsevier, vol. 189(C), pages 31-41.
    11. Da, Gaofeng & Zhang, Xin & He, Zhenwen & Ding, Weiyong, 2025. "Estimating the all-terminal signatures for networks by using deep neural network," Reliability Engineering and System Safety, Elsevier, vol. 253(C).
    12. Papaioannou, Iason & Geyer, Sebastian & Straub, Daniel, 2019. "Improved cross entropy-based importance sampling with a flexible mixture model," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    13. Abedi, Amin & Gaudard, Ludovic & Romerio, Franco, 2019. "Review of major approaches to analyze vulnerability in power system," Reliability Engineering and System Safety, Elsevier, vol. 183(C), pages 153-172.
    14. David, Alexander E. & Gjorgiev, Blazhe & Sansavini, Giovanni, 2020. "Quantitative comparison of cascading failure models for risk-based decision making in power systems," Reliability Engineering and System Safety, Elsevier, vol. 198(C).
    15. Ahmed, Shoaib & Li, Tie & Zhou, Xin Yi & Yi, Ping & Chen, Run, 2025. "Quantifying the environmental footprints of biofuels for sustainable passenger ship operations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 207(C).
    16. Zohre Alipour & Mohammad Ali Saniee Monfared & Enrico Zio, 2014. "Comparing topological and reliability-based vulnerability analysis of Iran power transmission network," Journal of Risk and Reliability, , vol. 228(2), pages 139-151, April.
    17. Fabian Dickmann & Nikolaus Schweizer, 2014. "Faster Comparison of Stopping Times by Nested Conditional Monte Carlo," Papers 1402.0243, arXiv.org.
    18. Dikshit, Saransh & Dobson, Ian & Alipour, Alice, 2024. "Cascading structural failures of towers in an electric power transmission line due to straight line winds," Reliability Engineering and System Safety, Elsevier, vol. 250(C).
    19. Tang, Daogui & Fang, Yi-Ping & Zio, Enrico, 2023. "Vulnerability analysis of demand-response with renewable energy integration in smart grids to cyber attacks and online detection methods," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    20. Johansson, Jonas & Hassel, Henrik, 2010. "An approach for modelling interdependent infrastructures in the context of vulnerability analysis," Reliability Engineering and System Safety, Elsevier, vol. 95(12), pages 1335-1344.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:reensy:v:253:y:2025:i:c:s0951832024006100. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/reliability-engineering-and-system-safety .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.