A novel critical infrastructure resilience assessment approach using dynamic Bayesian networks

Cai, Baoping; Xie, Min; Liu, Yonghong; Liu, Yiliu; Ji, Renjie; Qing, Feng

doi:10.1063/1.5005245

Cited by 8 publications

(7 citation statements)

References 5 publications

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“…Nan and Sansavini multiplied residual performance, recovery rate, restored performance, and the reciprocals of failure rate and cumulative impact [61], [186]. Cai et al proposed a more complex resilience metric with the product of steady-state, residual, and recovery performance and natural logarithm of disruption time and recovery duration [236], [237]. Across these examples, weighting and form validation is generally underexplored.…”

Section: Ensembles Of Summary Metric Functionsmentioning

confidence: 99%

Infrastructure resilience curves: Performance measures and summary metrics

Poulin

Kane

2021

Reliability Engineering & System Safety

111

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Section: Ensembles Of Summary Metric Functionsmentioning

confidence: 99%

Infrastructure resilience curves: Performance measures and summary metrics

Poulin

Kane

2021

Reliability Engineering & System Safety

111

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“…• methods from the domain of management science which can be adapted to issues management of CI safety (Ouyang, 2014;Hurley, 2017;Cai et al, 2017;Tien and Kiureghain, 2017;Johansen and Tien, 2018), • methods for exchange of information on the threats to which CI facilities are vulnerable (Caldwell, 2015;Häyhtiö and Zaerens, 2017), • establishing a safety threshold 3 for the functionality of CI facilities (Manas, 2017;Hatton et al, 2018).…”

Section: The Protection Of Critical Infrastructurementioning

confidence: 99%

The Role of Integral Model of Critical Infrastructure Safety in Industry 4.0

Wiśniewski¹

2021

ERSJ

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Purpose: This paper aims to create a dedicated tool for managing the level of availability of services delivered by critical infrastructure (CI) facilities, which will affect the Sustainable Development Goals. The paper established a link between sustainability, Industry 4.0, and critical infrastructure. The literature analysis has indicated that the security of Industry 4.0 is discussed almost exclusively in the perspective of cyber security. Design/Methodology/Approach: Verification of the tool was carried out based on simulations and computational experience using data PKN ORLEN Refinery Inc. Findings: As a result of the work carried out, an Integral Model of Critical Infrastructure Safety (IMCIS) was obtained, which allows us to present CI's current characteristics. It was established that the canon of characterization of CI objects consists of a set of resources, a set of functionalities, a set of threats, a set of safeguards, a set of threat dependencies, and a set of dependencies of objects recognized as CI. Practical Implications: A universal application of the IMCIS is to estimate the level of risk before and after implementing safeguards and generating adverse event scenarios. The identified threats are the decision-making areas of the decision problem, whose solution is indicated by a set of safeguards reducing the risk level to an acceptable level. Originality/Value: The solution can be used to identify a set of safeguards that, on the one hand, will reduce the cost of the business operator to a minimum and, on the other hand, achieve the required level of availability of CI functionality. In addition, IMCIS allows analyzing all threats that may affect the infrastructure of Industry 4.0, taking into account many independent decision-making centers.

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“…For example, Bayesian networks are identified as suitable models for probabilistic knowledge presentation and used for evaluation of the reliability as well as resilience of a variety of systems and infrastructures [6][7][8]26]. The stress-strain model developed in [27] is considered as a generic approach to operationalize the four cornerstones of resilience, as outlined in [15] and [16].…”

Section: Modelling Of Resiliencementioning

confidence: 99%

“…In general, there is a great variety of resilience definitions. They differ in perspective, scope, and objective, and ultimately result in different approaches to describe, evaluate, and enhance the resilience of infrastructure systems [3][4][5][6][7][8][9][10][11][12][13][14]. The variety of resilience definitions and the large application range of resilience developments has already led to a wide spectrum of resilience models.…”

Section: Introductionmentioning

confidence: 99%

Finite State Machine Modelling to Facilitate the Resilience of Infrastructures: Reflections

Engler¹,

Baldauf

Torres

et al. 2020

Infrastructures

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The ability of an infrastructure to be resistant against hazards or to accommodate and recover from hazard-induced destructions and disturbances is characterized as resilience. Usually, infrastructures are engineered socio-technical systems or systems-of-systems. Jackson and Ferris consider the use of finite state machine (FSM) modelling as a suitable means to depict and investigate the resilience of such engineered systems. This paper discusses the capabilities and limitations of the FSM model proposed by Jackson and Ferris and if it should be used for the representation and evaluation of the resilience of an infrastructure. The discussion is conducted on a more general level. However, special attention is paid to monitoring because, on the one hand, monitoring is one of the cornerstones of resilience and, on the other hand, Scott and Ferris define a state that is emphasized by an increased level of situational awareness as a result of happened and perceived events. Consequently, the question has to be answered of how the models are able to reflect the need for routine monitoring of the resilience of infrastructures in order to initiate, if necessary, adjustment procedures as an appropriate response to changes in internal and external conditions. The results of this theoretical study are a fundamental step towards the practical application of the FSM approach for the design of resilient infrastructures.

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A novel critical infrastructure resilience assessment approach using dynamic Bayesian networks

Cited by 8 publications

References 5 publications

Infrastructure resilience curves: Performance measures and summary metrics

Infrastructure resilience curves: Performance measures and summary metrics

The Role of Integral Model of Critical Infrastructure Safety in Industry 4.0

Finite State Machine Modelling to Facilitate the Resilience of Infrastructures: Reflections

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