Vulnerability analysis of critical infrastructure network

2021 ◽  
Vol 35 ◽  
pp. 100472
Author(s):  
A. Kizhakkedath ◽  
K. Tai
Keyword(s):  
Critical Infrastructure ◽  
Vulnerability Analysis ◽  
Infrastructure Network

Conceptualization of a Critical Infrastructure Network – Model for Flood Risk Assessments

2021 ◽  
Author(s):  
Roman Schotten ◽  
Daniel Bachmann
Keyword(s):  
Risk Analysis ◽  
Flood Risk ◽  
Critical Infrastructure ◽  
Risk Mitigation ◽  
Mitigation Measures ◽  
Critical Infrastructures ◽  
Flood Exposure ◽  
Infrastructure Network ◽  
Flood Risk Analysis ◽  
De Bruijn

<p><span>In flood risk analysis it is a key principle to predetermine consequences of flooding to assets, people and infrastructures. Damages to critical infrastructures are not restricted to the flooded area. The effects of directly affected objects cascades to other infrastructures, which are not directly affected by a flood. Modelling critical infrastructure networks is one possible answer to the question ‘how to include indirect and direct impacts to critical infrastructures?’.</span></p><p>Critical infrastructures are connected in very complex networks. The modelling of those networks has been a basis for different purposes (Ouyang, 2014). Thus, it is a challenge to determine the right method to model a critical infrastructure network. For this example, a network-based and topology-based method will be applied (Pant et al., 2018). The basic model elements are points, connectors and polygons which are utilized to resemble the critical infrastructure network characteristics.</p><p>The objective of this model is to complement the state-of-the-art flood risk analysis with a quantitative analysis of critical infrastructure damages and disruptions for people and infrastructures. These results deliver an extended basis to differentiate the flood risk assessment and to derive measures for flood risk mitigation strategies. From a technical point of view, a critical infrastructure damage analysis will be integrated into the tool ProMaIDes (Bachmann, 2020), a free software for a risk-based evaluation of flood risk mitigation measures.</p><p>The data on critical infrastructure cascades and their potential linkages is scars but necessary for an actionable modelling. The CIrcle method from Deltares delivers a method for a workshop that has proven to deliver applicable datasets for identifying and connecting infrastructures on basis of cascading effects (de Bruijn et al., 2019). The data gained from CIrcle workshops will be one compound for the critical infrastructure network model.</p><p>Acknowledgment: This work is part of the BMBF-IKARIM funded project PARADes (Participatory assessment of flood related disaster prevention and development of an adapted coping system in Ghana).</p><p>Bachmann, D. (2020). ProMaIDeS - Knowledge Base. https://promaides.myjetbrains.com</p><p>de Bruijn, K. M., Maran, C., Zygnerski, M., Jurado, J., Burzel, A., Jeuken, C., & Obeysekera, J. (2019). Flood resilience of critical infrastructure: Approach and method applied to Fort Lauderdale, Florida. Water (Switzerland), 11(3). https://doi.org/10.3390/w11030517</p><p>Ouyang, M. (2014). Review on modeling and simulation of interdependent critical infrastructure systems. Reliability Engineering and System Safety, 121, 43–60. https://doi.org/10.1016/j.ress.2013.06.040</p><p>Pant, R., Thacker, S., Hall, J. W., Alderson, D., & Barr, S. (2018). Critical infrastructure impact assessment due to flood exposure. Journal of Flood Risk Management, 11(1), 22–33. https://doi.org/10.1111/jfr3.12288</p>

scholarly journals Complexity and Vulnerability Analysis of Critical Infrastructures: A Methodological Approach

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Yongliang Deng ◽  
Liangliang Song ◽  
Zhipeng Zhou ◽  
Ping Liu
Keyword(s):  
Decision Making ◽  
Critical Infrastructure ◽  
Methodological Approach ◽  
Network Models ◽  
Vulnerability Analysis ◽  
Clustering Coefficient ◽  
Near Miss ◽  
System Component ◽  
Emergency Situations ◽  
New Research

Vulnerability analysis of network models has been widely adopted to explore the potential impacts of random disturbances, deliberate attacks, and natural disasters. However, almost all these models are based on a fixed topological structure, in which the physical properties of infrastructure components and their interrelationships are not well captured. In this paper, a new research framework is put forward to quantitatively explore and assess the complexity and vulnerability of critical infrastructure systems. Then, a case study is presented to prove the feasibility and validity of the proposed framework. After constructing metro physical network (MPN), Pajek is employed to analyze its corresponding topological properties, including degree, betweenness, average path length, network diameter, and clustering coefficient. With a comprehensive understanding of the complexity of MPN, it would be beneficial for metro system to restrain original near-miss or accidents and support decision-making in emergency situations. Moreover, through the analysis of two simulation protocols for system component failure, it is found that the MPN turned to be vulnerable under the condition that the high-degree nodes or high-betweenness edges are attacked. These findings will be conductive to offer recommendations and proposals for robust design, risk-based decision-making, and prioritization of risk reduction investment.

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