scholarly journals Dimensionality reduction of the resilience model of a critical infrastructure network by means of elementary effects sensitivity analysis

2016 ◽  
pp. 2797-2804
Author(s):  
P Turati
Keyword(s):  
Sensitivity Analysis ◽  
Dimensionality Reduction ◽  
Critical Infrastructure ◽  
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 G-CAS: Greedy Algorithm-Based Security Event Correlation System for Critical Infrastructure Network

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Peng Lu ◽  
Teng Hu ◽  
Hao Wang ◽  
Ruobin Zhang ◽  
Guo Wu
Keyword(s):  
Correlation Analysis ◽  
Real Time ◽  
Greedy Algorithm ◽  
Critical Infrastructure ◽  
Time Correlation ◽  
Event Analysis ◽  
Event Correlation ◽  
Security Threat ◽  
Real Time Analysis ◽  
Infrastructure Network

The attacks on the critical infrastructure network have increased sharply, and the strict management measures of the critical infrastructure network have caused its correlation analysis technology for security events to be relatively backward; this makes the critical infrastructure network’s security situation more severe. Currently, there is no common correlation analysis technology for the critical infrastructure network, and most technologies focus on expanding the dimension of data analysis, but with less attention to the optimization of analysis performance. The analysis performance does not meet the practical environment, and real-time analysis is even more impossible; as a result, the efficiency of security threat detection is greatly declined. To solve this issue, we propose the greedy tree algorithm, a correlation analysis approach based on the greedy algorithm, which optimizes event analysis steps and significantly improves the performance, so the real-time correlation analysis can be realized. We first verify the performance of the algorithm through formalization, and then the G-CAS (Greedy Correlation Analysis System) is implemented based on this algorithm and is applied in a real critical infrastructure network, which outperformed the current mainstream products.

Modelling critical infrastructure network interdependencies and failure

2019 ◽  
Vol 15 (1) ◽  
pp. 1
Author(s):  
Sim Mong Soon ◽  
Robert Tiong Lee Kong ◽  
Lin Jiwei ◽  
Tai Kang
Keyword(s):  
Critical Infrastructure ◽  
Infrastructure Network
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