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Uncertainty in vulnerability of networks under attack

This study builds conceptual explanations and empirical examinations of the vulnerability response of networks under attack. Two quantities of “vulnerability” and “uncertainty in vulnerability” are defined by scrutinizing the performance loss trajectory of networks experiencing attacks. Both vulnera...

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Autores principales: Ermagun, Alireza, Tajik, Nazanin, Mahmassani, Hani
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9947912/
https://www.ncbi.nlm.nih.gov/pubmed/36823226
http://dx.doi.org/10.1038/s41598-023-29899-w
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author Ermagun, Alireza
Tajik, Nazanin
Mahmassani, Hani
author_facet Ermagun, Alireza
Tajik, Nazanin
Mahmassani, Hani
author_sort Ermagun, Alireza
collection PubMed
description This study builds conceptual explanations and empirical examinations of the vulnerability response of networks under attack. Two quantities of “vulnerability” and “uncertainty in vulnerability” are defined by scrutinizing the performance loss trajectory of networks experiencing attacks. Both vulnerability and uncertainty in vulnerability quantities are a function of the network topology and size. This is tested on 16 distinct topologies appearing in infrastructure, social, and biological networks with 8 to 26 nodes under two percolation scenarios exemplifying benign and malicious attacks. The findings imply (i) crossing path, tree, and diverging tail are the most vulnerable topologies, (ii) complete and matching pairs are the least vulnerable topologies, (iii) complete grid and complete topologies show the most uncertainty for vulnerability, and (iv) hub-and-spoke and double u exhibit the least uncertainty in vulnerability. The findings also imply that both vulnerability and uncertainty in vulnerability increase with an increase in the size of the network. It is argued that in networks with no undirected cycle and one undirected cycle, the uncertainty in vulnerability is maximal earlier in the percolation process. With an increase in the number of cycles, the uncertainty in vulnerability is accumulated at the end of the percolation process. This emphasizes the role of tailoring preparedness, response, and recovery phases for networks with different topologies when they might experience disruption.
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spelling pubmed-99479122023-02-23 Uncertainty in vulnerability of networks under attack Ermagun, Alireza Tajik, Nazanin Mahmassani, Hani Sci Rep Article This study builds conceptual explanations and empirical examinations of the vulnerability response of networks under attack. Two quantities of “vulnerability” and “uncertainty in vulnerability” are defined by scrutinizing the performance loss trajectory of networks experiencing attacks. Both vulnerability and uncertainty in vulnerability quantities are a function of the network topology and size. This is tested on 16 distinct topologies appearing in infrastructure, social, and biological networks with 8 to 26 nodes under two percolation scenarios exemplifying benign and malicious attacks. The findings imply (i) crossing path, tree, and diverging tail are the most vulnerable topologies, (ii) complete and matching pairs are the least vulnerable topologies, (iii) complete grid and complete topologies show the most uncertainty for vulnerability, and (iv) hub-and-spoke and double u exhibit the least uncertainty in vulnerability. The findings also imply that both vulnerability and uncertainty in vulnerability increase with an increase in the size of the network. It is argued that in networks with no undirected cycle and one undirected cycle, the uncertainty in vulnerability is maximal earlier in the percolation process. With an increase in the number of cycles, the uncertainty in vulnerability is accumulated at the end of the percolation process. This emphasizes the role of tailoring preparedness, response, and recovery phases for networks with different topologies when they might experience disruption. Nature Publishing Group UK 2023-02-23 /pmc/articles/PMC9947912/ /pubmed/36823226 http://dx.doi.org/10.1038/s41598-023-29899-w Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Ermagun, Alireza
Tajik, Nazanin
Mahmassani, Hani
Uncertainty in vulnerability of networks under attack
title Uncertainty in vulnerability of networks under attack
title_full Uncertainty in vulnerability of networks under attack
title_fullStr Uncertainty in vulnerability of networks under attack
title_full_unstemmed Uncertainty in vulnerability of networks under attack
title_short Uncertainty in vulnerability of networks under attack
title_sort uncertainty in vulnerability of networks under attack
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9947912/
https://www.ncbi.nlm.nih.gov/pubmed/36823226
http://dx.doi.org/10.1038/s41598-023-29899-w
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