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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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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. |
format | Online Article Text |
id | pubmed-9947912 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
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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