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A Novel Model for Vulnerability Analysis through Enhanced Directed Graphs and Quantitative Metrics

The rapid evolution of industrial components, the paradigm of Industry 4.0, and the new connectivity features introduced by 5G technology all increase the likelihood of cybersecurity incidents. Such incidents are caused by the vulnerabilities present in these components. Designing a secure system is...

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Autores principales: Longueira-Romero, Ángel, Iglesias, Rosa, Flores, Jose Luis, Garitano, Iñaki
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8952879/
https://www.ncbi.nlm.nih.gov/pubmed/35336299
http://dx.doi.org/10.3390/s22062126
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author Longueira-Romero, Ángel
Iglesias, Rosa
Flores, Jose Luis
Garitano, Iñaki
author_facet Longueira-Romero, Ángel
Iglesias, Rosa
Flores, Jose Luis
Garitano, Iñaki
author_sort Longueira-Romero, Ángel
collection PubMed
description The rapid evolution of industrial components, the paradigm of Industry 4.0, and the new connectivity features introduced by 5G technology all increase the likelihood of cybersecurity incidents. Such incidents are caused by the vulnerabilities present in these components. Designing a secure system is critical, but it is also complex, costly, and an extra factor to manage during the lifespan of the component. This paper presents a model to analyze the known vulnerabilities of industrial components over time. The proposed Extended Dependency Graph (EDG) model is based on two main elements: a directed graph representation of the internal structure of the component, and a set of quantitative metrics based on the Common Vulnerability Scoring System (CVSS). The EDG model can be applied throughout the entire lifespan of a device to track vulnerabilities, identify new requirements, root causes, and test cases. It also helps prioritize patching activities. The model was validated by application to the OpenPLC project. The results reveal that most of the vulnerabilities associated with OpenPLC were related to memory buffer operations and were concentrated in the libssl library. The model was able to determine new requirements and generate test cases from the analysis.
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spelling pubmed-89528792022-03-26 A Novel Model for Vulnerability Analysis through Enhanced Directed Graphs and Quantitative Metrics Longueira-Romero, Ángel Iglesias, Rosa Flores, Jose Luis Garitano, Iñaki Sensors (Basel) Article The rapid evolution of industrial components, the paradigm of Industry 4.0, and the new connectivity features introduced by 5G technology all increase the likelihood of cybersecurity incidents. Such incidents are caused by the vulnerabilities present in these components. Designing a secure system is critical, but it is also complex, costly, and an extra factor to manage during the lifespan of the component. This paper presents a model to analyze the known vulnerabilities of industrial components over time. The proposed Extended Dependency Graph (EDG) model is based on two main elements: a directed graph representation of the internal structure of the component, and a set of quantitative metrics based on the Common Vulnerability Scoring System (CVSS). The EDG model can be applied throughout the entire lifespan of a device to track vulnerabilities, identify new requirements, root causes, and test cases. It also helps prioritize patching activities. The model was validated by application to the OpenPLC project. The results reveal that most of the vulnerabilities associated with OpenPLC were related to memory buffer operations and were concentrated in the libssl library. The model was able to determine new requirements and generate test cases from the analysis. MDPI 2022-03-09 /pmc/articles/PMC8952879/ /pubmed/35336299 http://dx.doi.org/10.3390/s22062126 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Longueira-Romero, Ángel
Iglesias, Rosa
Flores, Jose Luis
Garitano, Iñaki
A Novel Model for Vulnerability Analysis through Enhanced Directed Graphs and Quantitative Metrics
title A Novel Model for Vulnerability Analysis through Enhanced Directed Graphs and Quantitative Metrics
title_full A Novel Model for Vulnerability Analysis through Enhanced Directed Graphs and Quantitative Metrics
title_fullStr A Novel Model for Vulnerability Analysis through Enhanced Directed Graphs and Quantitative Metrics
title_full_unstemmed A Novel Model for Vulnerability Analysis through Enhanced Directed Graphs and Quantitative Metrics
title_short A Novel Model for Vulnerability Analysis through Enhanced Directed Graphs and Quantitative Metrics
title_sort novel model for vulnerability analysis through enhanced directed graphs and quantitative metrics
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8952879/
https://www.ncbi.nlm.nih.gov/pubmed/35336299
http://dx.doi.org/10.3390/s22062126
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