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Smart Home IoT Network Risk Assessment Using Bayesian Networks

A risk assessment model for a smart home Internet of Things (IoT) network is implemented using a Bayesian network. The directed acyclic graph of the Bayesian network is constructed from an attack graph that details the paths through which different attacks can occur in the IoT network. The parameter...

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Detalles Bibliográficos
Autores principales: Flores, Miguel, Heredia, Diego, Andrade, Roberto, Ibrahim, Mariam
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9140821/
https://www.ncbi.nlm.nih.gov/pubmed/35626557
http://dx.doi.org/10.3390/e24050668
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author Flores, Miguel
Heredia, Diego
Andrade, Roberto
Ibrahim, Mariam
author_facet Flores, Miguel
Heredia, Diego
Andrade, Roberto
Ibrahim, Mariam
author_sort Flores, Miguel
collection PubMed
description A risk assessment model for a smart home Internet of Things (IoT) network is implemented using a Bayesian network. The directed acyclic graph of the Bayesian network is constructed from an attack graph that details the paths through which different attacks can occur in the IoT network. The parameters of the Bayesian network are estimated with the maximum likelihood method applied to a data set obtained from the simulation of attacks, in five simulation scenarios. For the risk assessment, inferences in the Bayesian network and the impact of the attacks are considered, focusing on DoS attacks, MitM attacks and both at the same time to the devices that allow the automation of the smart home and that are generally the ones that individually have lower levels of security.
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spelling pubmed-91408212022-05-28 Smart Home IoT Network Risk Assessment Using Bayesian Networks Flores, Miguel Heredia, Diego Andrade, Roberto Ibrahim, Mariam Entropy (Basel) Article A risk assessment model for a smart home Internet of Things (IoT) network is implemented using a Bayesian network. The directed acyclic graph of the Bayesian network is constructed from an attack graph that details the paths through which different attacks can occur in the IoT network. The parameters of the Bayesian network are estimated with the maximum likelihood method applied to a data set obtained from the simulation of attacks, in five simulation scenarios. For the risk assessment, inferences in the Bayesian network and the impact of the attacks are considered, focusing on DoS attacks, MitM attacks and both at the same time to the devices that allow the automation of the smart home and that are generally the ones that individually have lower levels of security. MDPI 2022-05-10 /pmc/articles/PMC9140821/ /pubmed/35626557 http://dx.doi.org/10.3390/e24050668 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
Flores, Miguel
Heredia, Diego
Andrade, Roberto
Ibrahim, Mariam
Smart Home IoT Network Risk Assessment Using Bayesian Networks
title Smart Home IoT Network Risk Assessment Using Bayesian Networks
title_full Smart Home IoT Network Risk Assessment Using Bayesian Networks
title_fullStr Smart Home IoT Network Risk Assessment Using Bayesian Networks
title_full_unstemmed Smart Home IoT Network Risk Assessment Using Bayesian Networks
title_short Smart Home IoT Network Risk Assessment Using Bayesian Networks
title_sort smart home iot network risk assessment using bayesian networks
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9140821/
https://www.ncbi.nlm.nih.gov/pubmed/35626557
http://dx.doi.org/10.3390/e24050668
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