Safety, Security and Privacy in Machine Learning Based Internet of Things

[EN] Recent developments in communication and information technologies, especially in the internet of things (IoT), have greatly changed and improved the human lifestyle. Due to the easy access to, and increasing demand for, smart devices, the IoT system faces new cyber-physical security and privacy...

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Detalles Bibliográficos
Autores: Abbas, Ghulam, Mehmood, Amjad, Carsten, Maple, Epiphaniou, Gregory, Lloret, Jaime|||0000-0002-0862-0533
Tipo de recurso: artículo
Fecha de publicación:2022
País:España
Institución:Universitat Politècnica de València (UPV)
Repositorio:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglés
OAI Identifier:oai:riunet.upv.es:10251/202173
Acceso en línea:https://riunet.upv.es/handle/10251/202173
Access Level:acceso abierto
Palabra clave:Internet of things (IoT)
Machine learning
Security and privacy
CPS
INGENIERÍA TELEMÁTICA
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repository_id_str
spelling Safety, Security and Privacy in Machine Learning Based Internet of ThingsAbbas, GhulamMehmood, AmjadCarsten, MapleEpiphaniou, GregoryLloret, Jaime|||0000-0002-0862-0533Internet of things (IoT)Machine learningSecurity and privacyCPSINGENIERÍA TELEMÁTICA[EN] Recent developments in communication and information technologies, especially in the internet of things (IoT), have greatly changed and improved the human lifestyle. Due to the easy access to, and increasing demand for, smart devices, the IoT system faces new cyber-physical security and privacy attacks, such as denial of service, spoofing, phishing, obfuscations, jamming, eavesdropping, intrusions, and other unforeseen cyber threats to IoT systems. The traditional tools and techniques are not very efficient to prevent and protect against the new cyber-physical security challenges. Robust, dynamic, and up-to-date security measures are required to secure IoT systems. The machine learning (ML) technique is considered the most advanced and promising method, and opened up many research directions to address new security challenges in the cyber-physical systems (CPS). This research survey presents the architecture of IoT systems, investigates different attacks on IoT systems, and reviews the latest research directions to solve the safety and security of IoT systems based on machine learning techniques. Moreover, it discusses the potential future research challenges when employing security methods in IoT systems.MDPI AGDepartamento de ComunicacionesEscuela Politécnica Superior de GandiaRepositorio Institucional de la Universitat Politècnica de València Riunet20222022-09-01journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://riunet.upv.es/handle/10251/202173reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valénciainstname:Universitat Politècnica de València (UPV)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Reconocimiento (by)http://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:riunet.upv.es:10251/2021732026-06-13T07:49:27Z
dc.title.none.fl_str_mv Safety, Security and Privacy in Machine Learning Based Internet of Things
title Safety, Security and Privacy in Machine Learning Based Internet of Things
spellingShingle Safety, Security and Privacy in Machine Learning Based Internet of Things
Abbas, Ghulam
Internet of things (IoT)
Machine learning
Security and privacy
CPS
INGENIERÍA TELEMÁTICA
title_short Safety, Security and Privacy in Machine Learning Based Internet of Things
title_full Safety, Security and Privacy in Machine Learning Based Internet of Things
title_fullStr Safety, Security and Privacy in Machine Learning Based Internet of Things
title_full_unstemmed Safety, Security and Privacy in Machine Learning Based Internet of Things
title_sort Safety, Security and Privacy in Machine Learning Based Internet of Things
dc.creator.none.fl_str_mv Abbas, Ghulam
Mehmood, Amjad
Carsten, Maple
Epiphaniou, Gregory
Lloret, Jaime|||0000-0002-0862-0533
author Abbas, Ghulam
author_facet Abbas, Ghulam
Mehmood, Amjad
Carsten, Maple
Epiphaniou, Gregory
Lloret, Jaime|||0000-0002-0862-0533
author_role author
author2 Mehmood, Amjad
Carsten, Maple
Epiphaniou, Gregory
Lloret, Jaime|||0000-0002-0862-0533
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Departamento de Comunicaciones
Escuela Politécnica Superior de Gandia
Repositorio Institucional de la Universitat Politècnica de València Riunet
dc.subject.none.fl_str_mv Internet of things (IoT)
Machine learning
Security and privacy
CPS
INGENIERÍA TELEMÁTICA
topic Internet of things (IoT)
Machine learning
Security and privacy
CPS
INGENIERÍA TELEMÁTICA
description [EN] Recent developments in communication and information technologies, especially in the internet of things (IoT), have greatly changed and improved the human lifestyle. Due to the easy access to, and increasing demand for, smart devices, the IoT system faces new cyber-physical security and privacy attacks, such as denial of service, spoofing, phishing, obfuscations, jamming, eavesdropping, intrusions, and other unforeseen cyber threats to IoT systems. The traditional tools and techniques are not very efficient to prevent and protect against the new cyber-physical security challenges. Robust, dynamic, and up-to-date security measures are required to secure IoT systems. The machine learning (ML) technique is considered the most advanced and promising method, and opened up many research directions to address new security challenges in the cyber-physical systems (CPS). This research survey presents the architecture of IoT systems, investigates different attacks on IoT systems, and reviews the latest research directions to solve the safety and security of IoT systems based on machine learning techniques. Moreover, it discusses the potential future research challenges when employing security methods in IoT systems.
publishDate 2022
dc.date.none.fl_str_mv 2022
2022-09-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://riunet.upv.es/handle/10251/202173
url https://riunet.upv.es/handle/10251/202173
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento (by)
http://creativecommons.org/licenses/by/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento (by)
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv MDPI AG
publisher.none.fl_str_mv MDPI AG
dc.source.none.fl_str_mv reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
instname:Universitat Politècnica de València (UPV)
instname_str Universitat Politècnica de València (UPV)
reponame_str RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
collection RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,301603

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