A Lightweight IDS Based on Blockchain and Machine Learning for Detecting Physical Attacks in Wireless Sensor Networks

[EN] Wireless sensor networks (WSNs) are vulnerable to physical attacks in which adversaries gain partial or full control of sensor nodes, compromising the integrity of the network. Conventional security mechanisms impose excessive computational overhead and are not well suited to resource-constrain...

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Detalhes bibliográficos
Autores: Jabor, Maytham S., Azez, Aqeel S., Campelo Rivadulla, José Carlos|||0000-0003-0558-7683, Bonastre Pina, Alberto Miguel|||0000-0003-3639-8420
Tipo de documento: artigo
Data de publicação:2026
País:España
Recursos:Universitat Politècnica de València (UPV)
Repositório:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglês
OAI Identifier:oai:dnet:riunet______::85627c55b0f9059478fbdd365475acea
Acesso em linha:https://riunet.upv.es/handle/10251/236093
Access Level:Acceso aberto
Palavra-chave:WSN
Blockchain
IDS
Physical attack
ANN
Lightweight
Intrusion detection
09.- Desarrollar infraestructuras resilientes, promover la industrialización inclusiva y sostenible, y fomentar la innovación
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oai_identifier_str oai:dnet:riunet______::85627c55b0f9059478fbdd365475acea
network_acronym_str ES
network_name_str España
repository_id_str
spelling A Lightweight IDS Based on Blockchain and Machine Learning for Detecting Physical Attacks in Wireless Sensor NetworksJabor, Maytham S.Azez, Aqeel S.Campelo Rivadulla, José Carlos|||0000-0003-0558-7683Bonastre Pina, Alberto Miguel|||0000-0003-3639-8420WSNBlockchainIDSPhysical attackANNLightweightIntrusion detection09.- Desarrollar infraestructuras resilientes, promover la industrialización inclusiva y sostenible, y fomentar la innovación[EN] Wireless sensor networks (WSNs) are vulnerable to physical attacks in which adversaries gain partial or full control of sensor nodes, compromising the integrity of the network. Conventional security mechanisms impose excessive computational overhead and are not well suited to resource-constrained WSN devices. This paper proposes a lightweight, two-layer intrusion detection system (IDS) that integrates blockchain (BC) technology with machine learning for physical attack detection in WSNs. The first layer employs a lightweight BC protocol among cluster heads (CHs) and the base station (BS) to detect data integrity violations through hash-based consensus. The second layer applies an artificial neural network (ANN) at the base station to detect attacks that bypass blockchain verification, without imposing any processing load on sensor nodes. Simulation experiments on a 100-node WSN demonstrate that the combined system achieves 97.42% accuracy and 98.35% recall, outperforming five established classifiers and both standalone components. The system sustains detection rates above 99.98% under 30 simultaneous attackers and maintains reliable operation under packet loss conditions up to 10%.MDPI AGDepartamento de Informática de Sistemas y ComputadoresInstituto Universitario de Tecnologías de la Información y ComunicacionesEscuela Técnica Superior de Ingeniería InformáticaRepositorio Institucional de la Universitat Politècnica de València Riunet20262026-03-20journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://riunet.upv.es/handle/10251/236093reponame: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:dnet:riunet______::85627c55b0f9059478fbdd365475acea2026-06-13T07:49:27Z
dc.title.none.fl_str_mv A Lightweight IDS Based on Blockchain and Machine Learning for Detecting Physical Attacks in Wireless Sensor Networks
title A Lightweight IDS Based on Blockchain and Machine Learning for Detecting Physical Attacks in Wireless Sensor Networks
spellingShingle A Lightweight IDS Based on Blockchain and Machine Learning for Detecting Physical Attacks in Wireless Sensor Networks
Jabor, Maytham S.
WSN
Blockchain
IDS
Physical attack
ANN
Lightweight
Intrusion detection
09.- Desarrollar infraestructuras resilientes, promover la industrialización inclusiva y sostenible, y fomentar la innovación
title_short A Lightweight IDS Based on Blockchain and Machine Learning for Detecting Physical Attacks in Wireless Sensor Networks
title_full A Lightweight IDS Based on Blockchain and Machine Learning for Detecting Physical Attacks in Wireless Sensor Networks
title_fullStr A Lightweight IDS Based on Blockchain and Machine Learning for Detecting Physical Attacks in Wireless Sensor Networks
title_full_unstemmed A Lightweight IDS Based on Blockchain and Machine Learning for Detecting Physical Attacks in Wireless Sensor Networks
title_sort A Lightweight IDS Based on Blockchain and Machine Learning for Detecting Physical Attacks in Wireless Sensor Networks
dc.creator.none.fl_str_mv Jabor, Maytham S.
Azez, Aqeel S.
Campelo Rivadulla, José Carlos|||0000-0003-0558-7683
Bonastre Pina, Alberto Miguel|||0000-0003-3639-8420
author Jabor, Maytham S.
author_facet Jabor, Maytham S.
Azez, Aqeel S.
Campelo Rivadulla, José Carlos|||0000-0003-0558-7683
Bonastre Pina, Alberto Miguel|||0000-0003-3639-8420
author_role author
author2 Azez, Aqeel S.
Campelo Rivadulla, José Carlos|||0000-0003-0558-7683
Bonastre Pina, Alberto Miguel|||0000-0003-3639-8420
author2_role author
author
author
dc.contributor.none.fl_str_mv Departamento de Informática de Sistemas y Computadores
Instituto Universitario de Tecnologías de la Información y Comunicaciones
Escuela Técnica Superior de Ingeniería Informática
Repositorio Institucional de la Universitat Politècnica de València Riunet
dc.subject.none.fl_str_mv WSN
Blockchain
IDS
Physical attack
ANN
Lightweight
Intrusion detection
09.- Desarrollar infraestructuras resilientes, promover la industrialización inclusiva y sostenible, y fomentar la innovación
topic WSN
Blockchain
IDS
Physical attack
ANN
Lightweight
Intrusion detection
09.- Desarrollar infraestructuras resilientes, promover la industrialización inclusiva y sostenible, y fomentar la innovación
description [EN] Wireless sensor networks (WSNs) are vulnerable to physical attacks in which adversaries gain partial or full control of sensor nodes, compromising the integrity of the network. Conventional security mechanisms impose excessive computational overhead and are not well suited to resource-constrained WSN devices. This paper proposes a lightweight, two-layer intrusion detection system (IDS) that integrates blockchain (BC) technology with machine learning for physical attack detection in WSNs. The first layer employs a lightweight BC protocol among cluster heads (CHs) and the base station (BS) to detect data integrity violations through hash-based consensus. The second layer applies an artificial neural network (ANN) at the base station to detect attacks that bypass blockchain verification, without imposing any processing load on sensor nodes. Simulation experiments on a 100-node WSN demonstrate that the combined system achieves 97.42% accuracy and 98.35% recall, outperforming five established classifiers and both standalone components. The system sustains detection rates above 99.98% under 30 simultaneous attackers and maintains reliable operation under packet loss conditions up to 10%.
publishDate 2026
dc.date.none.fl_str_mv 2026
2026-03-20
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/10251/236093
url https://riunet.upv.es/handle/10251/236093
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 15,811543

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