Evaluating and enhancing intrusion detection systems in IoMT: the importance of domain-specific datasets

The emergence of the Internet of Medical Things (IoMT) is revolutionizing healthcare delivery, but also introducing critical challenges to cybersecurity and patient safety. Intrusion Detection Systems (IDSs) enhanced by Machine Learning (ML) have emerged as a powerful solution to identify cyberattac...

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Autores: Doménech Fons, Jordi|||0000-0002-2977-7265, León Abarca, Olga|||0000-0003-2869-051X, Siddiqui, Shuaib, Pegueroles Vallés, Josep R.|||0000-0002-5194-4883
Tipo de recurso: artículo
Fecha de publicación:2025
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/438778
Acceso en línea:https://hdl.handle.net/2117/438778
https://dx.doi.org/10.1016/j.iot.2025.101631
Access Level:acceso abierto
Palabra clave:Internet of Medical Things
Intrusion detection systems
Machine learning
Dataset Shift
Dataset optimization
CICIoMT2024
CICIoT2023
Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Telemàtica i xarxes d'ordinadors
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repository_id_str
spelling Evaluating and enhancing intrusion detection systems in IoMT: the importance of domain-specific datasetsDoménech Fons, Jordi|||0000-0002-2977-7265León Abarca, Olga|||0000-0003-2869-051XSiddiqui, ShuaibPegueroles Vallés, Josep R.|||0000-0002-5194-4883Internet of Medical ThingsIntrusion detection systemsMachine learningDataset ShiftDataset optimizationCICIoMT2024CICIoT2023Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Telemàtica i xarxes d'ordinadorsThe emergence of the Internet of Medical Things (IoMT) is revolutionizing healthcare delivery, but also introducing critical challenges to cybersecurity and patient safety. Intrusion Detection Systems (IDSs) enhanced by Machine Learning (ML) have emerged as a powerful solution to identify cyberattacks in these environments. However, existing studies often rely on general IoT datasets, potentially limiting their applicability in IoMT-specific scenarios. This study addresses these limitations by comparing the performance of ML models trained on a general IoT dataset (CICIoT2023) and an IoMT-specific dataset (CICIoMT2024) to demonstrate the importance of domain-specific data. Our findings reveal substantial drops of up to 66.87% in the F1-score when models trained on one dataset are tested on the other. Furthermore, the study critiques key dataset design choices in CICIoMT2024, and proposes baseline optimization techniques including uniform windowing, proper train-validation-test splits, adjustments in temporal dependencies for time series data, and improved dataset balancing. By applying these techniques, we observe significant improvements in IDS performance in comparison to other approaches, with scores of 0.9985 in model accuracy. The findings show the necessity of using IoMT-specific datasets and carefully designed preprocessing techniques to build robust IDSs tailored to the unique demands of medical IoT environments.This research was funded by the predoctoral program AGAUR-FI, Spain ajuts Joan Oró with grant number 2024 FI-1 00643 and by the Chair of Cybersecurity called CARISMATICA, Spain. This funding is backed by the Secretariat of Universities and Research of the Department of Research and Universities of the Generalitat of Catalonia, Spain, as well as the European Social Plus Fund and the funds from the Recovery, Transformation, and Resilience, financed by the European Union (Next Generation), under the auspices of the INCIBE. Moreover, the authors thank Oriol López Petit for advice on experimental design and statistical analysis.Peer ReviewedElsevier20252025-06-0120252025-07-15journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/438778https://dx.doi.org/10.1016/j.iot.2025.101631reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/4387782026-05-27T15:37:01Z
dc.title.none.fl_str_mv Evaluating and enhancing intrusion detection systems in IoMT: the importance of domain-specific datasets
title Evaluating and enhancing intrusion detection systems in IoMT: the importance of domain-specific datasets
spellingShingle Evaluating and enhancing intrusion detection systems in IoMT: the importance of domain-specific datasets
Doménech Fons, Jordi|||0000-0002-2977-7265
Internet of Medical Things
Intrusion detection systems
Machine learning
Dataset Shift
Dataset optimization
CICIoMT2024
CICIoT2023
Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Telemàtica i xarxes d'ordinadors
title_short Evaluating and enhancing intrusion detection systems in IoMT: the importance of domain-specific datasets
title_full Evaluating and enhancing intrusion detection systems in IoMT: the importance of domain-specific datasets
title_fullStr Evaluating and enhancing intrusion detection systems in IoMT: the importance of domain-specific datasets
title_full_unstemmed Evaluating and enhancing intrusion detection systems in IoMT: the importance of domain-specific datasets
title_sort Evaluating and enhancing intrusion detection systems in IoMT: the importance of domain-specific datasets
dc.creator.none.fl_str_mv Doménech Fons, Jordi|||0000-0002-2977-7265
León Abarca, Olga|||0000-0003-2869-051X
Siddiqui, Shuaib
Pegueroles Vallés, Josep R.|||0000-0002-5194-4883
author Doménech Fons, Jordi|||0000-0002-2977-7265
author_facet Doménech Fons, Jordi|||0000-0002-2977-7265
León Abarca, Olga|||0000-0003-2869-051X
Siddiqui, Shuaib
Pegueroles Vallés, Josep R.|||0000-0002-5194-4883
author_role author
author2 León Abarca, Olga|||0000-0003-2869-051X
Siddiqui, Shuaib
Pegueroles Vallés, Josep R.|||0000-0002-5194-4883
author2_role author
author
author
dc.subject.none.fl_str_mv Internet of Medical Things
Intrusion detection systems
Machine learning
Dataset Shift
Dataset optimization
CICIoMT2024
CICIoT2023
Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Telemàtica i xarxes d'ordinadors
topic Internet of Medical Things
Intrusion detection systems
Machine learning
Dataset Shift
Dataset optimization
CICIoMT2024
CICIoT2023
Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Telemàtica i xarxes d'ordinadors
description The emergence of the Internet of Medical Things (IoMT) is revolutionizing healthcare delivery, but also introducing critical challenges to cybersecurity and patient safety. Intrusion Detection Systems (IDSs) enhanced by Machine Learning (ML) have emerged as a powerful solution to identify cyberattacks in these environments. However, existing studies often rely on general IoT datasets, potentially limiting their applicability in IoMT-specific scenarios. This study addresses these limitations by comparing the performance of ML models trained on a general IoT dataset (CICIoT2023) and an IoMT-specific dataset (CICIoMT2024) to demonstrate the importance of domain-specific data. Our findings reveal substantial drops of up to 66.87% in the F1-score when models trained on one dataset are tested on the other. Furthermore, the study critiques key dataset design choices in CICIoMT2024, and proposes baseline optimization techniques including uniform windowing, proper train-validation-test splits, adjustments in temporal dependencies for time series data, and improved dataset balancing. By applying these techniques, we observe significant improvements in IDS performance in comparison to other approaches, with scores of 0.9985 in model accuracy. The findings show the necessity of using IoMT-specific datasets and carefully designed preprocessing techniques to build robust IDSs tailored to the unique demands of medical IoT environments.
publishDate 2025
dc.date.none.fl_str_mv 2025
2025-06-01
2025
2025-07-15
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://hdl.handle.net/2117/438778
https://dx.doi.org/10.1016/j.iot.2025.101631
url https://hdl.handle.net/2117/438778
https://dx.doi.org/10.1016/j.iot.2025.101631
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
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/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
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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