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Assessing machine learning techniques for intrusion detection in cyber-physical systems

2023-08-18Research article Open access
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authorProfile.emailbiblioteca@isel.pt
datacite.subject.fosEngenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática
dc.contributor.authorSantos, Vinicius F.
dc.contributor.authorAlbuquerque, Célio
dc.contributor.authorPassos, Diego
dc.contributor.authorEreno Quincozes, Silvio
dc.contributor.authorMossé, Daniel
dc.date.accessioned2025-09-19T10:46:14Z
dc.date.available2025-09-19T10:46:14Z
dc.date.issued2023-08-18
dc.description.abstractCyber-physical systems (CPS) are vital to key infrastructures such as Smart Grids and water treatment, and are increasingly vulnerable to a broad spectrum of evolving attacks. Whereas traditional security mechanisms, such as encryption and firewalls, are often inadequate for CPS architectures, the implementation of Intrusion Detection Systems (IDS) tailored for CPS has become an essential strategy for securing them. In this context, it is worth noting the difference between traditional offline Machine Learning (ML) techniques and understanding how they perform under different IDS applications. To answer these questions, this article presents a novel comparison of five offline and three online ML algorithms for intrusion detection using seven CPS-specific datasets, revealing that offline ML is superior when attack signatures are present without time constraints, while online techniques offer a quicker response to new attacks. The findings provide a pathway for enhancing CPS security through a balanced and effective combination of ML techniques.eng
dc.identifier.citationSantos, V. F., Albuquerque, C., Passos, D., Quincozes, S. E., & Mossé, D. (2023). Assessing machine learning techniques for intrusion detection in cyber-physical systems. Energies, 16(16), 1-18. https://doi.org/10.3390/en16166058
dc.identifier.doihttps://doi.org/10.3390/en16166058
dc.identifier.eissn1996-1073
dc.identifier.urihttp://hdl.handle.net/10400.21/22148
dc.language.isoeng
dc.peerreviewedyes
dc.publisherMDPI
dc.relation.hasversionhttps://www.mdpi.com/1996-1073/16/16/6058
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectCyber-physical systems
dc.subjectIntrusion detection systems
dc.subjectOffline machine learning
dc.subjectOnline machine learning
dc.titleAssessing machine learning techniques for intrusion detection in cyber-physical systemseng
dc.typeresearch article
dspace.entity.typePublication
oaire.citation.endPage18
oaire.citation.issue16
oaire.citation.startPage1
oaire.citation.titleEnergies
oaire.citation.volume16
oaire.versionhttp://purl.org/coar/version/c_be7fb7dd8ff6fe43
person.familyNameAlbuquerque
person.familyNamePassos
person.familyNameEreno Quincozes
person.givenNameCélio
person.givenNameDiego
person.givenNameSilvio
person.identifier.orcid0000-0002-7959-6569
person.identifier.orcid0000-0002-9707-1176
person.identifier.orcid0000-0001-6793-4033
person.identifier.ridO-4975-2015
person.identifier.ridACS-4328-2022
person.identifier.scopus-author-id15836551100
person.identifier.scopus-author-id24478915900
person.identifier.scopus-author-id57191616602
relation.isAuthorOfPublication7123bd56-5154-4351-bc80-e5c8bbacf15a
relation.isAuthorOfPublication1baae68b-74ca-4d47-9c93-d990147ada03
relation.isAuthorOfPublicationbd52eb9a-ae55-4389-8950-934670c79c03
relation.isAuthorOfPublication.latestForDiscovery7123bd56-5154-4351-bc80-e5c8bbacf15a

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