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Distributed machine learning and multi-agent systems for enhanced attack detection and resilience in iot networks

2025Comunicação em conferência Acesso aberto
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datacite.subject.fosEngenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática
datacite.subject.sdg09:Indústria, Inovação e Infraestruturas
dc.contributor.authorFunchal, Gustavo Silva
dc.contributor.authorPedrosa, Tiago
dc.contributor.authorPrieta, Fernando de la
dc.contributor.authorLeitão, Paulo
dc.date.accessioned2026-04-10T13:49:28Z
dc.date.available2026-04-10T13:49:28Z
dc.date.issued2025
dc.description.abstractThe exponential growth of connected devices, including sensors, mobile devices, and various Internet of Things (IoT) devices, has resulted in a substantial increase in data generation. Traditionally, data analysis involves transferring data to cloud computing systems, leading to latency issues and excessive network traffic. Edge computing emerges as a promising solution by bringing processing closer to the data sources. However, edge computing faces challenges, particularly in terms of limited computational power, which can create constraints in the execution of machine learning (ML) tasks. This paper aims to analyze strategies for distributing ML tasks among multiple nodes based on multi-agent systems (MAS) technology to have a collaborative approach and compare these strategies to provide an overview of best practices for achieving the optimal performance in intrusion detection for Industrial Internet of Things (IIoT). In this way, the well-known CICIoT2023 data set was used, and centralized and distributed ML techniques were implemented, and evaluated. The distributed edge ML approach achieved promising results, presenting an improvement of between 7.73% and 32.18% in the correction of wrong predictions of detection of attacks on IoT devices, significantly improving the precision and recall of the applied techniques.eng
dc.description.sponsorshipThis work has been supported by national funds through FCT/MCTES (PIDDAC): CeDRI, UIDB/05757/2020 (DOI: 10.54499/UIDB/05757/2 020) and UIDP/05757/2020 (DOI: 10.54499/UIDP/ 05757/2020); and SusTEC, LA/P/0007/2020 (DOI: 10.54499/LA/P/0007/2020). The author Gustavo Funchal thanks the FCT Portugal for the PhD Grant 2022.13712.BD.
dc.identifier.citationFunchal, Gustavo Silva; Pedrosa, Tiago; Prieta, Fernando de la; Leitão Paulo (2025). Distributed machine learning and multi-agent systems for enhanced attack detection and resilience in iot networks. In the 11th International Conference on Information Systems Security and Privacy. Porto, Portugal. 2, p. 192-203. ISSN 2184-4356. DOI: 10.5220/0013154400003899
dc.identifier.doi10.5220/0013154400003899
dc.identifier.issn2184-4356
dc.identifier.urihttp://hdl.handle.net/10198/36502
dc.language.isoeng
dc.peerreviewedyes
dc.publisherSciTePress
dc.relationResearch Centre in Digitalization and Intelligent Robotics
dc.relationAssociate Laboratory for Sustainability and Tecnology in Mountain Regions
dc.relation.hasversionhttps://www.scitepress.org/publishedPapers/2025/131544/pdf/index.html
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectIntrusion Detection Systems
dc.subjectMulti-Agent Systems
dc.subjectInternet of Things
dc.subjectMachine Learning
dc.titleDistributed machine learning and multi-agent systems for enhanced attack detection and resilience in iot networkseng
dc.typeconference paper
dspace.entity.typePublication
oaire.awardNumberUIDB/05757/2020
oaire.awardNumberLA/P/0007/2020
oaire.awardTitleResearch Centre in Digitalization and Intelligent Robotics
oaire.awardTitleAssociate Laboratory for Sustainability and Tecnology in Mountain Regions
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F05757%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/LA%2FP%2F0007%2F2020/PT
oaire.citation.conferenceDate2025
oaire.citation.conferencePlacePorto, Portugal
oaire.citation.endPage203
oaire.citation.startPage192
oaire.citation.title11th International Conference on Information Systems Security and Privacy
oaire.citation.volume2
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameFunchal
person.familyNamePedrosa
person.familyNameLeitão
person.givenNameGustavo Silva
person.givenNameTiago
person.givenNamePaulo
person.identifierhttps://scholar.google.com/citations?user=eegfgI4AAAAJ&hl=pt-PT&oi=ao
person.identifierA-8390-2011
person.identifier.ciencia-id9416-F3F1-B3EF
person.identifier.ciencia-idB81E-0583-AEDF
person.identifier.ciencia-id8316-8F13-DA71
person.identifier.orcid0000-0002-9691-9956
person.identifier.orcid0000-0003-4873-2705
person.identifier.orcid0000-0002-2151-7944
person.identifier.ridG-2249-2011
person.identifier.scopus-author-id57216637887
person.identifier.scopus-author-id35318153700
person.identifier.scopus-author-id35584388900
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
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