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Engineering the application of machine learning in an IDS based on IoT traffic flow

2023-02Journal article Open access
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dc.contributor.authorPrazeres, Nuno
dc.contributor.authorCosta, Rogério Luís de C.
dc.contributor.authorSantos, Leonel
dc.contributor.authorRabadão, Carlos
dc.date.accessioned2023-02-01T11:54:51Z
dc.date.available2023-02-01T11:54:51Z
dc.date.issued2023-02
dc.date.updated2023-01-31T15:08:18Z
dc.description.abstractInternet of Things (IoT) devices are now widely used, enabling intelligent services that, in association with new communication technologies like the 5G and broadband internet, boost smart-city environments. Despite their limited resources, IoT devices collect and share large amounts of data and are connected to the internet, becoming an attractive target for malicious actors. This work uses machine learning combined with an Intrusion Detection System (IDS) to detect possible attacks. Due to the limitations of IoT devices and low latency services, the IDS must have a specialized architecture. Furthermore, although machine learning-based solutions have high potential, there are still challenges related to training and generalization, which may impose constraints on the architecture. Our proposal is an IDS with a distributed architecture that relies on Fog computing to run specialized modules and use deep neural networks to identify malicious traffic inside IoT data flows. We compare our IoT-Flow IDS with three other architectures. We assess model generalization using test data from different datasets and evaluate their performance in terms of Recall, Precision, and F1-Score. Results confirm the feasibility of flowbased anomaly detection and the importance of network traffic segmentation and specialized models in the AI-based IDS for IoT.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1016/j.iswa.2023.200189pt_PT
dc.identifier.issn2667-3053
dc.identifier.slugcv-prod-3131101
dc.identifier.urihttp://hdl.handle.net/10400.8/8089
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.relationCEECINST/00051/2018pt_PT
dc.relationResearch Center in Informatics and Communications
dc.subjectIntrusion detection systemspt_PT
dc.subjectInternet of thingspt_PT
dc.subjectMachine learningpt_PT
dc.subjectSmart citypt_PT
dc.subjectCybersecuritypt_PT
dc.titleEngineering the application of machine learning in an IDS based on IoT traffic flowpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleResearch Center in Informatics and Communications
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04524%2F2020/PT
oaire.citation.startPage200189pt_PT
oaire.citation.titleIntelligent Systems with Applicationspt_PT
oaire.citation.volume17pt_PT
oaire.fundingStream6817 - DCRRNI ID
person.familyNameGonçalves dos Prazeres
person.familyNamede Carvalho Costa
person.familyNameSimões Santos
person.familyNameRabadão
person.givenNameNuno Alexandre
person.givenNameRogério Luís
person.givenNameLeonel Filipe
person.givenNameCarlos
person.identifier.ciencia-id7717-9573-0C0F
person.identifier.ciencia-idC212-374B-3FF1
person.identifier.ciencia-id2C1C-E900-6A57
person.identifier.orcid0000-0003-1760-6220
person.identifier.orcid0000-0003-2306-7585
person.identifier.orcid0000-0002-6883-7996
person.identifier.orcid0000-0001-7332-4397
person.identifier.ridA-7940-2016
person.identifier.ridM-3235-2013
person.identifier.scopus-author-id7801604983
person.identifier.scopus-author-id57203544345
person.identifier.scopus-author-id22433497800
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.cv.cienciaid7717-9573-0C0F | Rogério Luís de Carvalho Costa
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublicationcb0ac77a-dc59-4d22-a29a-48558f53dc33
relation.isAuthorOfPublication5654d934-3fa0-4afb-9b3b-f2736104924c
relation.isAuthorOfPublication68de522f-fc54-440b-83c2-7374dc26f0b3
relation.isAuthorOfPublication99f438ca-9099-4e7e-91ea-1a5cbab7a1ab
relation.isAuthorOfPublication.latestForDiscovery68de522f-fc54-440b-83c2-7374dc26f0b3
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