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A Comparative Analysis of Machine Learning Techniques for IoT Intrusion Detection

2022Book part Open access
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dc.contributor.authorVitorino, João
dc.contributor.authorAndrade, Rui
dc.contributor.authorPraça, Isabel
dc.contributor.authorSousa, Orlando Jorge Coelho Moura
dc.contributor.authorMaia, Eva
dc.date.accessioned2023-09-05T11:18:43Z
dc.date.available2023-09-05T11:18:43Z
dc.date.issued2022
dc.description.abstractThe digital transformation faces tremendous security challenges. In particular, the growing number of cyber-attacks targeting Internet of Things (IoT) systems restates the need for a reliable detection of malicious network activity. This paper presents a comparative analysis of supervised, unsupervised and reinforcement learning techniques on nine malware captures of the IoT-23 dataset, considering both binary and multi-class classification scenarios. The developed models consisted of Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Isolation Forest (iForest), Local Outlier Factor (LOF) and a Deep Reinforcement Learning (DRL) model based on a Double Deep Q-Network (DDQIN), adapted to the intrusion detection context. The most reliable performance was achieved by LightGBM. Nonetheless, iForest displayed good anomaly detection results and the DRL model demonstrated the possible benefits of employing this methodology to continuously improve the detection. Overall, the obtained results indicate that the analyzed techniques are well suited for IoT intrusion detection.pt_PT
dc.description.sponsorshipThe present work was done and funded in the scope of the European Union’s Horizon 2020 research and innovation program, under project SeCoIIA (grant agreement no. 871967). This work has also received funding from UIDP/00760/2020.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1007/978-3-031-08147-7_13pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.22/23451
dc.language.isoengpt_PT
dc.publisherSpringerpt_PT
dc.relationSecure Collaborative Intelligent Industrial Assets
dc.relationResearch Group on Intelligent Engineering and Computing for Advanced Innovation and Development
dc.relation.ispartofseriesLecture Notes in Computer Science (LNCS);volume 13291
dc.relation.publisherversionhttps://link.springer.com/chapter/10.1007/978-3-031-08147-7_13pt_PT
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/pt_PT
dc.subjectInternet of Thingspt_PT
dc.subjectIntrusion detectionpt_PT
dc.subjectSupervised learningpt_PT
dc.subjectUnsupervised learningpt_PT
dc.subjectReinforcement learningpt_PT
dc.titleA Comparative Analysis of Machine Learning Techniques for IoT Intrusion Detectionpt_PT
dc.typebook part
dspace.entity.typePublication
oaire.awardTitleSecure Collaborative Intelligent Industrial Assets
oaire.awardTitleResearch Group on Intelligent Engineering and Computing for Advanced Innovation and Development
oaire.awardURIinfo:eu-repo/grantAgreement/EC/H2020/871967/EU
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F00760%2F2020/PT
oaire.citation.endPage207pt_PT
oaire.citation.startPage191pt_PT
oaire.citation.titleInternational Symposium on Foundations and Practice of Security - FPS 2021pt_PT
oaire.citation.volume13291pt_PT
oaire.fundingStreamH2020
oaire.fundingStream6817 - DCRRNI ID
person.familyNameMachado Vitorino
person.familyNameAndrade
person.familyNamePraça
person.familyNameCoelho Moura Sousa
person.familyNameMaia
person.givenNameJoão Pedro
person.givenNameRui
person.givenNameIsabel
person.givenNameOrlando Jorge
person.givenNameEva
person.identifierHlqCxhoAAAAJ
person.identifier1408593
person.identifier299522
person.identifier.ciencia-id3312-592F-B628
person.identifier.ciencia-id751C-7ECE-59F0
person.identifier.ciencia-idC710-4218-1BFF
person.identifier.ciencia-idA110-F659-8B0A
person.identifier.ciencia-id4F14-EF83-C4B9
person.identifier.orcid0000-0002-4968-3653
person.identifier.orcid0000-0003-2356-3706
person.identifier.orcid0000-0002-2519-9859
person.identifier.orcid0000-0003-0779-3480
person.identifier.orcid0000-0002-8075-531X
person.identifier.ridK-8430-2014
person.identifier.scopus-author-id57579914600
person.identifier.scopus-author-id22734900800
project.funder.identifierhttp://doi.org/10.13039/501100008530
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameEuropean Commission
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typebookPartpt_PT
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