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XGB-RF: A Hybrid Machine Learning Approach for IoT Intrusion Detection

2022Journal article Open access
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dc.contributor.authorFaysal, Jabed Al
dc.contributor.authorMostafa, Sk Tahmid
dc.contributor.authorTamanna, Jannatul Sultana
dc.contributor.authorMumenin, Khondoker Mirazul
dc.contributor.authorArifin, Md. Mashrur
dc.contributor.authorAwal, Md. Abdul
dc.contributor.authorShome, Atanu
dc.contributor.authorMostafa, Sheikh Shanawaz
dc.date.accessioned2024-04-24T14:28:59Z
dc.date.available2024-04-24T14:28:59Z
dc.date.issued2022
dc.description.abstractIn the past few years, Internet of Things (IoT) devices have evolved faster and the use of these devices is exceedingly increasing to make our daily activities easier than ever. However, numerous security flaws persist on IoT devices due to the fact that the majority of them lack the memory and computing resources necessary for adequate security operations. As a result, IoT devices are affected by a variety of attacks. A single attack on network systems or devices can lead to significant damages in data security and privacy. However, machine-learning techniques can be applied to detect IoT attacks. In this paper, a hybrid machine learning scheme called XGB-RF is proposed for detecting intrusion attacks. The proposed hybrid method was applied to the N-BaIoT dataset containing hazardous botnet attacks. Random forest (RF) was used for the feature selection and eXtreme Gradient Boosting (XGB) classifier was used to detect different types of attacks on IoT environments. The performance of the proposed XGB-RF scheme is evaluated based on several evaluation metrics and demonstrates that the model successfully detects 99.94% of the attacks. After comparing it with state-of-the-art algorithms, our proposed model has achieved better performance for every metric. As the proposed scheme is capable of detecting botnet attacks effectively, it can significantly contribute to reducing the security concerns associated with IoT systems.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationFaysal, J.A.; Mostafa, S.T.; Tamanna, J.S.; Mumenin, K.M.; Arifin, M.M.; Awal, M.A.; Shome A.; Mostafa, S.S. XGB-RF: A Hybrid Machine Learning Approach for IoT Intrusion Detection. Telecom 2022, 3, 52–69. https://doi.org/10.3390/ telecom3010003pt_PT
dc.identifier.doi10.3390/telecom3010003pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.13/5647
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherMDPIpt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectIoT securitypt_PT
dc.subjectBotnet detectionpt_PT
dc.subjectRandom forestpt_PT
dc.subjectXGBpt_PT
dc.subjectFeature selectionpt_PT
dc.subjectMiraipt_PT
dc.subject.pt_PT
dc.subjectFaculdade de Ciências Exatas e da Engenhariapt_PT
dc.titleXGB-RF: A Hybrid Machine Learning Approach for IoT Intrusion Detectionpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage69pt_PT
oaire.citation.issue1pt_PT
oaire.citation.startPage52pt_PT
oaire.citation.titleTelecompt_PT
oaire.citation.volume3pt_PT
person.familyNameAWAL
person.familyNameMostafa
person.givenNameMD. ABDUL
person.givenNameSheikh Shanawaz
person.identifier34497
person.identifier.ciencia-idEE14-BEB3-F82B
person.identifier.orcid0000-0003-3028-4932
person.identifier.orcid0000-0002-7677-0971
person.identifier.ridN-9228-2015
person.identifier.scopus-author-id55489640900
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublicationf272eff0-b68c-477e-bc79-b267a3a8b181
relation.isAuthorOfPublicationf90aafd0-eedb-47ea-945a-40b1c1fe802a
relation.isAuthorOfPublication.latestForDiscoveryf90aafd0-eedb-47ea-945a-40b1c1fe802a

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