Publication:
A secure edge computing model using machine learning and IDS to detect and isolate intruders

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dc.citedby7
dc.contributor.authorMahadevappa P.en_US
dc.contributor.authorMurugesan R.K.en_US
dc.contributor.authorAl-amri R.en_US
dc.contributor.authorThabit R.en_US
dc.contributor.authorAl-Ghushami A.H.en_US
dc.contributor.authorAlkawsi G.en_US
dc.contributor.authorid57222119090en_US
dc.contributor.authorid57198406478en_US
dc.contributor.authorid57224896623en_US
dc.contributor.authorid58891173100en_US
dc.contributor.authorid57202984923en_US
dc.contributor.authorid57191982354en_US
dc.date.accessioned2025-03-03T07:43:49Z
dc.date.available2025-03-03T07:43:49Z
dc.date.issued2024
dc.description.abstractThe article presents a secure edge computing model that utilizes machine learning for intrusion detection and isolation. It addresses the security challenges arising from the rapid expansion of IoT and edge computing. The proposed Intrusion Detection System (IDS) combines Linear Discriminant Analysis (LDA) and Logistic Regression (LR) to swiftly and accurately identify intrusions without alerting neighboring devices. The model outperforms existing solutions with an accuracy of 96.56%, precision of 95.78%, and quick training time (0.04 s). It is effective against various types of attacks, enhancing the security of edge networks for IoT applications. ? The methodology employs a hybrid model that combines LDA and LR for intrusion detection. ? Machine learning techniques are used to analyze and identify intrusive activities during data acquisition by edge nodes. ? The methodology includes a mechanism to isolate suspected devices and data without notifying neighboring edge nodes to prevent intruders from gaining control over the edge network. ? 2024 The Author(s)en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo102597
dc.identifier.doi10.1016/j.mex.2024.102597
dc.identifier.scopus2-s2.0-85185300096
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85185300096&doi=10.1016%2fj.mex.2024.102597&partnerID=40&md5=c8231d5a145f9de7447796e6592ce70f
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/36674
dc.identifier.volume12
dc.publisherElsevier B.V.en_US
dc.relation.ispartofAll Open Access; Green Open Access
dc.sourceScopus
dc.sourcetitleMethodsX
dc.subjectadult
dc.subjectarticle
dc.subjectdiagnostic test accuracy study
dc.subjectdiscriminant analysis
dc.subjecthuman
dc.subjectlearning
dc.subjectlogistic regression analysis
dc.subjectmachine learning
dc.subjectmajor clinical study
dc.subjectmale
dc.titleA secure edge computing model using machine learning and IDS to detect and isolate intrudersen_US
dc.typeArticleen_US
dspace.entity.typePublication
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