Intrusion Detection System (IDS) of multiclassification IoT by using pipelining and an efficient machine learning

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dc.contributor.authorHazim, Layth Rafea
dc.contributor.authorJasim, Abdulrahman Ahmed
dc.contributor.authorAta, Oğuz
dc.contributor.authorIlyas, Muhammad
dc.date.accessioned2024-07-21T07:51:25Z
dc.date.available2024-07-21T07:51:25Z
dc.date.issued2023en_US
dc.departmentEnstitüler, Lisansüstü Eğitim Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalıen_US
dc.description.abstractThe Internet of Things (IoT) has quickly advanced and been incorporated into many different fields. With the use of IoT technology, gadgets can receive, process, and send data automatically. IoT has been rapidly accepted in many important fields since it makes life easier and increases service quality, yet it still faces significant privacy and security problems. An Intrusion Detection System (IDS) could be implemented as a security feature to protect IoT networks from a variety of cyberattacks. This study suggests using IDS to defend against a wide range of cyberattacks on IoT systems. The suggested approach makes use of the Multi-layer Perceptron (MLP) as well as Extra Trees (ExT) as efficient algorithms of classification. Also, the study uses the pipeline to put together several cross-validated phases while selecting various parameters to increase the detection rate. One dataset is utilized for evaluating and analyzing the performance outcomes so as to validate the efficiency of the suggested IDS approach. The evaluation findings show that the suggested IDS methods may greatly increase detection performance results concerning accuracy rate, precision, F1-score, and recall while also improving detection efficiency.en_US
dc.identifier.citationHazim, L. R., Jasim, A. A., Ata, O., Ilyas, M. (2023). Intrusion Detection System (IDS) of multiclassification IoT by using pipelining and an efficient machine learning. 9th International Conference on Engineering and Emerging Technology, ICEET 2023. 10.1109/ICEET60227.2023.10525915en_US
dc.identifier.isbn9798350316926
dc.identifier.scopus2-s2.0-85194070171
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://hdl.handle.net/20.500.12939/4772
dc.indekslendigikaynakScopus
dc.institutionauthorJasim, Abdulrahman Ahmed
dc.institutionauthorAta, Oğuz
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartof9th International Conference on Engineering and Emerging Technology, ICEET 2023
dc.relation.isversionof10.1109/ICEET60227.2023.10525915en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - İdari Personel ve Öğrencien_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectCybersecurityen_US
dc.subjectInternet of Things (IoT)en_US
dc.subjectIntrusion detection system (IDS)en_US
dc.subjectMachine learningen_US
dc.subjectNetwork securityen_US
dc.subjectPipelineen_US
dc.titleIntrusion Detection System (IDS) of multiclassification IoT by using pipelining and an efficient machine learning
dc.typeConference Object

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