An Efficient Deep Learning-based Intrusion Detection System for Internet of Things Networks with Hybrid Feature Reduction and Data Balancing Techniques

Basit Öğe Kaydı
dc.authorscopusid57195222623en_US
dc.authorscopusid26421178600en_US
dc.authorscopusid15077642900en_US
dc.contributor.authorKaramollaoglu, Hamdullah
dc.contributor.authorDogru, Ibrahim Alper
dc.contributor.authorYucedag, Ibrahim
dc.date.accessioned2024-08-23T16:03:22Z
dc.date.available2024-08-23T16:03:22Z
dc.date.issued2024en_US
dc.departmentDüzce Üniversitesien_US
dc.description.abstractWith the increasing use of Internet of Things (IoT) technologies, cyber-attacks on IoT devices are also increasing day by day. Detecting attacks on IoT networks before they cause any damage is crucial for ensuring the security of the devices on these networks. In this study, a novel Intrusion Detection System (IDS) was developed for IoT networks. The IoTID20 and BoT-IoT datasets were utilized during the training phase and performance testing of the proposed IDS. A hybrid method combining the Principal Component Analysis (PCA) and the Bat Optimization (BAT) algorithm was proposed for dimensionality reduction on the datasets. The Synthetic Minority Over-Sampling Technique (SMOTE) was used to address the problem of data imbalance in the classes of the datasets. The Convolutional Neural Networks (CNN) model, a deep learning method, was employed for attack classification. The proposed IDS achieved an accuracy rate of 99.97% for the IoTID20 dataset and 99.98% for the BoT-IoT dataset in attack classification. Furthermore, detailed analyses were conducted to determine the effects of the dimensionality reduction and data balancing models on the classification performance of the proposed IDS.en_US
dc.identifier.doi10.5755/j01.itc.53.1.34933
dc.identifier.endpage261en_US
dc.identifier.issn1392-124X
dc.identifier.issue1en_US
dc.identifier.scopus2-s2.0-85188941129en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage243en_US
dc.identifier.urihttps://doi.org/10.5755/j01.itc.53.1.34933
dc.identifier.urihttps://hdl.handle.net/20.500.12684/13706
dc.identifier.volume53en_US
dc.identifier.wosWOS:001280512700016en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherKaunas Univ Technologyen_US
dc.relation.ispartofInformation Technology And Controlen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectIntrusion detection systemen_US
dc.subjectdeep learningen_US
dc.subjectIoT networksen_US
dc.subjectfeature reductionen_US
dc.subjectdata balancingen_US
dc.subjectBat Algorithmen_US
dc.subjectSmoteen_US
dc.subjectSecurityen_US
dc.subjectModelen_US
dc.titleAn Efficient Deep Learning-based Intrusion Detection System for Internet of Things Networks with Hybrid Feature Reduction and Data Balancing Techniquesen_US
dc.typeArticleen_US

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu