An intrusion detection system on the internet of things using deep learning and multi-objective enhanced gorilla troops optimizer

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dc.authorscopusidAli Ghaffari / 57197223215
dc.authorwosidAli Ghaffari / AAV-3651-2020
dc.contributor.authorAsgharzadeh, Hossein
dc.contributor.authorGhaffari, Ali
dc.contributor.authorMasdari, Mohammad
dc.contributor.authorGharehchopogh, Farhad Soleimanian
dc.date.accessioned2025-04-18T07:43:35Z
dc.date.available2025-04-18T07:43:35Z
dc.date.issued2024
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractIn recent years, developed Intrusion Detection Systems (IDSs) perform a vital function in improving security and anomaly detection. The effectiveness of deep learning-based methods has been proven in extracting better features and more accurate classification than other methods. In this paper, a feature extraction with convolutional neural network on Internet of Things (IoT) called FECNNIoT is designed and implemented to better detect anomalies on the IoT. Also, a binary multi-objective enhance of the Gorilla troops optimizer called BMEGTO is developed for effective feature selection. Finally, the combination of FECNNIoT and BMEGTO and KNN algorithm-based classification technique has led to the presentation of a hybrid method called CNN-BMEGTO-KNN. In the next step, the proposed model is implemented on two benchmark data sets, NSL-KDD and TON-IoT and tested regarding the accuracy, precision, recall, and F1-score criteria. The proposed CNN-BMEGTO-KNN model has reached 99.99% and 99.86% accuracy on TON-IoT and NSL-KDD datasets, respectively. In addition, the proposed BMEGTO method can identify about 27% and 25% of the effective features of the NSL-KDD and TON-IoT datasets, respectively.
dc.description.sponsorshipIstinye University
dc.identifier.citationAsgharzadeh, H., Ghaffari, A., Masdari, M., & Gharehchopogh, F. S. (2024). An Intrusion Detection System on The Internet of Things Using Deep Learning and Multi-objective Enhanced Gorilla Troops Optimizer. Journal of Bionic Engineering, 21(5), 2658-2684.
dc.identifier.doi10.1007/s42235-024-00575-7
dc.identifier.endpage2684
dc.identifier.issn1672-6529
dc.identifier.issn2543-2141
dc.identifier.issue5
dc.identifier.scopus2-s2.0-85197801400
dc.identifier.scopusqualityQ1
dc.identifier.startpage2658
dc.identifier.urihttp://dx.doi.org/10.1007/s42235-024-00575-7
dc.identifier.urihttps://hdl.handle.net/20.500.12713/6483
dc.identifier.volume21
dc.identifier.wosWOS:001264627100001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorGhaffari, Ali
dc.institutionauthoridAli Ghaffari /0000-0001-5407-8629
dc.language.isoen
dc.publisherSpringer
dc.relation.ispartofJournal of bionic engineering
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectIntrusion Detection
dc.subjectInternet of Things
dc.subjectConvolutional Neural Network
dc.subjectMulti-Objective
dc.subjectGorilla Troops Optimizer
dc.titleAn intrusion detection system on the internet of things using deep learning and multi-objective enhanced gorilla troops optimizer
dc.typeArticle

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