A multi-objective mutation-based dynamic Harris Hawks optimization for botnet detection in IoT

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dc.authoridArasteh, Bahman/0000-0001-5202-6315
dc.authoridSoleimanian Gharehchopogh, Farhad/0000-0003-1588-1659
dc.authorwosidArasteh, Bahman/AAN-9555-2021
dc.contributor.authorGharehchopogh, Farhad Soleimanian
dc.contributor.authorAbdollahzadeh, Benyamin
dc.contributor.authorBarshandeh, Saeid
dc.contributor.authorArasteh, Bahman
dc.date.accessioned2024-05-19T14:42:45Z
dc.date.available2024-05-19T14:42:45Z
dc.date.issued2023
dc.departmentİstinye Üniversitesien_US
dc.description.abstractThe increasing trend toward using the Internet of Things (IoT) increased the number of intrusions and intruders annually. Hence, the integration, confidentiality, and access to digital resources would be threatened continually. The significance of security implementation in digital platforms and the need to design defensive systems to discover different intrusions made the researchers study updated and effective methods, such as Botnet Detection for IoT systems. Many problem space features and network behavior unpredictability made the Intrusion Detection System (IDS) the main problem in maintaining computer networks' security. Furthermore, many insignificant features have turned the feature selection (FS) problem into a vast IDS aspect. This paper introduces a novel binary multi-objective dynamic Harris Hawks Optimization (HHO) enhanced with mutation operator (MODHHO) and applies it to Botnet Detection in IoT. Afterward, the Feature Selection (FS) is undertaken, and the K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Multilayer Perceptron (MLP), and Decision Tree (DT) classifiers are used to estimate the potential of the selected features in the precise detection of intrusions. The simulation results illustrated that the MODHHO algorithm performs well in Botnet Detection in IoT and is preferred to other approaches in its performance metrics. Besides, the computational complexity analysis results suggest that the MODHHO algorithm's overhead is more optimal than similar approaches. The MODHHO algorithm has performed better in comparison with other compared algorithms in all 5 data sets. In contrast with the machine learning methods of the proposed model in all five data sets, it has had a better error rate according to the AUC, G-mean, and TPR criteria. And according to the comparison made with filter-based methods, it has performed almost better in three datasets.en_US
dc.identifier.doi10.1016/j.iot.2023.100952
dc.identifier.issn2543-1536
dc.identifier.issn2542-6605
dc.identifier.scopus2-s2.0-85173546137en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org10.1016/j.iot.2023.100952
dc.identifier.urihttps://hdl.handle.net/20.500.12713/5281
dc.identifier.volume24en_US
dc.identifier.wosWOS:001088335200001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofInternet of Thingsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240519_kaen_US
dc.subjectInternet Of Thingsen_US
dc.subjectFeature Selectionen_US
dc.subjectMulti -Objective Optimizationen_US
dc.subjectBotnet Detectionen_US
dc.subjectHarris Hawks Optimizationen_US
dc.titleA multi-objective mutation-based dynamic Harris Hawks optimization for botnet detection in IoTen_US
dc.typeArticleen_US

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