VEM-IDSs: enhancing an ensemble learning method of voting approach for binary classification of intrusion detection system

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dc.contributor.authorAwad, Omer Fawzi
dc.contributor.authorÇevik, Mesut
dc.contributor.authorFarhan, Hameed Mutlag
dc.contributor.authorKocakoyun Aydoğan, Şenay
dc.date.accessioned2026-04-27T10:54:26Z
dc.date.available2026-04-27T10:54:26Z
dc.date.issued2026
dc.departmentİstanbul Gedik Üniversitesi
dc.description2nd International Conference on Intelligent Systems, Blockchain, and Communication Technologies, ISBCom, 10-11 May 2025, Cairo.
dc.description.abstractIn the realm of cybersecurity, the responsibility for protecting information and data for the Internet of Things is significant, for the prevention of intrusiors from using the data by detecting the vulnerabilities, improve the mechanism, and developing strategies for protecting IoT networks to repel attacks. Intrusion Detection Systems (IDSs) are one of the strategies that can monitor the network to protect data. Machine Learning algorithms are used to increase IDS efficiency and achieve high security. Our study: conducts experiments using ensemble voting (Soft and Hard) classification to detect and classify data in IoT. Our experiments contain two stages. First, build models for three algorithms (K-Nearest Neighbor (KNN), Logistic Regression (LR), and Random Forest (RF). Second, building an ensemble voting classifier consisting of three algorithms. For the two stages, we proposed Edge-IIoTset dataset to classify attacks as benign or malicious, (RF_LR) models result of soft voting have a high accuracy.
dc.identifier.doi10.1007/978-3-032-15784-3_33
dc.identifier.endpage458
dc.identifier.isbn9783032157836
dc.identifier.issn2367-3370
dc.identifier.scopus2-s2.0-105035615448
dc.identifier.scopusqualityQ4
dc.identifier.startpage445
dc.identifier.urihttps://doi.org/10.1007/978-3-032-15784-3_33
dc.identifier.urihttps://hdl.handle.net/11501/2694
dc.identifier.volume1801 LNNS
dc.indekslendigikaynakScopus
dc.institutionauthorKocakoyun Aydoğan, Şenay
dc.language.isoen
dc.publisherSpringer Science and Business Media Deutschland GmbH
dc.relation.ispartof2nd International Conference on Intelligent Systems, Blockchain, and Communication Technologies, ISBCom
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectBenign
dc.subjectEnsemble Voting
dc.subjectIntrusion Detection System
dc.subjectIoT
dc.titleVEM-IDSs: enhancing an ensemble learning method of voting approach for binary classification of intrusion detection system
dc.typeConference Object

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