Cybersecurity for Internet of Things: Intrusion Detection with Machine Learning and Dimension Reduction

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dc.contributor.authorOzturk-Birim S.
dc.contributor.authorGunduz-Cure M.
dc.date.accessioned2024-07-22T08:03:16Z
dc.date.available2024-07-22T08:03:16Z
dc.date.issued2023
dc.description.abstractThe IoT connects an increasing number of devices to the Internet, simplifying lives but also exposing new vulnerabilities to cyberattacks. The intrusion detection system (IDS) helps detect and prevent attacks on IoT networks. This study aims to develop an intrusion detection and classification system using machine learning and dimension reduction techniques on two datasets. Performance metrics, dataset characteristics, and critical aspects are analyzed. The RF and XGB methods were used to classify attacks in Bot-IoT and Ton-IoT datasets, with and without dimension reduction. Precision, recall, and fl were used to measure classification performance. XGB outperformed in multiclass classification, while RFC excelled in binary classification. The use of PCA reduced computation time for XGB in binary classification but increased it for RFC. XGB showed decreased computation time and a slight performance impact in multiclass classification. © 2023 IEEE.
dc.identifier.DOI-ID10.1109/ASYU58738.2023.10296825
dc.identifier.urihttp://akademikarsiv.cbu.edu.tr:4000/handle/123456789/12190
dc.language.isoEnglish
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.subjectClassification (of information)
dc.subjectComputer crime
dc.subjectInternet of things
dc.subjectIntrusion detection
dc.subjectMachine learning
dc.subjectNetwork security
dc.subjectBinary classification
dc.subjectComputation time
dc.subjectDimension reduction
dc.subjectInternet of thing
dc.subjectIntrusion detection system
dc.subjectIntrusion Detection Systems
dc.subjectMachine-learning
dc.subjectMulti-class classification
dc.subjectPrincipal component analyse
dc.subjectPrincipal-component analysis
dc.subjectPrincipal component analysis
dc.titleCybersecurity for Internet of Things: Intrusion Detection with Machine Learning and Dimension Reduction
dc.typeConference paper

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