Comparison of Multiple Feature Selection Techniques for Machine Learning-Based Detection of IoT Attacks

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dc.contributor.authorPhan, Viet Anhcs
dc.contributor.authorJeřábek, Jancs
dc.contributor.authorMalina, Lukášcs
dc.date.accessioned2025-02-18T11:36:13Z
dc.date.available2025-02-18T11:36:13Z
dc.date.issued2024-07-30cs
dc.description.abstractThe Internet of Things (IoT) has become increasingly practical in applications such as smart homes, autonomous vehicles, and environmental monitoring. However, this rapid expansion has led to significant cybersecurity threats. Detecting these threats is critical, and while machine learning techniques are valuable, they struggle with high-dimensional data. Feature selection helps by reducing computational costs while maintaining model generalization. Selecting the most effective feature selection method is a crucial task. This research addresses this gap by testing five feature selection methods: Random Forest (RF), Recursive Feature Elimination (RFE), Logistic Regression (LR), XGBoost Regression (XGBoost), and Information Gain (IG) using the CIC-IoT 2023 dataset. It evaluates these methods when being used with five machine learning models: Decision Tree (DT), Random Forest (RF), k-Nearest Neighbors (k-NN), Gradient Boosting (GB), and Multi-layer Perceptron (MLP) using metrics like accuracy, precision, recall, and F1-score across three datasets. The results show that RFE, especially with the RF model, achieves the highest accuracy (99.57%) with 30 features. RF is the most stable, with accuracy from 83% to 99.56%. Additionally, the 5-feature scheme is best for implementing IDS on resource-limited IoT devices, with RFE paired with the k-NN model being the optimal combination.en
dc.formattextcs
dc.format.extent1-10cs
dc.format.mimetypeapplication/pdfcs
dc.identifier.citationARES '24: Proceedings of the 19th International Conference on Availability, Reliability and Security. 2024, p. 1-10.en
dc.identifier.doi10.1145/3664476.3670440cs
dc.identifier.isbn979-8-4007-1718-5cs
dc.identifier.orcid0009-0003-1787-8063cs
dc.identifier.orcid0000-0001-9487-5024cs
dc.identifier.orcid0000-0002-7208-2514cs
dc.identifier.other189196cs
dc.identifier.researcheridJMC-4821-2023cs
dc.identifier.researcheridE-3929-2018cs
dc.identifier.researcheridE-2174-2018cs
dc.identifier.scopus59245308100cs
dc.identifier.scopus23011945600cs
dc.identifier.scopus49863792100cs
dc.identifier.urihttps://hdl.handle.net/11012/250054
dc.language.isoencs
dc.publisherAssociation for Computing Machinerycs
dc.relation.ispartofARES '24: Proceedings of the 19th International Conference on Availability, Reliability and Securitycs
dc.relation.urihttps://dl.acm.org/doi/10.1145/3664476.3670440cs
dc.rightsCreative Commons Attribution 4.0 Internationalcs
dc.rights.accessopenAccesscs
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/cs
dc.subjectIoTen
dc.subjectAnomaly Detectionen
dc.subjectIDSen
dc.subjectMachine Learningen
dc.subjectFeature Selectionen
dc.titleComparison of Multiple Feature Selection Techniques for Machine Learning-Based Detection of IoT Attacksen
dc.type.driverconferenceObjecten
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
eprints.grantNumberinfo:eu-repo/grantAgreement/MV0/VK/VK01030019cs
sync.item.dbidVAV-189196en
sync.item.dbtypeVAVen
sync.item.insts2025.02.18 12:36:13en
sync.item.modts2025.02.13 15:32:04en
thesis.grantorVysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií. Ústav telekomunikacícs
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