INTRUSION DETECTION SYSTEM (IDS) DEVELOPMENT USING TREE-BASED MACHINE LEARNING ALGORITHMS

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dc.contributor.authorWitcha Chimphlee
dc.contributor.authorSiriporn Chimphlee
dc.date.accessioned2025-03-10T07:34:45Z
dc.date.available2025-03-10T07:34:45Z
dc.date.issued2023
dc.description.abstractThe paper proposes a two-phase classification method for detecting anomalies in network traffic, aiming to tackle the challenges of imbalance and feature selection. The study uses Information Gain to select relevant features and evaluates its performance on the CICIDS-2018 dataset with various classifiers. Results indicate that the ensemble classifier achieved the highest accuracy, precision, and recall. The proposed method addresses challenges in intrusion detection and highlights the effectiveness of ensemble classifiers in improving anomaly detection accuracy. Also, the quantity of pertinent characteristics chosen by Information Gain has a considerable impact on the F1-score and detection accuracy. Specifically, the Ensemble Learning achieved the highest accuracy of 98.36% and F1-score of 97.98% using the relevant selected features. © (2023), (Academy and Industry Research Collaboration Center (AIRCC)). All Rights Reserved.
dc.identifier.citationInternational Journal of Computer Networks and Communications
dc.identifier.doi10.5121/ijcnc.2023.15406
dc.identifier.issn9752293
dc.identifier.scopus2-s2.0-85169072179
dc.identifier.urihttps://repository.dusit.ac.th//handle/123456789/4567
dc.languageEnglish
dc.publisherAcademy and Industry Research Collaboration Center (AIRCC)
dc.rightsAll Open Access; Gold Open Access
dc.rights.holderScopus
dc.subjectAnomaly Detection
dc.subjectCICIDS-2018 dataset
dc.subjectFeature Selection
dc.subjectImbalance Data
dc.subjectIntrusion Detection System
dc.titleINTRUSION DETECTION SYSTEM (IDS) DEVELOPMENT USING TREE-BASED MACHINE LEARNING ALGORITHMS
dc.typeArticle
mods.location.urlhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85169072179&doi=10.5121%2fijcnc.2023.15406&partnerID=40&md5=011afd36f7146a0688524489a1300cc6
oaire.citation.endPage109
oaire.citation.issue4
oaire.citation.startPage93
oaire.citation.volume15
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