Hyperparameters optimization XGBoost for network intrusion detection using CSE-CIC-IDS 2018 dataset

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dc.contributor.authorWitcha Chimphlee
dc.contributor.authorSiriporn Chimphlee
dc.contributor.correspondenceS. Chimphlee; Department of Data Science and Analytics, Faculty of Science and Technology, Suan Dusit University, Bangkok, Thailand; email: siriporn.chi@gmail.com
dc.date.accessioned2025-03-10T07:34:21Z
dc.date.available2025-03-10T07:34:21Z
dc.date.issued2024
dc.description.abstractWith the introduction of high-speed internet access, the demand for security and dependable networks has grown. In recent years, network attacks have gotten more complex and intense, making security a vital component of organizational information systems. Network intrusion detection systems (NIDS) have become an essential detection technology to protect data integrity and system availability against such attacks. NIDS is one of the most well-known areas of machine learning software in the security field, with machine learni ng algorithms constantly being developed to improve performance. This research focuses on detecting abnormalities in societal infiltration using the hyperparameters optimization XGBoost (HO-XGB) algorithm with the Communications Security Establishment-The Canadian Institute for Cybersecurity-Intrusion Detection System2018 (CSE-CIC-IDS2018) dataset to get the best potential results. When compared to typical machine learning methods published in the literature, HO-XGB outperforms them. The study shows that XGBoost outperforms other detection algorithms. We refined the HO-XGB model's hyperparameters, which included learning_rate, subsample, max_leaves, max_depth, gamma, colsample_bytree, min_child_weight, n_estimators, max_depth, and reg_alpha. The experimental findings reveal that HO-XGB1 outperforms multiple parameter settings for intrusion detection, effectively optimizing XGBoost's hyperparameters.
dc.identifier.citationIAES International Journal of Artificial Intelligence
dc.identifier.doi10.11591/ijai.v13.i1.pp817-826
dc.identifier.issn20894872
dc.identifier.scopus2-s2.0-85185470900
dc.identifier.urihttps://repository.dusit.ac.th//handle/123456789/4490
dc.languageEnglish
dc.publisherInstitute of Advanced Engineering and Science
dc.rightsAll Open Access; Gold Open Access
dc.rights.holderScopus
dc.subjectExtreme gradient boosting
dc.subjectHyperparameters
dc.subjectMachine learning
dc.subjectNetwork intrusion detection
dc.subjectXGBoost
dc.titleHyperparameters optimization XGBoost for network intrusion detection using CSE-CIC-IDS 2018 dataset
dc.typeArticle
mods.location.urlhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85185470900&doi=10.11591%2fijai.v13.i1.pp817-826&partnerID=40&md5=9692ba311624d60380fa0bb7c3c100eb
oaire.citation.endPage826
oaire.citation.issue1
oaire.citation.startPage817
oaire.citation.volume13
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