Optimizing Intrusion Detection Systems in Three Phases on the CSE-CIC-IDS-2018 Dataset

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dc.contributor.authorSurasit Songma
dc.contributor.authorTheera Sathuphan
dc.contributor.authorThanakorn Pamutha
dc.contributor.correspondenceS. Songma; Department of Information Technology, Faculty of Science and Technology, Suan Dusit University, Bangkok, 10300, Thailand; email: surasit_son@dusit.ac.th
dc.date.accessioned2025-03-10T07:34:44Z
dc.date.available2025-03-10T07:34:44Z
dc.date.issued2023
dc.description.abstractThis article examines intrusion detection systems in depth using the CSE-CIC-IDS-2018 dataset. The investigation is divided into three stages: to begin, data cleaning, exploratory data analysis, and data normalization procedures (min-max and Z-score) are used to prepare data for use with various classifiers; second, in order to improve processing speed and reduce model complexity, a combination of principal component analysis (PCA) and random forest (RF) is used to reduce non-significant features by comparing them to the full dataset; finally, machine learning methods (XGBoost, CART, DT, KNN, MLP, RF, LR, and Bayes) are applied to specific features and preprocessing procedures, with the XGBoost, DT, and RF models outperforming the others in terms of both ROC values and CPU runtime. The evaluation concludes with the discovery of an optimal set, which includes PCA and RF feature selection. © 2023 by the authors.
dc.identifier.citationComputers
dc.identifier.doi10.3390/computers12120245
dc.identifier.issn2073431X
dc.identifier.scopus2-s2.0-85180676522
dc.identifier.urihttps://repository.dusit.ac.th//handle/123456789/4520
dc.languageEnglish
dc.publisherMultidisciplinary Digital Publishing Institute (MDPI)
dc.rightsAll Open Access; Gold Open Access; Green Open Access
dc.rights.holderScopus
dc.subjectCSE-CIC-IDS-2018 dataset
dc.subjectexploratory data analysis
dc.subjectfeature selection
dc.subjectintrusion detection system
dc.subjectmachine learning techniques
dc.subjectperformance evaluation
dc.subjectthree-phase models
dc.titleOptimizing Intrusion Detection Systems in Three Phases on the CSE-CIC-IDS-2018 Dataset
dc.typeArticle
mods.location.urlhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85180676522&doi=10.3390%2fcomputers12120245&partnerID=40&md5=16460691ef29d75d49e8b6627351e4db
oaire.citation.issue12
oaire.citation.volume12
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