Machine Learning Models Stacking in the Malicious Links Detecting

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dc.contributor.authorKhukalenko, Yevhenii
dc.contributor.authorStopochkina, Iryna
dc.contributor.authorIlin, Mykola
dc.date.accessioned2023-11-22T10:43:44Z
dc.date.available2023-11-22T10:43:44Z
dc.date.issued2023
dc.description.abstractAn analysis of the performance of various classifiers on address and network groups of features was performed. A new classification model is proposed, which is a stacking of 3 models: kNN, XGBoost and Transformer. The best model for stacking was experimentally determined: Logistic Regression, which made it possible to improve the result of the best available model by 3%. The hypothesis that stacking a larger number of worse models has an advantage over stacking a smaller number of more productive models on the used data set was confirmed: regardless of the choice of stacking metaalgorithm, stacking of three models showed better results than stacking two.uk
dc.format.pagerangePp. 67-79uk
dc.identifier.citationKhukalenko, Ye. Machine Learning Models Stacking in the Malicious Links Detecting / Yevhenii Khukalenko, Iryna Stopochkina, Mykola Ilin // Theoretical and Applied Cybersecurity : scientific journal. – 2023. – Vol. 5, Iss. 1. – Pp. 67–79. – Bibliogr. 33 ref.uk
dc.identifier.doihttps://doi.org/10.20535/tacs.2664-29132023.1.287752
dc.identifier.urihttps://ela.kpi.ua/handle/123456789/62372
dc.language.isoenuk
dc.publisherIgor Sikorsky Kyiv Polytechnic Instituteuk
dc.publisher.placeKyivuk
dc.relation.ispartofTheoretical and Applied Cybersecurity: scientific journal, Vol. 5, No. 1uk
dc.subjectMalicious URLuk
dc.subjectMachine Learninguk
dc.subjectStackinguk
dc.subject.udc004.056:004.89uk
dc.titleMachine Learning Models Stacking in the Malicious Links Detectinguk
dc.typeArticleuk

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Theoretical and Applied Cybersecurity: scientific journal, Vol. 5, No. 1