Machine Learning Models Stacking in the Malicious Links Detecting

Khukalenko, YevheniiStopochkina, IrynaIlin, Mykola2023-11-222023-11-222023Khukalenko, 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.https://ela.kpi.ua/handle/123456789/62372An 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.enMalicious URLMachine LearningStackingMachine Learning Models Stacking in the Malicious Links DetectingArticlePp. 67-79https://doi.org/10.20535/tacs.2664-29132023.1.287752004.056:004.89