Yevhenii Khukalenko, Iryna Stopochkina, Mykola Ilin
{"title":"恶意链接检测中的机器学习模型叠加","authors":"Yevhenii Khukalenko, Iryna Stopochkina, Mykola Ilin","doi":"10.20535/tacs.2664-29132023.1.287752","DOIUrl":null,"url":null,"abstract":"
 
 
 An 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 meta-algorithm, stacking of three models showed better results than stacking two.
 
 
","PeriodicalId":471817,"journal":{"name":"Theoretical and applied cybersecurity","volume":"54 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Machine Learning Models Stacking in the Malicious Links Detecting\",\"authors\":\"Yevhenii Khukalenko, Iryna Stopochkina, Mykola Ilin\",\"doi\":\"10.20535/tacs.2664-29132023.1.287752\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"
 
 
 An 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 meta-algorithm, stacking of three models showed better results than stacking two.
 
 
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Machine Learning Models Stacking in the Malicious Links Detecting
An 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 meta-algorithm, stacking of three models showed better results than stacking two.