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dc.contributor.authorCoşkun, Koraty
dc.contributor.authorÇetin, Gürcan
dc.date.accessioned2022-06-22T12:44:32Z
dc.date.available2022-06-22T12:44:32Z
dc.date.issued2022en_US
dc.identifier.citationÇoşkun K., Çetin G., “A Comparative Evaluation of The Boosting Algorithms For Network Attack Classification” Int. J. of 3D Printing Tech. Dig. Ind., 6(1): 102-112, (2022).en_US
dc.identifier.issn2602-3350
dc.identifier.urihttps://doi.org/10.46519/ij3dptdi.1030539
dc.identifier.urihttps://hdl.handle.net/20.500.12809/10049
dc.description.abstractThe security of information resources is an extremely critical problem. The network infrastructure that enables internet access, in particular, may be targeted by attackers from a variety of national and international locations, resulting in losses for institutions that utilize it. Anomaly detection systems, sometimes called Intrusion Detection Systems (IDSs), are designed to identify abnormalities in such networks. The success of IDSs, however, is limited by the algorithms and learning capacity used in the background. Because of the complex behavior of malicious entities, it is critical to adopt effective techniques that assure high performance while being time efficient. The success rate of the boosting algorithms in identifying malicious network traffic was studied in this study. The boosting approach, one of the most used Ensemble Learning techniques, is accepted as a way to cope with this challenge. In this work, Google Colab has been used to model well-known boosting algorithms. The AdaBoost, CatBoost, GradientBoost, LightGBM, and XGBoost models have been applied to the CICID2017 dataset. The performance of the classifiers has been evaluated with accuracy, precision, recall, f1-score, kappa value, ROC curve and AUC. As a result of the investigation, it was discovered that the XGBoost algorithm produced the greatest results in terms of f1-score, with 99.89 percent, and the AUC values were extremely near to 1, with 0.9989. LightGBM and GradientBoost models, on the other hand, have been shown to be less effective in detecting attack types with little data.en_US
dc.item-language.isoengen_US
dc.publisherKerim ÇETİNKAYAen_US
dc.relation.isversionof10.46519/ij3dptdi.1030539en_US
dc.item-rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectBoosting Algorithmsen_US
dc.subjectEnsemble Learningen_US
dc.subjectIntrusion Detection Systemsen_US
dc.subjectNetwork Attacksen_US
dc.titleA COMPARATIVE EVALUATION OF THE BOOSTING ALGORITHMS FOR NETWORK ATTACK CLASSIFICATIONen_US
dc.item-typearticleen_US
dc.contributor.departmentMÜ, Teknoloji Fakültesi, Bilişim Sistemleri Mühendisliği Bölümüen_US
dc.contributor.authorID0000-0003-3186-2781en_US
dc.contributor.authorID0000-0001-7859-0547en_US
dc.contributor.institutionauthorCoşkun, Koray
dc.contributor.institutionauthorÇetin, Gürcan
dc.identifier.volume6en_US
dc.identifier.issue1en_US
dc.identifier.startpage102en_US
dc.identifier.endpage112en_US
dc.relation.journalInternational Journal of 3D Printing Technologies and Digital Industryen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US


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