Evaluation DDoS attack detection through the application of machine learning techniques on the CICIDS2017 dataset in the field of information security

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dc.contributor.authorAhmed, Ali Saadoon
dc.contributor.authorKurnaz, Sefer
dc.contributor.authorKhaleel, Arshad M.
dc.date.accessioned2023-10-04T13:48:57Z
dc.date.available2023-10-04T13:48:57Z
dc.date.issued2023en_US
dc.departmentFakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.description.abstractAmongst network and Intrusion Detection System (IDS) threats, Distributed Denial of Service (DDoS) attacks often take precedence due to their significant potential to disrupt services, leading to financial and reputational damages for organizations. This study employs eight advanced machine learning techniques to distinguish between two types of DDoS attacks: DoS Hulk and DoS Slow HTTP Test. The applied algorithms include Decision Tree (DT), Random Forest (RF), Gradient Boosting (GB), AdaBoost, Naive Bayes (NB), Extreme Gradient Boosting (XGB), Ridge regression, and Multilayer Perceptron (MLP). Utilizing a Python environment, these methods were applied to the DDoS attacks in the CICIDS2017 dataset for classification into benign or DoS categories across two distinct experiments. The results were highly encouraging: The first experiment achieved an accuracy rate exceeding 99%, while the second experiment achieved a perfect success rate of 100%. These findings outperform those of previous studies in terms of their efficiency, demonstrating the potential of these machine learning techniques in enhancing DDoS attack detection.en_US
dc.identifier.citationAhmed, A. S., Kurnaz, S., & Khaleel, A. M. (2023). Evaluation DDoS attack detection through the application of machine learning techniques on the CICIDS2017 dataset in the field of information security. Mathematical Modelling of Engineering Problems, 10(4), 1125-1134.en_US
dc.identifier.endpage1134en_US
dc.identifier.issn2369-0739
dc.identifier.issue4en_US
dc.identifier.scopus2-s2.0-85171644963
dc.identifier.scopusqualityQ3
dc.identifier.startpage1125en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12939/4071
dc.identifier.volume10en_US
dc.indekslendigikaynakScopus
dc.institutionauthorKurnaz, Sefer
dc.language.isoen
dc.publisherInternational Information and Engineering Technology Associationen_US
dc.relation.ispartofMathematical Modelling of Engineering Problems
dc.relation.isversionof10.18280/mmep.100404en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectAlgorithmsen_US
dc.subjectDoS attacksen_US
dc.subjectIDS threatsen_US
dc.subjectPython environmenten_US
dc.titleEvaluation DDoS attack detection through the application of machine learning techniques on the CICIDS2017 dataset in the field of information security
dc.typeArticle

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