Detection DDOS Attacks Using Machine Learning Methods

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dc.authorid0000-0002-0233-064Xen_US
dc.contributor.authorAytaç, Tuğba
dc.contributor.authorAydın, Muhammed Ali
dc.contributor.authorZaim, Abdül Halim
dc.date.accessioned2020-10-28T08:04:02Z
dc.date.available2020-10-28T08:04:02Z
dc.date.issued2020en_US
dc.departmentFakülteler, Mühendislik ve Tasarım Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.description.abstractWishing to communicate with each other of people contributes to improving technology, and it has made the internet concept an indispensable part of our daily life. Cyber attacks from extranets to enterprise networks or intranets, which are used as personal, can cause pecuniary loss and intangible damage. It is critical to take due precautions for minimizing the losses by early detection of attacks. This study aims to analyze the rate of success in the intrusion detection system by using different methods. In this study, the CICDDoS2019 data set has been used, and DDOS attacks in this data set were compared. The success rates of threat determination were analyzed as using Artificial Neural Networks (ANN), Support Vector Machine (SVM), Gaussian Naive Bayes, Multinomial Naive Bayes, Bernoulli Naive Bayes, Logistic Regression, K-nearest neighbor (KNN), Decision Tree (entropy-gini) and Random Forest algorithms. It has been seen that the highest of the success rate is the models that ensure almost 100% success that was made by using K-nearest neighbor, Logistic Regression, Naive Bayes, (Multinomial – Bernoulli algorithms).en_US
dc.identifier.doi10.5152/electrica.2020.20049en_US
dc.identifier.endpage167en_US
dc.identifier.issue2en_US
dc.identifier.scopus2-s2.0-85100421067en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage159en_US
dc.identifier.trdizinid410319en_US
dc.identifier.urihttps://hdl.handle.net/11467/3472
dc.identifier.urihttps://doi.org/10.5152/electrica.2020.20049
dc.identifier.volume20en_US
dc.identifier.wosWOS:000562987500006en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakTR-Dizinen_US
dc.language.isoenen_US
dc.publisherİstanbul Üniversitesien_US
dc.relation.ispartofElectricaen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectCICDDoS2019en_US
dc.subjectintrusion detection systemen_US
dc.subjectmachine learning methodsen_US
dc.titleDetection DDOS Attacks Using Machine Learning Methodsen_US
dc.typeArticleen_US

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