Detection of Intrusions with Machine Learning Methods

dc.authoridALBAYRAK, AHMET/0000-0002-2166-1102
dc.contributor.authorBostancı, Beyzanur
dc.contributor.authorAlbayrak, Ahmet
dc.date.accessioned2023-07-26T11:57:43Z
dc.date.available2023-07-26T11:57:43Z
dc.date.issued2021
dc.departmentDÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.description2nd International Informatics and Software Engineering Conference (IISEC) - Artificial Intelligence for Digital Transformation -- DEC 16-17, 2021 -- Ankara, TURKEYen_US
dc.description.abstractToday, especially with the emergence of social networks and IoT technologies, big data has entered the literature. With the development of technology, the size of the data has increased and accordingly data security gaps have emerged. In this study, Support Vector Machines and Random Forest algorithms, which are Supervised Machine Learning Algorithms, were used to analyze a data set consisting of unauthorized network logins. As a result of the experimental studies, it was observed that both algorithms produced good results, but the Random Forest approach produced better results.en_US
dc.description.sponsorshipIEEE Turkey Secten_US
dc.identifier.doi10.1109/IISEC54230.2021.9672361
dc.identifier.isbn978-1-6654-0759-5
dc.identifier.scopus2-s2.0-85125325170en_US
dc.identifier.urihttps://doi.org/10.1109/IISEC54230.2021.9672361
dc.identifier.urihttps://hdl.handle.net/20.500.12684/13282
dc.identifier.wosWOS:000841548300013en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorBostancı, Beyzanur
dc.institutionauthorAlbayrak, Ahmet
dc.language.isoenen_US
dc.publisherIeeeen_US
dc.relation.ispartof2nd International Informatics and Software Engineering Conference (Iisec)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz$2023V1Guncelleme$en_US
dc.subjectBig Data; Support Vector Machine; Random Forest; Machine Learningen_US
dc.subjectBig Dataen_US
dc.titleDetection of Intrusions with Machine Learning Methodsen_US
dc.typeConference Objecten_US

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