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dc.contributor.authorÖsken, Sinem
dc.contributor.authorYıldırım, Ecen Nur
dc.contributor.authorKarataş, Gözde
dc.contributor.authorCuhacı, Levent
dc.date.accessioned2020-02-18T08:56:10Z
dc.date.available2020-02-18T08:56:10Z
dc.date.issued2019
dc.identifier.isbn978-1-7281-1013-4
dc.identifier.urihttps://hdl.handle.net/11413/6235
dc.description.abstractIn this study, a systematic mapping study was conducted to systematically evaluate publications on Intrusion Detection Systems with Deep Learning. 6088 papers have been examined by using systematic mapping method to evaluate the publications related to this paper, which have been used increasingly in the Intrusion Detection Systems. The goal of our study is to determine which deep learning algorithms were used mostly in the algortihms, which criteria were taken into account for selecting the preferred deep learning algorithm, and the most searched topics of intrusion detection with deep learning algorithm model. Scientific studies published in the last 10 years have been studied in the IEEE Explorer, ACM Digital Library, Science Direct, Scopus and Wiley databases.
dc.language.isoen_UStr_TR
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/us/*
dc.subjectDeep Learning
dc.subjectIntrusion Detection
dc.subjectSystematic Mapping Study
dc.subjectDerin Öğrenme
dc.subjectİzinsiz Giriş Tespiti
dc.subjectSistematik Harita Çalışması
dc.titleIntrusion detection systems with deep learning: A systematic mapping study
dc.typeconferenceObjecttr_TR
dc.relation.journal2019 Scientific Meeting on Electrial-Electronics & Biomedical Engineering and Computer Science (EBBT)tr_TR


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Attribution-NonCommercial-NoDerivs 3.0 United States
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 United States