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dc.contributor.authorTÜRK, Fuat
dc.date.accessioned2024-04-18T10:58:29Z
dc.date.available2024-04-18T10:58:29Z
dc.date.issued2023
dc.identifier.issn2147-3188
dc.identifier.urihttp://dspace.beu.edu.tr:8080/xmlui/handle/123456789/14825
dc.description.abstractThe use of intelligent devices in almost every sector, and the provision of services by private and public institutions through network servers, cloud technologies, and database systems are now mostly remotely controlled. Due to the increasing demands on network systems, unfortunately, both malicious software and users are showing more interest in these areas. Some organizations are facing almost hundreds or even thousands of network attacks daily. Therefore, it is not enough to solve the attacks with a virus program or a firewall. Detection and accurate analysis of network attacks are crucial for the operation of the entire system. With the use of deep learning and machine learning, attack detection, and classification can be successfully performed. This study conducted a comprehensive attack detection process on the UNSW-NB15 and NSL-KDD datasets using existing machine learning and deep learning algorithms. In the UNSW-NB15 dataset, an accuracy of 98.6% and 98.3% was achieved for two-class and multi-class classification, respectively, and 97.8% and 93.4% accuracy were obtained in the NSL-KDD dataset. The results prove that machine learning algorithms are an effective solution for intrusion detection systems.tr_TR
dc.language.isoEnglishtr_TR
dc.publisherBitlis Eren Üniversitesitr_TR
dc.rightsinfo:eu-repo/semantics/openAccesstr_TR
dc.subjectInstruction Detection Systemstr_TR
dc.subjectNetwork Attackstr_TR
dc.subjectNSL-KDD Datasettr_TR
dc.subjectUNSW-NB15 Datasettr_TR
dc.titleAnalysis of Intrusion Detection Systems in UNSW-NB15 and NSL-KDD Datasets with Machine Learning Algorithmstr_TR
dc.typeArticletr_TR
dc.identifier.issue2tr_TR
dc.identifier.startpage465tr_TR
dc.identifier.endpage477tr_TR
dc.relation.journalBitlis Eren Üniversitesi Fen Bilimleri Dergisitr_TR
dc.identifier.volume12tr_TR


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