A comparison of neural network approaches for network intrusion detection

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dc.authorscopusid57207472273
dc.authorscopusid57192819774
dc.contributor.authorOney, Mehmet Uğur
dc.contributor.authorPeker, Serhat
dc.date.accessioned2022-02-15T16:58:13Z
dc.date.available2022-02-15T16:58:13Z
dc.date.issued2020
dc.departmentBakırçay Üniversitesien_US
dc.descriptionInternational Conference on Artificial Intelligence and Applied Mathematics in Engineering (ICAIAME) -- APR 20-22, 2019 -- Antalya, TURKEYen_US
dc.description.abstractNowadays, network intrusion detection is an important area of research in computer network security, and the use of artificial neural networks (ANNs) have become increasingly popular in this field. Despite this, the research concerning comparison of artificial neural network architectures in the network intrusion detection is a relatively insufficient. To make up for this lack, this study aims to examine the neural network architectures in network intrusion detection to determine which architecture performs best, and to examine the effects of the architectural components, such as optimization functions, activation functions, learning momentum on the performance. For this purpose, 6480 neural networks were generated, their performances were evaluated by conducting a series of experiments on KDD99 dataset, and the results were reported. This study will be a useful reference to researchers and practitioners hoping to use ANNs in network intrusion detection.en_US
dc.identifier.doi10.1007/978-3-030-36178-5_49
dc.identifier.endpage608en_US
dc.identifier.isbn978-3-030-36178-5
dc.identifier.isbn978-3-030-36177-8
dc.identifier.issn2367-4512
dc.identifier.scopus2-s2.0-85083459839en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage597en_US
dc.identifier.urihttps://doi.org/10.1007/978-3-030-36178-5_49
dc.identifier.urihttps://hdl.handle.net/20.500.14034/369
dc.identifier.volume43en_US
dc.identifier.wosWOS:000678771000049en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer International Publishing Agen_US
dc.relation.journalArtificial Intelligence And Applied Mathematics In Engineering Problemsen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectNetwork intrusion detectionen_US
dc.subjectData miningen_US
dc.subjectData classificationen_US
dc.subjectMachine learningen_US
dc.subjectANNsen_US
dc.subjectSvmen_US
dc.subjectAlgorithmen_US
dc.titleA comparison of neural network approaches for network intrusion detectionen_US
dc.typeConference Objecten_US

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