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dc.contributor.authorLatah, Majd
dc.contributor.authorToker, L.
dc.date.accessioned2020-11-30T09:55:48Z
dc.date.available2020-11-30T09:55:48Z
dc.date.issued2020-06
dc.identifier.issn2405-9595en_US
dc.identifier.urihttp://hdl.handle.net/10679/7154
dc.identifier.urihttps://www.sciencedirect.com/science/article/pii/S2405959519303480
dc.description.abstractDenial of Service attacks (DoS) are considered to be a major threat against today's communication networks. Recently, a novel networking paradigm that provides enhanced programming abilities has been proposed to attain an efficient control and management in future networks. In this work, we take the advantage of software-defined networking (SDN) to minimize the false positive rate of DoS attack detection systems. Our system combines flow-based and packet-based approaches to minimize the false positive rate (FPR). The experimental results conducted on NSL-KDD dataset have shown the effectiveness of our proposed approach, which successfully minimized the FPR as low as 0.3%. (C) 2020 The Korean Institute of Communications and Information Sciences (KICS).en_US
dc.language.isoengen_US
dc.publisherElsevieren_US
dc.relation.ispartofICT Express
dc.rightsopenAccess
dc.titleMinimizing false positive rate for DoS attack detection: A hybrid SDN-based approachen_US
dc.typeArticleen_US
dc.description.versionPublisher versionen_US
dc.peerreviewedyesen_US
dc.publicationstatusPublisheden_US
dc.contributor.departmentÖzyeğin University
dc.identifier.volume6en_US
dc.identifier.issue2en_US
dc.identifier.startpage125en_US
dc.identifier.endpage127en_US
dc.identifier.wosWOS:000537706700012
dc.identifier.doi10.1016/j.icte.2019.11.002en_US
dc.subject.keywordsSecurityen_US
dc.subject.keywordsSoftware defined networking (SDN)en_US
dc.subject.keywordsIntrusion detectionen_US
dc.subject.keywordsMachine learningen_US
dc.identifier.scopusSCOPUS:2-s2.0-85076236215
dc.contributor.ozugradstudentLatah, Majd
dc.contributor.authorMale1
dc.relation.publicationcategoryArticle - International Refereed Journal - Institution PhD Student


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