Minimizing false positive rate for DoS attack detection: A hybrid SDN-based approach
dc.contributor.author | Latah, Majd | |
dc.contributor.author | Toker, L. | |
dc.date.accessioned | 2020-11-30T09:55:48Z | |
dc.date.available | 2020-11-30T09:55:48Z | |
dc.date.issued | 2020-06 | |
dc.identifier.issn | 2405-9595 | en_US |
dc.identifier.uri | http://hdl.handle.net/10679/7154 | |
dc.identifier.uri | https://www.sciencedirect.com/science/article/pii/S2405959519303480 | |
dc.description.abstract | Denial 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.iso | eng | en_US |
dc.publisher | Elsevier | en_US |
dc.relation.ispartof | ICT Express | |
dc.rights | openAccess | |
dc.title | Minimizing false positive rate for DoS attack detection: A hybrid SDN-based approach | en_US |
dc.type | Article | en_US |
dc.description.version | Publisher version | en_US |
dc.peerreviewed | yes | en_US |
dc.publicationstatus | Published | en_US |
dc.contributor.department | Özyeğin University | |
dc.identifier.volume | 6 | en_US |
dc.identifier.issue | 2 | en_US |
dc.identifier.startpage | 125 | en_US |
dc.identifier.endpage | 127 | en_US |
dc.identifier.wos | WOS:000537706700012 | |
dc.identifier.doi | 10.1016/j.icte.2019.11.002 | en_US |
dc.subject.keywords | Security | en_US |
dc.subject.keywords | Software defined networking (SDN) | en_US |
dc.subject.keywords | Intrusion detection | en_US |
dc.subject.keywords | Machine learning | en_US |
dc.identifier.scopus | SCOPUS:2-s2.0-85076236215 | |
dc.contributor.ozugradstudent | Latah, Majd | |
dc.contributor.authorMale | 1 | |
dc.relation.publicationcategory | Article - International Refereed Journal - Institution PhD Student |
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