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dc.contributor.authorLatah, Majd
dc.contributor.authorToker, L.
dc.date.accessioned2018-10-08T08:34:43Z
dc.date.available2018-10-08T08:34:43Z
dc.date.issued2018-08-24
dc.identifier.issn2047-4954
dc.identifier.urihttp://hdl.handle.net/10679/5992
dc.identifier.urihttps://ieeexplore.ieee.org/document/8519833
dc.description.abstractSoftware-defined networking (SDN) is a new paradigm that allows developing more flexible network applications. A SDN controller, which represents a centralised controlling point, is responsible for running various network applications as well as maintaining different network services and functionalities. Choosing an efficient intrusion detection system helps in reducing the overhead of the running controller and creates a more secure network. In this study, we investigate the performance of the well-known anomaly-based intrusion detection approaches in terms of accuracy, false alarm rate, precision, recall, f1-measure, area under receiver operator characteristic curve, execution time and McNemar's test. Precisely, the authors focus on supervised machine-learning approaches where we use the following classifiers: decision trees, extreme learning machine, Naive Bayes, linear discriminant analysis, neural networks, support vector machines, random forest, K-nearest-neighbour, AdaBoost, RUSBoost, LogitBoost and BaggingTrees where we employ the well-known NSL-KDD benchmark dataset to compare the performance of each one of these classifiers.en_US
dc.language.isoengen_US
dc.publisherIET Networksen_US
dc.relation.ispartofIET Networksen_US
dc.rightsrestrictedAccess
dc.titleTowards an efficient anomaly-based intrusion detection for software-defined networksen_US
dc.typeArticleen_US
dc.peerreviewedyesen_US
dc.publicationstatusPublisheden_US
dc.contributor.departmentÖzyeğin University
dc.identifier.startpage1
dc.identifier.startpage453
dc.identifier.endpage7
dc.identifier.endpage459
dc.identifier.wosWOS:000448943800013
dc.identifier.doi10.1049/iet-net.2018.5080en_US
dc.subject.keywordsSoftware defined networkingen_US
dc.subject.keywordsNetwork securityen_US
dc.subject.keywordsArtificial intelligenceen_US
dc.identifier.scopusSCOPUS:2-s2.0-85055954593
dc.contributor.ozugradstudentLatah, Majd
dc.contributor.authorMale1


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