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dc.contributor.authorErdem, Hamit
dc.contributor.authorOzgur, Atilla
dc.date.accessioned2019-05-25T13:24:50Z
dc.date.available2019-05-25T13:24:50Z
dc.date.issued2018
dc.identifier.issn1300-1884
dc.identifier.urihttps://dergipark.org.tr/download/article-file/442691
dc.identifier.urihttp://hdl.handle.net/11727/3298
dc.description.abstractWith the improvements in information systems, intrusion detection systems (IDS) become more important. IDS can be thought as a classification problem. An important step of classification applications is feature selection step. Nowadays, to improve accuracy of classifiers, it is recommended to use classifier fusion instead of single classifiers. This study proposes to use genetic algorithms for both feature selection and weight selection for classifier fusion in IDS. This proposed system called GA-NS-AB, has been applied to NSL-KDD dataset. Number of classifiers used in fusion changes between 2 and 8. Following classifiers have been used: Adaboost, Decision Tree, Logistic Regression, Naive Bayes, Random Forests, Gradient Boosting, K-Nearest Neighbor, and Neural Networks Multi-Layer Perceptron. The results of the proposed method have been compared with simple voting and probability voting fusion methods and single classifiers. In addition, GA-NS-AB is also compared with previous results. GA-NS-AB is a high accuracy classifier fusion that reduces test and training time.en_US
dc.language.isoturen_US
dc.relation.isversionof10.17341/gazimmfd.406781en_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectFeature selectionen_US
dc.subjectClassifier fusionen_US
dc.subjectGenetic algorithmsen_US
dc.subjectIntrusion detection systemsen_US
dc.subjectMachine learningen_US
dc.titleFeature selection and multiple classifier fusion using genetic algorithms in intrusion detection systemsen_US
dc.typearticleen_US
dc.relation.journalJOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GAZI UNIVERSITYen_US
dc.identifier.volume33en_US
dc.identifier.issue1en_US
dc.identifier.startpage75en_US
dc.identifier.endpage87en_US
dc.identifier.wos000427552600007


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