Computational intelligence algorithms to handle dimensionality reduction for enhancing intrusion detection system

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dc.contributor.authorAlsaadi, Husam Ibrahiem
dc.contributor.authorAlmuttairi, Rafah M.
dc.contributor.authorBayat, Oğuz
dc.contributor.authorUçan, Osman Nuri
dc.date.accessioned2021-05-15T11:33:48Z
dc.date.available2021-05-15T11:33:48Z
dc.date.issued2020
dc.departmentMühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.description.abstractIn this paper, propose to use computational intelligence models to improve intrusion detection system, the computational intelligence algorithms are used as preprocessing steps for selecting most significant features from network data. Two computational intelligence algorithms, namely Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) are implemented to generate subset of relevant features. The computational intelligence approaches have been applied to optimize the classification of algorithms. The most significant features obtained from computational intelligence is fed into the classification algorithm. Novelty of this presents research of use computational intelligence algorithms namely Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) for handling dimensionality reduction. The dimensionality reduction is obstructed time processing of classification algorithms. Three classification algorithms namely K-Nearest Neighbors (KNN), Support Vector Machine (SVM) and Naive Bayes (NB) are implemented for intrusion detection system. Benchmark datasets, namely, KDD cup and NSL-KDD datasets are used to demonstrate and validate the performance of the proposed model for intrusion detection. From the empirical results, it is observed that the classification algorithm has improved the intrusion detection system with using computational intelligence algorithms. A comparative result analysis between the proposed model and different existing models is presented. It is concluded that the proposed model has outperformed of conventional models.en_US
dc.identifier.doi10.6688/JISE.202003_36(2).0009
dc.identifier.endpage308en_US
dc.identifier.issn1016-2364
dc.identifier.issue2en_US
dc.identifier.scopus2-s2.0-85082871954
dc.identifier.scopusqualityQ2
dc.identifier.startpage293en_US
dc.identifier.urihttps://doi.org/10.6688/JISE.202003_36(2).0009
dc.identifier.urihttps://hdl.handle.net/20.500.12939/230
dc.identifier.volume36en_US
dc.identifier.wosWOS:000523607200009
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorUçan, Osman Nuri
dc.institutionauthorBayat, Oğuz
dc.institutionauthorAlsaadi, Husam Ibrahiem
dc.language.isoen
dc.publisherInst Information Scienceen_US
dc.relation.ispartofJournal of Information Science and Engineering
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectComputational Intelligence Algorithmen_US
dc.subjectClassification Algorithmsen_US
dc.subjectIntrusion Detection Systemen_US
dc.subjectSupport Vector Machineen_US
dc.subjectK-Nearest Neighborsen_US
dc.titleComputational intelligence algorithms to handle dimensionality reduction for enhancing intrusion detection system
dc.typeArticle

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