Empirical study on multiclass classification-based network intrusion detection

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dc.contributor.authorElmasry, Wisam
dc.contributor.authorAkbulut, Akhan
dc.contributor.authorZaim, Abdül Halim
dc.date.accessioned2020-11-21T15:53:31Z
dc.date.available2020-11-21T15:53:31Z
dc.date.issued2019en_US
dc.departmentİstanbul Ticaret Üniversitesien_US
dc.description.abstractEarly and effective network intrusion detection is deemed to be a critical basis for cybersecurity domain. In the past decade, although a significant amount of work has focused on network intrusion detection, it is still a challenge to establish an intrusion detection system with a high detection rate and a relatively low false alarm rate. In this paper, we have performed a comprehensive empirical study on network intrusion detection as a multiclass classification task, not just to detect a suspicious connection but also to assign the correct type as well. To surpass the previous studies, we have utilized four deep learning models, namely, deep neural networks, long short-term memory recurrent neural networks, gated recurrent unit recurrent neural networks, and deep belief networks. Our approach relies on the pretraining of the models by exploiting a particle swarm optimization–based algorithm for their hyperparameters selection. In order to investigate the performance differences, we also included two well-known shallow learning methods, namely, decision forest and decision jungle. Furthermore, we used in our experiments four datasets, which are dedicated to intrusion detection systems to explore various environments. These datasets are KDD CUP 99, NSL-KDD, CIDDS, and CICIDS2017. Moreover, 22 evaluation metrics are used to assess the model's performance in each of the datasets. Finally, intensive quantitative, Friedman test, and ranking methods analyses of our results are provided at the end of this paper. The results show a significant improvement in the detection of network attacks with our recommended approach. © 2019 Wiley Periodicals, Inc.en_US
dc.identifier.doi10.1111/coin.12220en_US
dc.identifier.endpage954en_US
dc.identifier.issn0824-7935
dc.identifier.issue4en_US
dc.identifier.scopus2-s2.0-85067394449en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage919en_US
dc.identifier.urihttps://doi.org/10.1111/coin.12220
dc.identifier.urihttps://hdl.handle.net/11467/3609
dc.identifier.volume35en_US
dc.identifier.wosWOS:000492750300010en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherBlackwell Publishing Inc.en_US
dc.relation.ispartofComputational Intelligenceen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectcyber securityen_US
dc.subjectdeep learningen_US
dc.subjectnetwork intrusion detectionen_US
dc.subjectparticle swarm optimizationen_US
dc.titleEmpirical study on multiclass classification-based network intrusion detectionen_US
dc.typeArticleen_US

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