Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/12230
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dc.contributor.authorCaner, Serhaten_US
dc.contributor.authorErdoğmuş, Neslien_US
dc.contributor.authorErten, Yusuf Muraten_US
dc.date.accessioned2022-08-01T12:29:14Z-
dc.date.available2022-08-01T12:29:14Z-
dc.date.issued2022-
dc.identifier.urihttps://doi.org/10.3906/ELK-2104-50-
dc.identifier.urihttps://hdl.handle.net/11147/12230-
dc.identifier.urihttps://search.trdizin.gov.tr/yayin/detay/528806-
dc.description.abstractAn intrusion detection system is an automated monitoring tool that analyzes network traffic and detects malicious activities by looking out either for known patterns of attacks or for an anomaly. In this study, intrusion detection and classification performances of different deep learning based systems are examined. For this purpose, 24 deep neural networks with four different architectures are trained and evaluated on CICIDS2017 dataset. Furthermore, the best performing model is utilized to inspect raw network traffic features and rank them with respect to their contributions to success rates. By selecting features with respect to their ranks, sets of varying size from 3 to 77 are assessed in terms of classification accuracy and time efficiency. The results show that recurrent neural networks with a certain level of complexity can achieve comparable success rates with state-of-the-art systems using a small feature set of size 9; while the average time required to classify a test sample is halved compared to the complete set.en_US
dc.language.isoenen_US
dc.publisherTürkiye Kliniklerien_US
dc.relation.ispartofTurkish Journal of Electrical Engineering and Computer Sciencesen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectDeep learningen_US
dc.subjectFeature selectionen_US
dc.subjectNetwork intrusion detectionen_US
dc.subjectRecurrent Neural Networksen_US
dc.titlePerformance analysis and feature selection for network-based intrusion detection with deep learningen_US
dc.typeArticleen_US
dc.authorid0000-0003-1242-4487en_US
dc.authorid0000-0002-6875-2685en_US
dc.institutionauthorCaner, Serhaten_US
dc.institutionauthorErdoğmuş, Neslien_US
dc.departmentİzmir Institute of Technology. Computer Engineeringen_US
dc.identifier.wosWOS:000774599800011en_US
dc.identifier.scopus2-s2.0-85128265867en_US
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.doi10.3906/ELK-2104-50-
dc.contributor.affiliation01. Izmir Institute of Technologyen_US
dc.contributor.affiliation01. Izmir Institute of Technologyen_US
dc.contributor.affiliationİzmir Ekonomi Üniversitesien_US
dc.relation.issn1300-0632en_US
dc.description.volume30en_US
dc.description.issue3en_US
dc.description.startpage629en_US
dc.description.endpage643en_US
dc.identifier.trdizinid528806en_US
dc.identifier.scopusqualityQ3-
item.languageiso639-1en-
item.cerifentitytypePublications-
item.grantfulltextopen-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.openairetypeArticle-
item.fulltextWith Fulltext-
crisitem.author.dept01. Izmir Institute of Technology-
crisitem.author.dept03.04. Department of Computer Engineering-
crisitem.author.dept03.04. Department of Computer Engineering-
Appears in Collections:Computer Engineering / Bilgisayar Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
TR Dizin İndeksli Yayınlar / TR Dizin Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
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