Anomaly-Based Intrusion Detection From Network Flow Features Using Variational Autoencoder

Basit Öğe Kaydı
dc.authoridZavrak/0000-0001-6950-8927
dc.authorwosidZavrak/C-6702-2014
dc.authorwosidISKEFIYELI, Murat/AAC-3406-2021
dc.contributor.authorZavrak, Sultan
dc.contributor.authorIskefiyeli, Murat
dc.date.accessioned2021-12-01T18:48:12Z
dc.date.available2021-12-01T18:48:12Z
dc.date.issued2020
dc.department[Belirlenecek]en_US
dc.description.abstractThe rapid increase in network traffic has recently led to the importance of flow-based intrusion detection systems processing a small amount of traffic data. Furthermore, anomaly-based methods, which can identify unknown attacks are also integrated into these systems. In this study, the focus is concentrated on the detection of anomalous network traffic (or intrusions) from flow-based data using unsupervised deep learning methods with semi-supervised learning approach. More specifically, Autoencoder and Variational Autoencoder methods were employed to identify unknown attacks using flow features. In the experiments carried out, the flow-based features extracted out of network traffic data, including typical and different types of attacks, were used. The Receiver Operating Characteristics (ROC) and the area under ROC curve, resulting from these methods were calculated and compared with One-Class Support Vector Machine. The ROC curves were examined in detail to analyze the performance of the methods in various threshold values. The experimental results show that Variational Autoencoder performs, for the most part, better than Autoencoder and One-Class Support Vector Machine.en_US
dc.description.sponsorshipScientific and Technological Research Council of Turkey (TUBITAK)Turkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) [2211]en_US
dc.description.sponsorshipThe work of Sultan Zavrak was supported by the Scienti~c and Technological Research Council of Turkey (TUBITAK) through the 2211/C Ph.D. Scholarship Programme for Priority Areas.en_US
dc.identifier.doi10.1109/ACCESS.2020.3001350
dc.identifier.endpage108358en_US
dc.identifier.issn2169-3536
dc.identifier.scopus2-s2.0-85086985318en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage108346en_US
dc.identifier.urihttps://doi.org/10.1109/ACCESS.2020.3001350
dc.identifier.urihttps://hdl.handle.net/20.500.12684/10478
dc.identifier.volume8en_US
dc.identifier.wosWOS:000544044400003en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherIeee-Inst Electrical Electronics Engineers Incen_US
dc.relation.ispartofIeee Accessen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectIntrusion detectionen_US
dc.subjectFeature extractionen_US
dc.subjectTelecommunication trafficen_US
dc.subjectDeep learningen_US
dc.subjectSupport vector machinesen_US
dc.subjectAnomaly detectionen_US
dc.subjectComputer hackingen_US
dc.subjectFlow anomaly detectionen_US
dc.subjectintrusion detectionen_US
dc.subjectdeep learningen_US
dc.subjectvariational autoencoderen_US
dc.subjectsemi-supervised learningen_US
dc.subjectDetection Systemen_US
dc.titleAnomaly-Based Intrusion Detection From Network Flow Features Using Variational Autoencoderen_US
dc.typeArticleen_US

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