Flow-based intrusion detection on software-defined networks: a multivariate time series anomaly detection approach

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dc.authoridZavrak, Sultan/0000-0001-6950-8927
dc.contributor.authorZavrak, Sultan
dc.contributor.authorIskefiyeli, Murat
dc.date.accessioned2023-07-26T11:58:19Z
dc.date.available2023-07-26T11:58:19Z
dc.date.issued2023
dc.departmentDÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.description.abstractIn this study, we present and implement the SAnDet (SDN anomaly detector) architecture, an anomaly-based intrusion detection system designed to take advantage of the capabilities offered by software-defined networking (SDN) architecture, as a controller application. The SAnDet system is composed of three modules: statistics collection, anomaly detection, and anomaly prevention. In particular, we utilize replicator neural networks (RNN), which is a specialized variant of the autoencoder, and the LSTM-based encoder-decoder (EncDecAD) method, which is a special type of long short-term memory (LSTM) network that has demonstrated a strong performance on data series particularly, to identify unknown attacks using flow features collected from OpenFlow switches. In our experiments, we utilize flow-based features extracted from network traffic data containing various types of attacks as input to our models in the form of time series. We evaluate the performance of our methods using the accuracy and area under the receiver operating characteristic curve (AUC) metrics. Our experimental results demonstrate that EncDecAD outperforms RNN and that our approach offers several benefits over previously conducted research.en_US
dc.description.sponsorshipScientific and Technological Research Council of Turkey (TUBITAK) [2211/C]en_US
dc.description.sponsorshipS. Zavrak is supported by The Scientific and Technological Research Council of Turkey (TUBITAK) under 2211/C Ph.D. Scholarship Programme for Priority Areas.en_US
dc.identifier.doi10.1007/s00521-023-08376-5
dc.identifier.issn0941-0643
dc.identifier.issn1433-3058
dc.identifier.scopus2-s2.0-85149292972en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1007/s00521-023-08376-5
dc.identifier.urihttps://hdl.handle.net/20.500.12684/13458
dc.identifier.wosWOS:000943603500001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorZavrak, Sultan
dc.language.isoenen_US
dc.publisherSpringer London Ltden_US
dc.relation.ispartofNeural Computing & Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmz$2023V1Guncelleme$en_US
dc.subjectIntrusion Detection; Anomaly Detection; Deep Learning; Semi-Supervised Learning; Software-Defined Networks; Time Series Anomaly Detectionen_US
dc.subjectDetection System; Neural-Networks; Sdn; Mitigationen_US
dc.titleFlow-based intrusion detection on software-defined networks: a multivariate time series anomaly detection approachen_US
dc.typeArticleen_US

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