RPL Attack Detection and Prevention in the Internet of Things Networks Using a GRU Based Deep Learning

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dc.authoridYalcin, Nesibe/0000-0003-0324-9111
dc.authoridyalcin, nesibe/0000-0003-0324-9111
dc.authoridCAKIR, SEMIH/0000-0003-3072-9532
dc.authorwosidYalcin, Nesibe/B-7685-2018
dc.authorwosidyalcin, nesibe/A-2805-2016
dc.contributor.authorCakir, Semih
dc.contributor.authorToklu, Sinan
dc.contributor.authorYalcin, Nesibe
dc.date.accessioned2021-12-01T18:48:43Z
dc.date.available2021-12-01T18:48:43Z
dc.date.issued2020
dc.department[Belirlenecek]en_US
dc.description.abstractCyberattacks targeting Internet of Things (IoT), have increased significantly, over the past decade, with the spread of internet-connected smart devices and applications. Routing Protocol for Low-Power and Lossy Network (RPL) enables messages to be routed between nodes for the Wireless Sensor Network in the network layer. RPL protocol, which is sensitive and difficult to protect, is exposed to various attacks. These attacks negatively affect data transmission and cause great destruction to the topology by consuming the resources. Hello Flooding (HF) attacks against RPL cause consumption of constrained resources (memory, processing and energy) in nodes. Therefore, in this study, a Gated Recurrent Unit network model based deep learning has been proposed to predict and prevent HF attacks on RPL protocol in IoT networks. The proposed model has been compared with Support Vector Machine and Logistic Regression methods, and different power states and total energy consumptions of the nodes have been taken into consideration and experimented with. The results confirm the promised and expected performance from the model in terms of source efficiency and IoT security. In addition, attack detection has been carried out with a much lower error rate than literature studies for HF attacks from RPL flood attacks.en_US
dc.identifier.doi10.1109/ACCESS.2020.3029191
dc.identifier.endpage183689en_US
dc.identifier.issn2169-3536
dc.identifier.scopus2-s2.0-85102766783en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage183678en_US
dc.identifier.urihttps://doi.org/10.1109/ACCESS.2020.3029191
dc.identifier.urihttps://hdl.handle.net/20.500.12684/10592
dc.identifier.volume8en_US
dc.identifier.wosWOS:000584426300001en_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.subjectFloodsen_US
dc.subjectMachine learningen_US
dc.subjectHafniumen_US
dc.subjectRouting protocolsen_US
dc.subjectTopologyen_US
dc.subjectEnergy consumptionen_US
dc.subjectDeep learningen_US
dc.subjectgated recurrent uniten_US
dc.subjecthello floodingen_US
dc.subjectInternet of Thingsen_US
dc.subjectWireless Sensor Networksen_US
dc.subjectIntrusion Detectionen_US
dc.subjectSystemen_US
dc.titleRPL Attack Detection and Prevention in the Internet of Things Networks Using a GRU Based Deep Learningen_US
dc.typeArticleen_US

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