Employing machine learning based malicious signal detection for cognitive radio networks

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dc.authoridBayrakdar, M. Enes/0000-0001-9446-0988
dc.authorwosidBayrakdar, M. Enes/I-8075-2019
dc.contributor.authorTürkyılmaz, Yasin
dc.contributor.authorŞentürk, Arafat
dc.contributor.authorBayrakdar, Muhammed Enes
dc.date.accessioned2023-07-26T11:55:00Z
dc.date.available2023-07-26T11:55:00Z
dc.date.issued2023
dc.departmentDÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.description.abstractIn cognitive radio networks, the empty spectrum that is also named as spectrum hole is detected with the help of spectrum sensing techniques. Energy detection is the most utilized spectrum sensing technique owing to its low complexity. In the energy detection technique, a spectrum hole is detected with a predefined threshold. In this article, machine learning based malicious signal detection is employed for cognitive radio networks. The design of cognitive radio users and network environment is simulated with Riverbed simulation software. The received signal is controlled whether it is a malicious signal or just a secure sensing signal. The fuzzy logic based system is utilized for the security categorization of spectrum sensing signals as malicious, suspicious, and secure sensing signals. Fuzzy logic parameters are taken from the machine learning features that are chosen as the most effective 3 features among all 49 features. The security of primary users is enhanced when compared to other schemes in the literature. The results of the proposed machine learning based malicious signal detection system are validated with the results acquired from the fuzzy logic based approach. The random forest method gives the best results among all machine learning methods for the detection of signals.en_US
dc.identifier.doi10.1002/cpe.7457
dc.identifier.issn1532-0626
dc.identifier.issn1532-0634
dc.identifier.issue2en_US
dc.identifier.scopus2-s2.0-85141166047en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.urihttps://doi.org/10.1002/cpe.7457
dc.identifier.urihttps://hdl.handle.net/20.500.12684/12976
dc.identifier.volume35en_US
dc.identifier.wosWOS:000876244100001en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorTürkyılmaz, Yasin
dc.institutionauthorŞentürk, Arafat
dc.institutionauthorBayrakdar, Muhammed Enes
dc.language.isoenen_US
dc.publisherWileyen_US
dc.relation.ispartofConcurrency and Computation-Practice & Experienceen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz$2023V1Guncelleme$en_US
dc.subjectCognitive Radio Networks; Fuzzy Logic; Machine Learning; Securityen_US
dc.subjectSpectrum Access; Security; Nomaen_US
dc.titleEmploying machine learning based malicious signal detection for cognitive radio networksen_US
dc.typeArticleen_US

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