Real-Time Cyber Attack Detection Over HoneyPi Using Machine Learning

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dc.authoridhttps://orcid.org/0000-0001-8529-1721en_US
dc.authoridhttps://orcid.org/0000-0001-9859-1600en_US
dc.contributor.authorAlhan, Birkan
dc.contributor.authorGönen, Serkan
dc.contributor.authorKaracayılmaz, Gökçe
dc.contributor.authorBarışkan, Mehmet Ali
dc.contributor.authorYılmaz, Ercan Nurcan
dc.date.accessioned2023-10-28T09:55:28Z
dc.date.available2023-10-28T09:55:28Z
dc.date.issued2022en_US
dc.departmentMühendislik ve Mimarlık Fakültesien_US
dc.description.abstractThe rapid transition of all areas of our lives to the digital environment has kept people away from their intertwined social lives and made them dependent on the isolated cyber environment. This dependency has led to increased cyber threats and, subsequently, cyber-attacks nationally or internationally. Due to the high cost of cybersecurity systems and the expert nature of these systems' management, the cybersecurity component has been mostly ignored, especially in small and medium-sized organizations. In this context, a holistic cybersecurity architecture is designed in which fully open source and free software and hardware-based Raspberry Pi devices with low-cost embedded operating systems are used as a honeypot. In addition, the architectural structure has an integrated, flexible, and easily configurable end-to-end security approach. It is suitable for different platforms by creating end-user screens with personalized software for network security guards and system administrators.en_US
dc.identifier.doi10.17559/TV-20210523121614en_US
dc.identifier.endpage1401en_US
dc.identifier.issn1330-3651
dc.identifier.issn1848-6339
dc.identifier.issue4en_US
dc.identifier.scopus2-s2.0-85133178541en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage1394en_US
dc.identifier.urihttps://hdl.handle.net/11363/6102
dc.identifier.urihttps://doi.org/
dc.identifier.volume29en_US
dc.identifier.wosWOS:000818875800020en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorAlhan, Birkan
dc.institutionauthorGönen, Serkan
dc.institutionauthorBarışkan, Mehmet Ali
dc.language.isoenen_US
dc.publisherUNIV OSIJEK, TECH FAC, TRG IVANE BRLIC-MAZURANIC 2, SLAVONSKI BROD HR-35000, CROATIAen_US
dc.relation.ispartofTechnical Gazetteen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/us/*
dc.subjectArtificial Intelligenceen_US
dc.subjectCyber Securityen_US
dc.subjectHoneypoten_US
dc.subjectInternet of Thingsen_US
dc.subjectLSTMen_US
dc.subjectNaive Bayesen_US
dc.titleReal-Time Cyber Attack Detection Over HoneyPi Using Machine Learningen_US
dc.typeArticleen_US

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