IoT Malware Detection Based on OPCODE Purification

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dc.authorid0000-0002-0804-3588en_US
dc.authorid0000-0001-6610-6324en_US
dc.authorid0000-0002-1846-6090en_US
dc.authorid0000-0002-0233-064Xen_US
dc.contributor.authorGülataş, İbrahim
dc.contributor.authorKılınç, Hacı Hakan
dc.contributor.authorAydın, Muhammed Ali
dc.contributor.authorZaim, Abdul Halim
dc.date.accessioned2024-03-27T09:19:11Z
dc.date.available2024-03-27T09:19:11Z
dc.date.issued2023en_US
dc.departmentFakülteler, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.description.abstractMalware threat for Internet of Things (IoT) devices is increasing day by day. The constrained nature of IoT devices makes it impossible to apply high-resource-demand ing anti-malware tools for these devices. Therefore there is an enormous need for lightweight and efficient anti-malware solutions for IoT devices. In this study, machine learning-based malware detection is performed using purified OPCODE analysis for IoT devices with MIPS architecture. The proposed methodology reduced the runtime of IoT malware detection up to 7.2 times without reducing the accuracy ratio.en_US
dc.identifier.doi10.5152/electrica.2023.23043en_US
dc.identifier.endpage642en_US
dc.identifier.issue3en_US
dc.identifier.startpage634en_US
dc.identifier.trdizinid1264875en_US
dc.identifier.urihttps://hdl.handle.net/11467/7202
dc.identifier.urihttps://doi.org/10.5152/electrica.2023.23043
dc.identifier.volume23en_US
dc.identifier.wosWOS:001093363400020en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakTR-Dizinen_US
dc.language.isoenen_US
dc.publisherIUC University Press, AVESen_US
dc.relation.ispartofElectricaen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Başka Kurum Yazarıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectInternet of Things Malware detection, malware analysis, Operation Code analysisen_US
dc.titleIoT Malware Detection Based on OPCODE Purificationen_US
dc.typeArticleen_US

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