TRConv: Multi-Platform Malware Classification via Target Regulated Convolutions

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dc.authoridEgitmen, Alper/0000-0001-9249-3700
dc.contributor.authorEgitmen, Alper
dc.contributor.authorYavuz, Ali Gokhan
dc.contributor.authorYavuz, Sirma
dc.date.accessioned2025-02-20T08:42:15Z
dc.date.available2025-02-20T08:42:15Z
dc.date.issued2024
dc.departmentTürk-Alman Üniversitesien_US
dc.description.abstractMalware is an important threat to digital workflow. Traditional malware modeling approaches focused on using hand-crafted features while recent approaches proved the necessity of using learning based methodologies. In this paper, we propose a novel opcode based methodology that additionally learns multiple behavioral target variables to effectively regulate and guide the static malware classification. Our methodology shows that introduction of previously extracted malware behavior-related target variables immediately improve binary malware classification performance in both Android and Windows platforms. The contributions of our methodology has been extensively validated on the AMDArgus and the MOTIF dataset. Mean classification accuracy and F1 scores suggest that our model is robust against random opcode injection attacks compared to other convolution based architectures.
dc.description.sponsorshipYimath;ldimath;z Technical University Scientific Research Projects Coordination Unit
dc.description.sponsorshipNo Statement Available
dc.identifier.doi10.1109/ACCESS.2024.3401627
dc.identifier.endpage71504en_US
dc.identifier.issn2169-3536
dc.identifier.scopus2-s2.0-85193266679
dc.identifier.scopusqualityQ1
dc.identifier.startpage71492en_US
dc.identifier.urihttps://doi.org/10.1109/ACCESS.2024.3401627
dc.identifier.urihttps://hdl.handle.net/20.500.12846/1608
dc.identifier.volume12en_US
dc.identifier.wosWOS:001231349900001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherIeee-Inst Electrical Electronics Engineers Inc
dc.relation.ispartofIeee Access
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20250220
dc.subjectConvolutional networken_US
dc.subjectopcode lengthen_US
dc.subjectmalware and benignen_US
dc.subjectmalware behaviour analysisen_US
dc.titleTRConv: Multi-Platform Malware Classification via Target Regulated Convolutions
dc.typeArticle

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