Comparative Analysis of Input Image Characteristics in Convolutional Neural Network-based Signature Detection

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dc.contributor.authorAdamec, M.
dc.contributor.authorTurcanik, M.
dc.coverage.issue2cs
dc.coverage.volume34cs
dc.date.accessioned2025-05-19T07:09:35Z
dc.date.available2025-05-19T07:09:35Z
dc.date.issued2025-06cs
dc.description.abstractThe detection of malware represents a primary concern in contemporary computer security and is therefore imperative for the protection of systems and data integrity. This research presents an innovative approach to comparing diverse input image formats with the objective of identifying the optimal methodology for detecting specific malware-related signatures using convolutional neural networks (CNN), which have been specifically developed by the authors for this purpose. Subsequently, machine code instructions are generated and then converted into four distinct image format options. The four image formats, namely 1xN fixed, 1xN scalable, NxN fixed, and NxN scalable, are subsequently employed for the training of the CNN. The study assesses the formats in question in terms of training time, accuracy, and computational complexity. The results demonstrate that the NxN scalable format exhibits the highest accuracy with accelerated training times in comparison to other formats. Furthermore, the scalable format necessitates only 25% of the original pixel count for a 96% classification success rate. The utilization of the NxN scalable format for machine code instruction representation results in enhanced accuracy, accelerated training, and a considerable reduction in pixel usage, indicating a promising avenue for optimizing the efficiency of malware detection.en
dc.formattextcs
dc.format.extent303-312cs
dc.format.mimetypeapplication/pdfen
dc.identifier.citationRadioengineering. 2025 vol. 34, č. 2, s. 303-312. ISSN 1210-2512cs
dc.identifier.doi10.13164/re.2025.0303en
dc.identifier.issn1210-2512
dc.identifier.urihttps://hdl.handle.net/11012/250924
dc.language.isoencs
dc.publisherRadioengineering Societycs
dc.relation.ispartofRadioengineeringcs
dc.relation.urihttps://www.radioeng.cz/fulltexts/2025/25_02_0303_0312.pdfcs
dc.rightsCreative Commons Attribution 4.0 International licenseen
dc.rights.accessopenAccessen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectSignature detectionen
dc.subjectCNN malware detectionen
dc.subjectmachine code visualizationen
dc.subjectstatic analysisen
dc.subjectinterpolationen
dc.titleComparative Analysis of Input Image Characteristics in Convolutional Neural Network-based Signature Detectionen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
eprints.affiliatedInstitution.facultyFakulta eletrotechniky a komunikačních technologiícs
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