From malware samples to fractal images: A new paradigm for classification

Ivan Zelinka, Miloslav Szczypka, Jan Plucar and Nikolay Kuznetsov

Mathematics and Computers in Simulation (MATCOM), 2024, vol. 218, issue C, 174-203

Abstract: To date, a large number of research papers have been written on malware classification, identification, classification into different families, and the distinction between malware and goodware. These works have been based on captured malware samples and have attempted to analyse malware and goodware using various techniques like the analysis of malware using malware visualization. These works usually convert malware samples capturing the malware structure into image structures which are then subject to image processing. In this paper, we propose an unconventional and novel approach to malware visualization based on its dynamical analysis, subsequent complex network conversion and fractal geometry, e.g. Julia sets visualization. Very interesting images being subsequently used to classify as malware and goodware. The classification is done by deep learning network. The results of the presented experiments of fractal conversion and subsequent classification are based on a database of 6,589,997 goodware, 827,853 potentially unwanted applications and 4,174,203 malware samples provided by ESET.11https://www.eset.com. This paper aims to show a new direction in visualizing malware using fractal geometry and possibilities in analysis and classification.

Keywords: Malware detection model; Malware classification method; Malware dynamical analysis; Goodware; Deep image processing; Fractal geometry (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:218:y:2024:i:c:p:174-203

DOI: 10.1016/j.matcom.2023.11.032

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