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http://dx.doi.org/10.33778/kcsa.2021.21.4.069

Analysis of Effects of Image Format on Detection Performance and Resource Usage in CNN-Based Malware Detection  

Seong-hyeon Byeon (국방대학교 국방과학학과)
Young-won Kim (국방대학교 국방과학학과)
Kwan-seob Ko (국방대학교 국방과학학과)
Soo-jin Lee (국방대학교 국방과학학과)
Publication Information
Convergence Security Journal / v.21, no.4, 2021 , pp. 69-75 More about this Journal
Abstract
Various image formats are being used when attempting to construct a malware detection model based on CNN. However, most previous studies emphasize only the detection or classification performance, and do not take into account the possible impact of image format on detection performance and resource usage. Therefore, in this paper, we analyze how the input image formats affect detection performance and resources usage when detecting android malware based on CNN. The dataset used in the experiment is the CICAndMal2017 Dataset. Subdataset extracted from the CICAndMal2017 Dataset were converted into images in four formats: BMP, JPG, PNG, and TIFF. We then trained our CNN model and measured malware detection performance and resource usage. As a result, there was no sifnificant difference between detection performance and the GPU/RAM usage, even if the image format changed. However, we found that the file size of the generated images varied by up to six times depending on the image format, and that significant differences occurred in the training time.
Keywords
CNN; Image; Malware; Detection; Classification; Resource Usage;
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