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http://dx.doi.org/10.13089/JKIISC.2022.32.2.381

Image Generation Method for Malware Detection Based on Machine Learning  

Jeon, YeJin (Korea Aerospace University)
Kim, Jin-e (Korea Aerospace University)
Ahn, Joonseon (Korea Aerospace University)
Publication Information
Journal of the Korea Institute of Information Security & Cryptology / v.32, no.2, 2022 , pp. 381-390 More about this Journal
Abstract
Many attempts have been made to apply image recognition based on machine learning which has recently advanced dramatically to malware detection. They convert executable files to images and train deep learning networks like CNN to recognize or categorize dangerous executable files, which shows promising results. In this study, we are looking for an effective image generation method that may be used to identify malware using machine learning. To that end, we experiment and assess the effectiveness of various image generation methods in relation to malware detection. Then, we suggest a linear image creation method which represents control flow more clearly and our experiment shows our method can result in better precision in malware detection.
Keywords
malware detection; static analysis; machine learning; image recognition;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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