MC-ISA: A Multi-Channel Code Visualization Method for Malware Detection

Abstract

Malware detection has always been a hot topic in the cyber security field. With continuous research over the years, many research methods and detection tools based on code visualization have been proposed and achieved good results. However, in the process of code visualization, the existing methods have some issues such as feature scarcity, feature loss and excessive dependence on manual analysis. To address these issues, we propose in this paper a code visualization method with multi-channel image size adaptation (MC-ISA) that can detect large-scale samples more quickly without manual reverse analysis. Experimental results demonstrate that MC-ISA achieves both higher accuracy and F1-score than the existing B2M algorithm after introducing three mechanisms including image size adaptive, color enhancement and multi-channel enhancement.