  Executable programs are highly structured files that can be recognized by
operating systems and loaded into memory, analyzed for their dependencies,
allocated resources, and ultimately executed. Each section of an executable
program possesses distinct file and semantic boundaries, resembling puzzle
pieces with varying shapes, textures, and sizes. These individualistic
sections, when combined in diverse manners, constitute a complete executable
program. This paper proposes a novel approach for the visualization and
classification of malware. Specifically, we segment the grayscale images
generated from malware binary files based on the section categories, resulting
in multiple sub-images of different classes. These sub-images are then treated
as multi-channel images and input into a deep convolutional neural network for
malware classification. Experimental results demonstrate that images of
different malware section classes exhibit favorable classification
characteristics. Additionally, we discuss how the width alignment of malware
grayscale images can influence the performance of the model.
