Examples of normalized grayscale images for samples of three different types of malware graph embedding representations after transformation in Dataset 1.

Examples of normalized grayscale images for samples of three different types of malware graph embedding representations after transformation in Dataset 1.

<p>(a), (b), and (c) belong to the Normal type, while (d), (e), and (f) belong to the Ransom type, and (g), (h), and (i) belong to the Miner type.</p>

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Bibliographic Details
Main Author:	Cuihua Ma (14855149) (author)
Other Authors:	Zhenwan Li (20890032) (author), Haixia Long (556765) (author), Anas Bilal (11785200) (author), Xiaowen Liu (190190) (author)
Published:	2025
Subjects:	Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified smote ), ensuring many existing methods incorporating node attributes convolutional neural network develop advanced models deep learning techniques world malware datasets structural information inherent graph convolutional network structural information network threats malware detection machine learning imbalanced datasets approach models xlink "> typically rely two real synthetic minority sequential nature sequence instructions sampling technique rigorously evaluated results demonstrate introduce first increasingly turning growing complexity grayscale images extensive experiments effectively capture directional relationships directed graph dgcn ). cnn ). classified using approaches consider approach compared
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