Android Malware Detection Based on RGB Images
and Multi-feature Fusion
Zhiqiang Wang *
Department of Cyberspace Security,
Beijing Electronic Science and
Technology Institute
Beijing, China

Abstract—With the widespread adoption of smartphones,
Android malware has become a significant challenge in the field
of mobile device security. Current Android malware detection
methods often rely on feature engineering to construct dynamic
or static features, which are then used for learning. However, static feature-based methods struggle to counter code obfuscation, packing, and signing techniques, while dynamic
feature-based methods involve time-consuming feature extraction. Image-based methods for Android malware detection offer better resilience against malware variants and polymorphic malware. This paper proposes an end-to-end Android malware detection technique based on RGB images and multi-feature fusion.

#Malware #MalwareDetection #Research #Analysis #RGB