Android Malware Detection Based on RGB Images and Multi-feature Fusion
Zhiqiang Wang, Qiulong Yu, Sicheng Yuan

TL;DR
This paper introduces an innovative Android malware detection method that converts app features into RGB images and uses image classification models, achieving high accuracy and robustness against malware variants.
Contribution
It presents a novel end-to-end detection approach combining multi-feature fusion into RGB images analyzed by image classification models, outperforming traditional feature-based methods.
Findings
Achieves up to 97.25% detection accuracy.
Outperforms existing methods relying solely on DEX files.
Validates effectiveness of multi-feature fusion in malware detection.
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. The approach involves extracting Dalvik Executable (DEX) files, AndroidManifest.xml files, and API calls from APK files, converting them into grayscale images, and enhancing their…
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Taxonomy
TopicsAdvanced Malware Detection Techniques
