An effective end-to-end android malware detection method. (15th May 2023) - Zhu, Huijuan; Wei, Huahui; Wang, Liangmin; Xu, Zhicheng; Sheng, Victor S.
Abstract: Android has rapidly become the most popular mobile operating system because of its open source, rich hardware selectivity, and millions of applications (Apps). Meanwhile, the open source of Android makes it the main target of malware. Malware detection methods based on manual features are easily bypassed by confusing technologies and are suffering from low code coverage. Thus, we propose an automated extraction method without any manual expert intervention. Specifically, we characterize the vital parts of the Dalvik executable (Dex) to an RGB (Red/Green/Blue) image. Furthermore, we propose a novel convolutional neural network (CNN) variant with diverse receptive fields using max pooling and average pooling simultaneously (MADRF), named MADRF-CNN, which can capture the dependencies between different parts of the image (transferred from the Dex file) by capitalizing on multi-scale context information. To evaluate the effectiveness of the proposed method, we conducted extensive experiments and our experimental results showed that the Accuracy of our method is 96.9%, which is much better than state-of-the-art solutions. Highlights: A novel deep network MADRF-CNN is proposed to learn informative features. A dataset (2, 507 malware and 1, 417 benign ones) is constructed. MADRF-CNN shows its satisfactory performance on malware detection.