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An Effective End-to-end Android Malware Detection Method by Huijuan Zhu; Huahui Wei; Liangmin Wang; Zhicheng Xu; Victor S. Sheng is a Computer Science article available to read on EtoBox.

What is An Effective End-to-end Android Malware Detection Method about?

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.

Who reads An Effective End-to-end Android Malware Detection Method?

Author

Huijuan Zhu; Huahui Wei; Liangmin Wang; Zhicheng Xu; Victor S. Sheng

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Computer Science (Physical Sciences)