Android malware detection based on multi-feature fusion and deep learning

Zhendong Wang; LiChen Xiong; Junling Wang; Dahai Li

doi:10.1117/12.3029105

6 May 2024 Android malware detection based on multi-feature fusion and deep learning

Zhendong Wang, LiChen Xiong, Junling Wang, Dahai Li

Proceedings Volume 13107, Fourth International Conference on Sensors and Information Technology (ICSI 2024); 131072C (2024) https://doi.org/10.1117/12.3029105
Event: Fourth International Conference on Sensors and Information Technology (ICSI 2024), 2024, Xiamen, China

Abstract

In recent years, the ubiquity of mobile devices has witnessed a concomitant surge in malware attacks, with Android platforms emerging as predominant targets. The inherent open-source architecture of the Android operating system inadvertently paves the way for the proliferation of malevolent applications. Conventional malware detection methodologies, which predominantly hinge on manual feature extraction and juxtaposition against feature repositories, are notably resource-draining. Furthermore, an over-reliance on singular features often obfuscates the demarcation between benign and malevolent applications. To address this lacuna, this study propounds an innovative Android malicious code detection paradigm, amalgamating multifarious features with the prowess of deep learning. By decompiling APK (Android application package), we extract three static features: Opcode (operation code), API (application program interface) call, and Permission (permission). We use the N-Gram method to process the operation code to improve the extraction efficiency, filter out effective permissions to improve the classification accuracy, and select APIs related to permissions to increase the logic between features. After encoding, we input them into DNN for classification. Experiments on a dataset containing 8008 applications show that using multi-features and deep learning networks can significantly improve the accuracy of malware detection, verifying the superiority of this method in detecting malicious code.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zhendong Wang, LiChen Xiong, Junling Wang, and Dahai Li "Android malware detection based on multi-feature fusion and deep learning", Proc. SPIE 13107, Fourth International Conference on Sensors and Information Technology (ICSI 2024), 131072C (6 May 2024); https://doi.org/10.1117/12.3029105

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KEYWORDS

Deep learning

Feature extraction

Performance modeling

Education and training

Machine learning

Data modeling

Neural networks

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