Research on Multi-model Android Malicious Application Detection Based on Feature Fusion
Zhan Fang, Jun Liu, Ribian Huang, Peng Chen, Xin Li, Xiao Chen · 2021
With the widespread use of the Android operating system, the number of applications on the platform is increasing, and malicious applications are also emerging. How to effectively identify android malware applications to prevent and protect the security of the mobile terminal is a crucial issue. This paper uses the feature fusion method and directly call the library function to extract the permissions and API features of the APK file, then decompile the APK file to obtain the opcode features and merge the three features with multiple features to generate a feature vector. Finally it use a multi-model neural network HYDRA to learn fusion feature vector, so that it can identify and detect malware. The work also compare it with other single-feature machine learning algorithms to verify its effect. Experimental results show that the accuracy of the multi-model neural network detection method based on feature fusion reaches 98.92%, which is better than other single-model feature methods.