Robust android malware detection based on subgraph network and denoising GCN network
Xiaofeng Lu, Jinglun Zhao, Píetro Lió · 2022
This paper proposes an Android malware detection model based on Android Function Call Graph (FCG) and Denoising Graph Convolutional Neural Network. This study proposes a method to simplify the FCG to reduce its size, and a new method to construct vertex feature vectors. The model uses the subgraph network to detect the underlying structural features of the FCG and discover the confusion attack. A denoising graph neural network is applied to graph convolution to reduce the impact of obfuscation attacks.