Robust android malware detection based on subgraph network and denoising GCN network

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.