An Android Malware Detection Framework Using Graph Embeddings and Convolutional Neural Networks

With the widespread use of mobile phones, the number of malware targeting smart devices has increased exponentially. In particular, the number of malware targeting Android devices, as it is the most popular operative system among smartphones. This paper proposes a novel framework for android malware detection based on the function call graph representation of an application. Our method generates an embedding of the function call graph using random walks and then, a convolutional neural network extracts features from their embedded matrix representation and labels a given application as benign or malicious considering the learned features. The method has been evaluated on a dataset of 3871 APKs and compared against DREBIN, a baseline benchmark. Experiments show that the method achieves competitive results without relying on the manual extraction of features.