Call Graph Based Android Malware Detection with CNN

With the increasing shipment of Android malware, malicious APK detection becomes more important. Based on static analysis, we propose a new perspective to detect malicious behaviors. In particular, we extract the patterns of suspicious APIs which are invoked together. We call these patterns local features. We propose a convolutional neural network(CNN) model based on APK’s call graph to extract local features. With the comparison of detection experiments, we demonstrate that the local features indeed help to detect malicious APKs and our model is effective in extracting local features.