A Two-Layer Deep Learning Method for Android Malware Detection Using Network Traffic

Abstract

Because of the characteristic of openness and flexibility, Android has become the most popular mobile platform. However, it has also become the most targeted system by mobile malware. It is necessary for the users to have a fast and reliable detection method. In this paper, a two-layer method is proposed to detect malware in Android APPs. The first layer is permission, intent and component information based static malware detection model. It combines the static features with fully connected neural network to detect the malware and test its effectiveness through experiment, the detection rate of the first layer is 95.22%. Then the result (benign APPs from the first layer) is input into the second layer. In the second layer, a new method CACNN which cascades CNN and AutoEncoder, is used to detect malware through network traffic features of APPs. The detection rate of the second layer is 99.3% in binary classification (2-classifier). Moreover, the new two-layer model can also detect malware by its category (4-classifier) and malicious family (40-classifier). The detection rates are 98.2% and 71.48% respectively. The experimental results show that our two-layer method not only can achieve semi-supervise learning, but also can effectively improve the detection rate of malicious Android APPs.