Android Malware Detection Using Control Flow Graphs and Text Analysis

The Android OS has a massive user-base with hundreds of millions of devices. Due to the growth of this platform, an increasing number of malicious applications are becoming available to download online. We propose a tool that, when provided a sample application, performs binary classification of the sample as either malicious or benign. We constructed control flow graphs from the API and library calls made by the sample application. We then used control flow graphs to train classification models that utilized text analysis methods such as TF-IDF. Our technique reported accuracy rates of up to 95%.