SUIP: An Android malware detection method based on data flow features

Abstract

Currently static detection is the most commonly used in Android malware detection. Among them, the extraction of various features is particularly important. In analysing the data flow features of applications, researchers usually use taint analysis method to extract. However, this method lack intermediate process features. So in this paper, we analyse the features of Android components to obtain application data transfer features for complementing the application data flow features and build a more complete combination of data flow features. Based on this, we propose a new Android malicious application detection method - SUIP. This method complements the missing features based on taint analysis, and combines the LightGBM algorithm to build a detection model. Finally, we use the sample set in Virusshare for experiments. Compared with the traditional static detection method of Android malicious code, the result shows that our detection method has a high detection accuracy of 98.50%.