科研成果详情

To mitigate security problem brought by Android malware, various work has been proposed such as behavior based malware detection and data mining based malware detection. In this paper, we put forward a novel Android malware detection model using data mining techniques. We design an algorithm with two steps. The first step is modeling Android application code into graph structure, called API control flow graph by us. Next step is calculating API sequences fulfilling minimum intra-family support in each malware family because malware in malware family usually share similar behavior pattern. Finally, supervised learning method is took advantage in building our malware detecting model with API sequences as input features. We evaluate this model with 1200 applications, half of them are malicious and half are benign, and find it effective in identifying Android malware and even unknown malware.