Learning Android Malware
Khanh Huu The Dam, Tayssir Touili · 2017
The number of Android malware is increasing every day. Thus Android malware detection is nowadays a big challenge. One of the most tedious tasks in malware detection is the extraction of malicious behaviors. This task is usually done manually and requires a huge effort of engineering. To avoid this step, we propose in this paper to use machine learning techniques for malware detection. Unlike the existing learning based approaches, we propose to use API call graphs to represent the behaviors of Android applications. Then, given a set of malicious applications and a set of benign applications, we apply well-known learning techniques based on Random Walk Graph Kernel (combined with Support Vector Machines). We can achieve a high detection rate with only few false alarms (98.76% for detection rate with 0.24% of false alarms).