A Comparison of Features for Android Malware Detection
Matthew Leeds, Miclain Keffeler, Travis Atkison · 2017
With the increase in mobile device use, there is a greater need for increasingly sophisticated malware detection algorithms. The research presented in this paper examines two types of features of Android applications, permission requests and system calls, as a way to detect malware. We are able to differentiate between benign and malicious apps by applying a machine learning algorithm. The model that is presented here achieved a classification accuracy of around 80% using permissions and 60% using system calls for a relatively small dataset. In the future, different machine learning algorithms will be examined to see if there is a more suitable algorithm. More features will also be taken into account and the training set will be expanded.