Abstract
In this paper, we propose An Android Behavior-Based Malware Detection Method using Machine Learning. We improve an Android application sandbox, Droidbox, by inserting a view-identification automatic trigger program which can click mobile applications in the meaningful order. Taking advantage of Droidbox result, we collect the behavior such as network activities, file read/write and permission as the feature data and use different machine learning algorithms to classify malware and evaluate the performance. We use a large number of malware and normal application samples to prove that our method has high accuracy.