Dynamic Permissions based Android Malware Detection using Machine Learning Techniques

A set of 123 dynamic permissions from 11000 Android applications is extracted in a largest publicly available dataset till date and a number of machine learning classification techniques including Naive Bayes (NB), Decision Tree (J48), Random Forest (RF), Simple Logistic (SL), and k-star are evaluated on the newly designed dataset for detecting malicious Android applications.