In-depth feature selection and ranking for automated detection of mobile malware

New malware detection techniques are highly needed due to the increasing threat posed by mobile malware. Machine learning techniques have provided promising results in this problem domain. However, feature selection, which is an essential instrument to overcome the curse of dimensionality, presenting higher interpretable results and optimizing the utilization of computational resources, requires more attention in order to induce better learning models for mobile malware detection. In this paper, in order to find out the minimum feature set that provides higher accuracy and analyze the discriminatory powers of different features, we employed feature selection and ranking methods to datasets characterized by system calls and permissions. These features were extracted from malware application samples belonging to two different time-frames (2010-2012 and 2017-2018) and benign applications. We demonstrated that selected feature sets with small sizes, in both feature categories, are able to provide high accuracy results. However, we identified a decline in the discriminatory power of the selected features in both categories when the dataset is induced by the recent malware samples instead of old ones, indicating a concept drift. Although we plan to model the concept drift in our future studies, the feature selection results presented in this study give a valuable insight regarding the change occurred in the best discriminating features during the evolvement of mobile malware over time.