Feature Selection and Evaluation of Permission-based Android Malware Detection

Android malware is a ubiquitous threat to the information security of mobile users. Android users often download applications from unauthorized and untrusted sources. Such applications may request several permissions from the user, and due to unawareness, the user may grant the required permissions. Android permissions are one of the significant sources of malware infection. By analyzing the permissions database classification of malware and benign applications can be done with the help of machine learning tools. There are a total of 330 permissions in Android applications. However, all of them may not contribute to the classification. In this paper, the proposed system investigates identifying the most influential permissions using feature reduction. The gain ratio is used for feature reduction and J48, Random Committee, Multilayer Perceptron, Sequential Minimal Optimization (SMO), and Randomizable filtered classifiers are used for evaluation of the selected features. The experimentation results show that five permissions can produce near full feature accuracy, thereby optimizing the malware detection system.