CONCLUSION AND FUTURE WORK
Android malware became potential risk to smart phone applications. The rationale behind this is the unprecedented popularity of Android platform for mobile phones. In this paper we studied different Android malware detection approaches in the literature and found the need for an approach that is cost effective besides increasing accuracy of prediction. We proposed a framework for building a classifier that takes care of malware detection. The proposed approach is based on the permissions of Android mobile apps. We proposed an algorithm known as Permission Significance-based Pruning for Android Malware Detection (PSP-AMD) that identifies significance of permissions based on the given dataset and perform pruning and ranking in order to build a final model that can be used to detect Android malware. Experiments are made with malware dataset collected from VirusTotal. We built a prototype application to demonstrate proof of the concept. The empirical results revealed that the proposed solution is effective in detection of Android malware. In future we investigate further in the permission pruning and ranking to optimize our solution. We also find it interested to work with other datasets to generalize our findings.