An Effective Risk Computation Metric for Android Malware Detection

Abstract

Android has been targeted by malware developers since it has emerged as widest used operating system for smartphones and mobile devices. Android security mainly relies on user decisions regarding to installing applications (apps) by approving their requested permissions. Therefore, a systematic user assistance mechanism for making appropriate decisions can significantly improve the security of Android based devices by preventing malicious apps installation. However, the criticality of permissions and the security risk values of apps are not well determined for users in order to make correct decisions. In this study, a new metric is introduced for effective risk computation of untrusted apps based on their required permissions. The metric leverages both frequency of permission usage in malwares and rarity of them in normal apps. Based on the proposed metric, an algorithm is developed and implemented for identifying critical permissions and effective risk computation. The proposed solution can be directly used by the mobile owners to make better decisions or by Android markets to filter out suspicious apps for further examination. Empirical evaluations on real malicious and normal app samples show that the proposed metric has high malware detection rate and is superior to recently proposed risk score measurements. Moreover, it has good performance on unseen apps in term of security risk computation.