Permission-based abnormal application detection for android

Abstract

Android has become one of the most popular mobile operating system because of numerous applications it provides. Android Market is the official application store which allows users to search and install applications to their Android devices. However, with the increasingly number of applications, malware is also beginning to turn up in app stores. To mitigate the security problem brought by malware, we put forward a novel permission-based abnormal application detection framework which identifies potentially dangerous apps by the reliability of their permission lists. To judge the reliability of app's permissions, we make use of the relation between app's description text and its permission list. In detail, we use Naive Bayes with Multinomial Event Model algorithm to build the relation between the description and the permission list of an application. We evaluate this framework with 5,685 applications in Android Market and find it effective in identifying abnormal application in Android Market. © 2012 Springer-Verlag.