An Improved Permission Management Scheme of Android Application Based on Machine Learning

Abstract

The Android permission mechanism prevents malicious application from accessing the mobile multimedia data and invoking the sensitive API. However, there are still lots of deficiencies in the current permission management, which results in the permission mechanism being unable to protect users’ private data properly. In this paper, a dynamic management scheme of Android permission based on machine learning is proposed to solve the problem of the existing permission mechanism. In order to accomplish the dynamic management, the proposed scheme maintains a dynamic permission management database which records the state of permissions for each application. Only the permission which is granted state in the database can be used in this application. In the whole process, the scheme first classifies the application by means of machine learning, then retrieves the corresponding permission information from databases, and issues the dangerous permission warning to users. Finally, the scheme updates the dynamic management database according to the users’ decisions. Through this scheme, users can prevent malicious behaviour of accessing private data and invoking sensitive API in time. The solution increases the flexibility of permission management and improves the security and reliability of multimedia data in Android devices.