Mobile app poses both traditional and new potential threats to system security and user privacy. There are malicious apps that may do harm to the system, and there are mis-behaviors of apps, which are reasonable and legal when not abused, yet may lead to real threats otherwise. Moreover, due to the nature of mobile apps, a running app in mobile devices may be only part of the software, and the server side behavior is usually not covered by analysis. Therefore, direct analysis on the app itself may be incomplete and additional sources of information are needed. In this dissertation, we discuss how we can apply machine learning techniques in multiple tasks for security issues in regard of mobile apps in the Android platform. These include malicious apps detection and security risk estimation of apps. Both direct sources of information from the developer of apps and indirect sources of information from user comments are utilized in these tasks. We also propose comparison of these different sources in the task of security risk estimation to point out the necessity of usage of indirect sources in mobile app security tasks.