University of Houston

In this work we determine the security risk posed by Google Play applications to mobile device users by using statistical and machine learning techniques. A Google Play application is characterized by its description, a category and a set of security permissions required to perform its described functions. Typically mobile users install applications based on their descriptions and don't necessarily analyze the permission settings. Therefore, an application is defined as unsafe or risky when some permissions are accessed which provide potentially sensitive and confidential information and not justified by its description. We use Stanford Topic Modeling Toolbox (TMT) to perfom topic model learning on a given dataset and then perform inferencing on a new corpus.