An investigation study for risk calculation of security vulnerabilities on android applications

Applications within mobile devices, although useful and entertaining, come with security risks to private information stored the device such as name, address, date of birth. Standards, frameworks, models, metrics have been proposed implemented combat these vulnerabilities, but they remain persist today. In this review, we discuss risk calculation android applications which is used determine overall an application. Besides, also present permission-based access control models that can be evaluate application user data. The study focuses on examining predictive analysis using machine learning. We conduct a comprehensive review leading studies accomplished investigating vulnerabilities for Android platform. examines various well-known prediction highlights sources technique, performance models. Some frameworks prove promising there still much more research needed done regarding applications.