Towards Automated Risk Assessment and Mitigation of Mobile Applications

Mobile operating systems, such as Apple's iOS and Google's Android, have supported a ballooning market of feature-rich mobile applications. However, helping users understand and mitigate security risks of mobile applications is still an ongoing challenge. While recent work has developed various techniques to reveal suspicious behaviors of mobile applications, there exists little work to answer the following question: are those behaviors necessarily inappropriate? In this paper, we seek an approach to cope with such a challenge and present a continuous and automated risk assessment framework called RiskMon that uses machine-learned ranking to assess risks incurred by users' mobile applications, especially Android applications. RiskMon combines users' coarse expectations and runtime behaviors of trusted applications to generate a risk assessment baseline that captures appropriate behaviors of applications. With the baseline, RiskMon assigns a risk score on every access attempt on sensitive information and ranks applications by their cumulative risk scores. Furthermore, we demonstrate how RiskMon supports risk mitigation with automated permission revocation. We also discuss a proof-of-concept implementation of RiskMon as an extension of the Android mobile platform and provide both system evaluation and usability study of our methodology.