Biblio

Smartphones have become ubiquitous in our everyday lives, providing diverse functionalities via millions of applications (apps) that are readily available. To achieve these functionalities, apps need to access and utilize potentially sensitive data, stored in the user's device. This can pose a serious threat to users' security and privacy, when considering malicious or underskilled developers. While application marketplaces, like Google Play store and Apple App store, provide factors like ratings, user reviews, and number of downloads to distinguish benign from risky apps, studies have shown that these metrics are not adequately effective. The security and privacy health of an application should also be considered to generate a more reliable and transparent trustworthiness score. In order to automate the trustworthiness assessment of mobile applications, we introduce the Trust4App framework, which not only considers the publicly available factors mentioned above, but also takes into account the Security and Privacy (S&P) health of an application. Additionally, it considers the S&P posture of a user, and provides an holistic personalized trustworthiness score. While existing automatic trustworthiness frameworks only consider trustworthiness indicators (e.g. permission usage, privacy leaks) individually, Trust4App is, to the best of our knowledge, the first framework to combine these indicators. We also implement a proof-of-concept realization of our framework and demonstrate that Trust4App provides a more comprehensive, intuitive and actionable trustworthiness assessment compared to existing approaches.

Mobile apps are widely adopted in daily life, and contain increasing security flaws. Many regulatory agencies and organizations have announced security guidelines for app development. However, most security guidelines involving technicality and compliance with this requirement is not easily feasible. Thus, we propose Mobile Apps Assessment and Analysis System (MAS), an automatic security validation system to improve guideline compliance. MAS combines static and dynamic analysis techniques, which can be used to verify whether android apps meet the security guideline requirements. We implemented MAS in practice and verified 143 real-world apps produced by the Taiwan government. Besides, we also validated 15,000 popular apps collected from Google Play Store produced in three countries. We found that most apps contain at least three security issues. Finally, we summarize the results and list the most common security flaws for consideration in further app development.