Research on Personal Privacy Security Detection Techniques for Android Applications

With the number of mobile applications (apps) increasing year by year, they have become an integral part of our daily lives as they are used to access a wide range of services that are available everywhere. While there are many benefits to using apps, there are also risks associated with using private personal information. This is because most apps need to collect personal information from users. In this process, most apps are subject to various sensitive behaviors, for example, some apps illegally collect and expose users' personal information, and users' privacy security is greatly threatened. Therefore, the privacy security of mobile apps has gradually become a focus of attention. In this paper, we propose an app sensitive privacy behavior detection method that combines hybrid static and dynamic detection with machine learning, so as to strengthen the protection of user privacy. In this paper, we also select privacy-sensitive key permissions and API calls that are highly correlated with privacy leakage behaviors, and develop new sensitivity criteria based on them. Comparative experimental results show that our method achieves 97% accuracy in privacy leakage detection and also detects malicious behaviors that leak users' privacy information through comprehensive analysis.