A Data Flow-Based Approach for Classification and Risk Estimation of Android Apps

The growing prevalence of Android applications (apps) has sparked concerns over the security of users' personal information. Of particular concern is the potential leakage of sensitive data, such as GPS locations, user contacts, and IMEI codes, to unauthorized entities. Existing security mechanisms primarily focus on binary classification, distinguishing between malicious and benign files, but fail to address the risks associated with potentially harmful benign apps that unintentionally leak information to third-party users. To bridge this gap, we propose a novel framework that combines static taint analysis and machine learning techniques to classify and identify risks in Android apps. The machine learning model is trained on a dataset comprising data flow-based features from 16,000 malware and benign apps. We evaluate its capability to estimate risks associated with unknown apps using a separate test dataset containing 4,000 apps. The classification accuracy of our mechanism is 94% among them 87 % of malware apps are identified as high-risk and over 1% of the benign apps are also identified as high-risk in the tested dataset. This research represents a significant step forward in enhancing app security and data privacy, catering to the evolving landscape of mobile app threats and ensuring a safer experience for Android users.