The increasing use of mobile devices has increased the risk of data theft, posing significant security challenges for individuals and organizations. This study proposes an early detection system for data theft on mobile devices using machine learning algorithms. The system is designed to identify suspicious patterns in application usage, network access, and CPU/memory activity, providing early warnings to prevent potential data loss. By employing algorithms such as Random Forest, Support Vector Machine (SVM), and Convolutional Neural Network (CNN), the developed models demonstrated significant performance: CNN achieved the highest accuracy of 95.1%, with a precision of 94.2%, recall of 93.5% , F1-score of 93.8%, and AUC-ROC of 0.96. Random Forest and SVM also showed competitive performance with accuracy rates of 94.7% and 92.5%, respectively. These findings highlight the high potential of machine learning algorithms for real-time detection of data theft threats, providing adaptive protection against evolving cyberattack methods. This approach offers a promising solution to strengthen mobile device security frameworks and safeguard user data against increasingly sophisticated cyber threats.
