Design of a Robust Mobile Phone Hacking Model Using Random Forest

Managing smart homes, banking, shopping, and communication in today’s digital world call for mobile phones. However, because these gadgets are hacker-prone, there are significant hazards to personal privacy, digital trust, and financial security. The primary objective of this research is to develop a trustworthy Mobile Phone Hacking Detection Model (MPHDM) in order to lower these dangers. Key indicators of hacking can be found by gathering and examining data from network traffic, application logs, mobile device logs, and user activity patterns. For categorization, advanced machine learning methods including K-Nearest Neighbors (KNNs), Random Forest, and Decision Tree are used. In terms of accuracy, the Random Forest model proved to be the most successful in real-time detection, surpassing the other models. Owners of the device are empowered by this paradigm to quickly reduce any possible harm. This research highlights the significance of early detection and offers a comprehensive a solution to strengthen smartphone security against dynamic risks found online.