Machine Learning-Driven Anomaly Detection: A Comprehensive Model for Digital Security

This research study proposes a novel approach for behavioral tracking and anomaly detection in digital systems by using AI-driven models, particularly for applications in signal processing and digital computer environments. The proposed AI model combines techniques of behavioral tracking, aiming at identifying deviations in the operations of the system, while enhanced monitoring and anomaly identification are the focus. This methodology captures subtle deviations in user behavior and system signals to enhance real-time security and system integrity, thereby reducing the likelihood of unauthorized access and system misuse.