Hybrid Full Recovery System

Digital data backups are widely used as evidence for operational continuity in legal, forensic, cybersecurity, and enterprise environments. However, the reliability of traditional digital backups is increasingly challenged by ransomware encryption, accidental mass deletion, storage overhead, and application performance bottlenecks caused by static scheduling. This research presents the Hybrid Full Recovery System, an event-driven data protection framework that performs real-time filesystem monitoring, intelligent snapshot triggering, resource-aware scheduling, and automated recovery guidance in a unified graphical workflow. The proposed system follows a modular Python-based architecture and integrates a non-blocking UI thread, asynchronous background workers, and persistent system-tray management to support explainable recovery decisions. Instead of depending on static timers or simple periodic copies, the system combines Smart Triggers, deletion burst detection, incremental delta archives, and resource thresholds to derive optimal recovery points. Experimental observations demonstrate improved performance, reduced storage overhead, and enhanced recovery reliability compared to traditional approaches.