Digital Twin-Driven Framework for Predictive Cyberattack Detection and Autonomous Resilience in IoT-Enabled Smart Grids

This research proposes a Digital Twin-driven framework for predictive cyberattack detection and autonomous resilience in IoT-enabled smart grids. The key contribution is the integration of real-time Digital Twin modeling, AI-based cyberattack prediction, and autonomous response mechanisms to continuously monitor grid behavior, identify anomalous activities, and mitigate threats before they cause significant disruption. The framework enables adaptive threat intelligence, real-time state synchronization, and self-healing responses, improving the security, resilience, reliability, and operational continuity of smart-grid infrastructures against evolving cyberattacks.