Cyber-attacks targeting critical infrastructure have increased dramatically in recent years, posing serious risks to national security, economic stability, and public safety. Among the most vulnerable systems are cyber-physical power systems, which integrate physical grid infrastructure with digital communication and control networks. These systems are increasingly exposed to sophisticated cyber threats capable of bypassing traditional defenses. This project aims to enhance the security and resilience of cyber-physical power systems by developing advanced tools to detect and mitigate stealthy cyber-attacks that could otherwise cause widespread outages or damage. The research combines machine learning, optimization, and power system modeling to protect critical electric power grid infrastructure. Beyond its technical innovations, the project will increase participation in STEM by offering workforce development opportunities that are open to all students, with particular attention to engaging participants from a wide range of institutional, geographic, and socioeconomic backgrounds. This project introduces a three-pronged technical framework that tightly integrates the physics of power systems with state-of-the-art computational methods. First, it uses region-based convolutional graph neural networks to detect anomalies in cyber-physical power systems by capturing spatial and temporal dynamics with high fidelity. Second, the project proposes a novel analytical framework base