Securing Power Cyber‐Physical Systems Against False Data Injection Attacks: Trends, Techniques, and Future Directions

False Data Injection Attacks (FDIAs) pose a significant threat to the security and stability of Power Cyber-Physical Systems (CPS). As these systems become more interconnected and automated, the risk of malicious data manipulation has increased, leading to potential grid instability, equipment damage, and large-scale outages. This review provides a comprehensive analysis of recent advancements in FDIA detection, defense mechanisms, and resilience strategies. It examines various detection methods, from traditional state estimation to machine learning-based approaches, and discusses the integration of hybrid techniques for enhanced accuracy. The review also explores emerging technologies such as federated learning, quantum computing, and IoT, highlighting their potential in strengthening FDIA defenses. Additionally, the importance of a multi-faceted approach, combining technical, regulatory, and operational solutions, is emphasized for ensuring the long-term security of power CPS. The review concludes with future research directions focused on dynamic attack modeling, adaptive defense systems, and the integration of emerging technologies to address the evolving landscape of cyber threats.