Cyber-Physical Resilience Against Coordinated False-Data Injection Attacks in Smart Grids

The increasing integration of communication networks, intelligent electronic devices, distributed energy resources, and automated control has transformed the electric grid into a tightly coupled cyber-physical system. This transformation also creates opportunities for false-data injection attacks (FDIAs), in which adversaries manipulate measurements or telemetry to bias state estimation and influence operational decisions. Coordinated FDIAs are especially challenging because several measurements, devices, or communication paths can be manipulated in a spatially and temporally consistent manner. A detector that examines individual measurements in isolation may therefore fail to identify an attack that appears plausible at each local point while being harmful at system level. This paper develops a cyber-physical resilience framework for coordinated FDIAs. The framework extends protection beyond attack detection by organizing defenses into preparation, observation, detection, localization, response, recovery, and adaptation. It combines physics-based state estimation with data-driven anomaly detection, network and device telemetry, adaptive measurement trust, and operational safeguards. The paper also proposes a reproducible simulation protocol for evaluating the framework on benchmark power-system models under different attack coverage, duration, grid-condition, and communication-failure scenarios. Rather than reporting unperformed experiments, the manuscript defines the metrics, baselines, ablation studies, and reporting standards required for a credible empirical evaluation. Finally, it discusses how recent work on adversarially robust photovoltaic diagnosis can inform hybrid detector design while distinguishing photovoltaic fault diagnosis from grid-level FDIA detection. The result is a structured research agenda for designing smart-grid defenses that preserve safe operation, not merely classification accuracy.