With the increasing integration of cyber-physical systems (CPS) in smart grids, state estimation based on hybrid supervisory control and data acquisition (SCADA) and wide area measurement systems (WAMS) measurement has significantly enhanced observability and accuracy. However, this integration also exposes the power system to more sophisticated cyber threats, particularly coordinated false data injection attacks (FDIAs), which can bypass traditional bad data detection (BDD) mechanisms by collaborative tampering measurements. This paper investigates hybrid SCADA/WAMS measurement systems to elucidate their susceptibility to coordinated FDIA and proposes a robust detection framework towards the FDIA. First, from the attacker’s perspective, a FDIA vector is constructed based on a hybrid measurement state estimation model employing the weighted least squares (WLS) method, designed to maximize attack impact while evading detection. Second, from the defender’s perspective, to address the detection of coordinated FDIA, a graph attention network (GAT)-based detection model is proposed, integrating grid topology and measurement features for high-precision attack identification. Finally, comprehensive case studies are conducted to verify the feasibility of the coordinated attack strategy and the effectiveness of the detection model. Performance and sensitivity analyses are conducted, and model interpretability is corroborated through attention weight visualization.