  This paper presents a Temporal Graph Neural Network (TGNN) framework for
detection and localization of false data injection and ramp attacks on the
system state in smart grids. Capturing the topological information of the
system through the GNN framework along with the state measurements can improve
the performance of the detection mechanism. The problem is formulated as a
classification problem through a GNN with message passing mechanism to identify
abnormal measurements. The residual block used in the aggregation process of
message passing and the gated recurrent unit can lead to improved computational
time and performance. The performance of the proposed model has been evaluated
through extensive simulations of power system states and attack scenarios
showing promising performance. The sensitivity of the model to intensity and
location of the attacks and model's detection delay versus detection accuracy
have also been evaluated.
