  False data injection attacks (FDIA) are becoming an active avenue of research
as such attacks are more frequently encountered in power systems. Contrary to
the detection of these attacks, less attention has been paid to identifying the
attacked units of the grid. To this end, this work jointly studies detecting
and localizing the stealth FDIA in modern power grids. Exploiting the inherent
graph topology of power systems as well as the spatial correlations of smart
meters' data, this paper proposes an approach based on the graph neural network
(GNN) to identify the presence and location of the FDIA. The proposed approach
leverages the auto-regressive moving average (ARMA) type graph convolutional
filters which offer better noise robustness and frequency response flexibility
compared to the polynomial type graph convolutional filters such as Chebyshev.
To the best of our knowledge, this is the first work based on GNN that
automatically detects and localizes FDIA in power systems. Extensive
simulations and visualizations show that the proposed approach outperforms the
available methods in both detection and localization FDIA for different IEEE
test systems. Thus, the targeted areas in power grids can be identified and
preventive actions can be taken before the attack impacts the grid.
