  False data injection attacks (FDIAs) represent a major class of attacks that
aim to break the integrity of measurements by injecting false data into the
smart metering devices in power grid. To the best of authors' knowledge, no
study has attempted to design a detector that automatically models the
underlying graph topology and spatially correlated measurement data of the
smart grids to better detect cyber attacks. The contributions of this paper to
detect and mitigate FDIAs are twofold. First, we present a generic, localized,
and stealth (unobservable) attack generation methodology and a publicly
accessible dataset for researchers to develop and test their algorithms.
Second, we propose a Graph Neural Network (GNN) based, scalable and real-time
detector of FDIAs that efficiently combines model-driven and data-driven
approaches by incorporating the inherent physical connections of modern AC
power grids and exploiting the spatial correlations of the measurement data. It
is experimentally verified by comparing the proposed GNN based detector with
the currently available FDIA detectors in literature that our algorithm
outperforms the best available solutions by 6.21\%, 0.69\%, and 2.73\% in
detection rate and by 3.65\%, 0.34\% and 1.38\% in F1 score for standard IEEE
testbeds with 14, 118, and 300 buses, respectively.
