  Recent studies have demonstrated that smart grids are vulnerable to stealthy
false data injection attacks (SFDIAs), as SFDIAs can bypass residual-based bad
data detection mechanisms. The SFDIA detection has become one of the focuses of
smart grid research. Methods based on deep learning technology have shown
promising accuracy in the detection of SFDIAs. However, most existing methods
rely on the temporal structure of a sequence of measurements but do not take
account of the spatial structure between buses and transmission lines. To
address this issue, we propose a spatiotemporal deep network, PowerFDNet, for
the SFDIA detection in AC-model power grids. The PowerFDNet consists of two
sub-architectures: spatial architecture (SA) and temporal architecture (TA).
The SA is aimed at extracting representations of bus/line measurements and
modeling the spatial structure based on their representations. The TA is aimed
at modeling the temporal structure of a sequence of measurements. Therefore,
the proposed PowerFDNet can effectively model the spatiotemporal structure of
measurements. Case studies on the detection of SFDIAs on the benchmark smart
grids show that the PowerFDNet achieved significant improvement compared with
the state-of-the-art SFDIA detection methods. In addition, an IoT-oriented
lightweight prototype of size 52 MB is implemented and tested for mobile
devices, which demonstrates the potential applications on mobile devices. The
trained model will be available at
\textit{https://github.com/HubYZ/PowerFDNet}.
