  The smart grid combines the classical power system with information
technology, leading to a cyber-physical system. In such an environment the
malicious injection of data has the potential to cause severe consequences.
Classical residual-based methods for bad data detection are unable to detect
well designed false data injection (FDI) attacks. Moreover, most work on FDI
attack detection is based on the linearized DC model of the power system and
fails to detect attacks based on the AC model. The aim of this paper is to
address these problems by using the graph structure of the grid and the AC
power flow model. We derive an attack detection method that is able to detect
previously undetectable FDI attacks. This method is based on concepts
originating from graph signal processing (GSP). The proposed detection scheme
calculates the graph Fourier transform of an estimated grid state and filters
the graph's high-frequency components. By comparing the maximum norm of this
outcome with a threshold we can detect the presence of FDI attacks. Case
studies on the IEEE 14-bus system demonstrate that the proposed method is able
to detect a wide range of previously undetectable attacks, both on angles and
on magnitudes of the voltages.
