TY - GEN
T1 - Detection of false data injection attacks in power systems with graph fourier transform
AU - Drayer, Elisabeth
AU - Routtenberg, Tirza
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/2
Y1 - 2018/7/2
N2 - With the transition from the hardware dominated analog power system to a digitized cyber-physical »smart grid», protection from attacks from the cyber domain has become increasingly important. In particular, malicious injection of false data has the potential to cause severe consequences. Classical residual-based methods for bad data detection are unable to detect a well designed false data injection (FDI) attack, which is based on detailed knowledge of the system topology. The aim of this paper is to overcome this limitation by making use of the inherent graph structure of the grid. Based on approaches developed for signal processing on graphs and on analysis of the spectrum of the graph, the proposed method enables the detection of previously undetectable FDI attacks. The main requirement for the proposed detector is that the estimated grid state is smooth with respect to the underlying weighted graph determined by the admittance matrix, that is, it has a low variation. Then, detection based on analysis of the high frequency components of the graph Fourier transform may be possible, as a function of the underlying topology of the grid. The feasibility of this idea is demonstrated with a case study on the IEEE 14-bus test grid.
AB - With the transition from the hardware dominated analog power system to a digitized cyber-physical »smart grid», protection from attacks from the cyber domain has become increasingly important. In particular, malicious injection of false data has the potential to cause severe consequences. Classical residual-based methods for bad data detection are unable to detect a well designed false data injection (FDI) attack, which is based on detailed knowledge of the system topology. The aim of this paper is to overcome this limitation by making use of the inherent graph structure of the grid. Based on approaches developed for signal processing on graphs and on analysis of the spectrum of the graph, the proposed method enables the detection of previously undetectable FDI attacks. The main requirement for the proposed detector is that the estimated grid state is smooth with respect to the underlying weighted graph determined by the admittance matrix, that is, it has a low variation. Then, detection based on analysis of the high frequency components of the graph Fourier transform may be possible, as a function of the underlying topology of the grid. The feasibility of this idea is demonstrated with a case study on the IEEE 14-bus test grid.
KW - Bad data detection
KW - False data injection
KW - Graph Fourier transform
KW - Graph signal processing
KW - Power system
UR - http://www.scopus.com/inward/record.url?scp=85063079840&partnerID=8YFLogxK
U2 - 10.1109/GlobalSIP.2018.8646454
DO - 10.1109/GlobalSIP.2018.8646454
M3 - Conference contribution
AN - SCOPUS:85063079840
T3 - 2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings
SP - 890
EP - 894
BT - 2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers
T2 - 2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018
Y2 - 26 November 2018 through 29 November 2018
ER -