TY - JOUR
T1 - Achieving efficient detection against false data injection attacks in smart grid
AU - Xu, Ruzhi
AU - Wang, Rui
AU - Guan, Zhitao
AU - Wu, Longfei
AU - Wu, Jun
AU - Du, Xiaojiang
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2017/7/18
Y1 - 2017/7/18
N2 - Internet of Things (IoT) technologies have been broadly applied in smart grid for monitoring physical or environmental conditions. Especially, state estimation is an important IoT-based application in smart grid, which is used in system monitoring to get the best estimate of the power grid state through an analysis of the meter measurements and power system topologies. However, false data injection attack (FDIA) is a severe threat to state estimation, which is known for the difficulty of detection. In this paper, we propose an efficient detection scheme against FDIA. First, two parameters that reflect the physical property of smart grid are investigated. One parameter is the control signal from the controller to the static Var compensator (CSSVC). A large CSSVC indicates there exists the intense voltage fluctuation. The other parameter is the quantitative node voltage stability index (NVSI). A larger NVSI indicates a higher vulnerability level. Second, according to the values of the CSSVC and NVSI, an optimized clustering algorithm is proposed to distribute the potential vulnerable nodes into several classes. Finally, based on these classes, a detection method is proposed for the real-time detection of the FDIA. The simulation results show that the proposed scheme can detect the FDIA effectively.
AB - Internet of Things (IoT) technologies have been broadly applied in smart grid for monitoring physical or environmental conditions. Especially, state estimation is an important IoT-based application in smart grid, which is used in system monitoring to get the best estimate of the power grid state through an analysis of the meter measurements and power system topologies. However, false data injection attack (FDIA) is a severe threat to state estimation, which is known for the difficulty of detection. In this paper, we propose an efficient detection scheme against FDIA. First, two parameters that reflect the physical property of smart grid are investigated. One parameter is the control signal from the controller to the static Var compensator (CSSVC). A large CSSVC indicates there exists the intense voltage fluctuation. The other parameter is the quantitative node voltage stability index (NVSI). A larger NVSI indicates a higher vulnerability level. Second, according to the values of the CSSVC and NVSI, an optimized clustering algorithm is proposed to distribute the potential vulnerable nodes into several classes. Finally, based on these classes, a detection method is proposed for the real-time detection of the FDIA. The simulation results show that the proposed scheme can detect the FDIA effectively.
KW - Smart grid
KW - control signal
KW - false data injection attack
KW - node voltage stability index
KW - state estimation
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U2 - 10.1109/ACCESS.2017.2728681
DO - 10.1109/ACCESS.2017.2728681
M3 - Article
AN - SCOPUS:85028915543
VL - 5
SP - 13787
EP - 13798
JO - IEEE Access
JF - IEEE Access
M1 - 7983355
ER -