Joint transformation based detection of false data injection attacks in smart grid
Links to Fileshttps://ieeexplore.ieee.org/document/7961272
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Type of Work9 pages
journal article pre-print
Citation of Original PublicationJames J. Q. Yu, Yunhe Hou, Victor O. K. Li, "Online False Data Injection Attack Detection With Wavelet Transform and Deep Neural Networks", Industrial Informatics IEEE Transactions on, vol. 14, no. 7, pp. 3271-3280, 2018,DOI: 10.1109/TII.2017.2720726 .
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© 2017 IEEE
false data injection
Kullback- Leibler distance
UMBC Ebiquity Research Group
For reliable operation and control of smart grid, estimating the correct states is of utmost importance to the system operator. With recent incorporation of information technology and advanced metering infrastructure, the futuristic grid is more prone to cyber-threats. The false data injection (FDI) attack is one of the most thoroughly researched cyber-attacks. Intelligently crafted, it can cause false estimation of states, which further seriously affects the entire power system operation. In this paper, we propose joint-transformation-based scheme to detect FDI attacks in real time. The proposed method is built on the dynamics of measurement variations. Kullback-Leibler distance is used to find out the difference between probability distributions obtained from measurement variations. The proposed method is tested using IEEE 14 bus system considering attack on different state variables. The results shows that the proposed scheme detects FDI attacks with high detection probability