Singh, Sandeep KumarKhanna, KushBose, RanjanJoshi, Anupam2018-10-182018-10-182017-06-28James 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 .10.1109/TII.2017.2720726http://hdl.handle.net/11603/11595For 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 probability9 pagesen-USThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.© 2017 IEEECyber securityfalse data injectionKullback- Leibler distancesmart gridUMBC Ebiquity Research GroupText