@inproceedings {318,
	title = {Kalman Filter Based Secure State Estimation and Individual Attacked Sensor Detection in Cyber-Physical Systems},
	booktitle = {Annual American Control Conference (ACC)},
	year = {2019},
	address = {Philadelphia, USA},
	abstract = {<p>In this paper we propose two real-time attack detection and secure state estimation algorithms, namely Rolling Window Detector (RWD) and Novel Residual Detector (NRD). \&nbsp;These algorithms are basically developed based on Kalman state estimation. In the former, we present a statistical testing approach which is handled over a finite time horizon T to detect individual attacked sensors. The latter extends the χ2 \&ndash;detector to be able to detect individual compromised sensors. Both methods then will be employed together with a modified version of Kalman filter to perform a secure state estimation with a relatively low estimation error. Efficiency of the algorithms will be assessed in both unstealthy and stealthy scenarios. \&nbsp;Productivity of the methods will be underlined in the stealthy case, which is of much more significance among cyber-security challenges. Simulation results on an IEEE 14-bus power grid test system along with a comprehensive comparison between the performance of RWD and NRD with a recently introduced tool, which is the only other method that tries to detect individual attacked sensors, proves the effectiveness of the algorithms.</p>
},
	author = {M.H. Basiri and J.G. Thistle and J.W. Simpson-Porco and S. Fischmeister}
}
