Detection of Faults and Attacks Including False Data Injection Attack in Smart Grid Using Kalman Filter

• Published in: IEEE transactions on control of network systems Vol. 1; no. 4; pp. 370 - 379
• Main Authors: Manandhar, Kebina, Xiaojun Cao, Fei Hu, Yao Liu
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.12.2014; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Detectors; Kalman filters; Mathematical model; Mathematical models; Network security; Security; Smart grids; security; Kalman filter smart grid; Cyber physical system; false data injection attack
• ISSN: 2325-5870; 2325-5870; 2372-2533
• DOI: 10.1109/TCNS.2014.2357531

By exploiting the communication infrastructure among the sensors, actuators, and control systems, attackers may compromise the security of smart-grid systems, with techniques such as denial-of-service (DoS) attack, random attack, and data-injection attack. In this paper, we present a mathematical model of the system to study these pitfalls and propose a robust security framework for the smart grid. Our framework adopts the Kalman filter to estimate the variables of a wide range of state processes in the model. The estimates from the Kalman filter and the system readings are then fed into the χ 2 -detector or the proposed Euclidean detector. The χ 2 -detector is a proven effective exploratory method used with the Kalman filter for the measurement of the relationship between dependent variables and a series of predictor variables. The χ 2 -detector can detect system faults/attacks, such as DoS attack, short-term, and long-term random attacks. However, the studies show that the χ 2 -detector is unable to detect the statistically derived false data-injection attack. To overcome this limitation, we prove that the Euclidean detector can effectively detect such a sophisticated injection attack.