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Detection of Faults and Attacks Including False Data Injection Attack in Smart Grid Using Kalman Filter | IEEE Journals & Magazine | IEEE Xplore

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


Abstract:

By exploiting the communication infrastructure among the sensors, actuators, and control systems, attackers may compromise the security of smart-grid systems, with techni...Show More

Abstract:

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 \chi^{2}-detector or the proposed Euclidean detector. The \chi^{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 \chi^{2}-detector can detect system faults/attacks, such as DoS attack, short-term, and long-term random attacks. However, the studies show that the \chi^{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.
Published in: IEEE Transactions on Control of Network Systems ( Volume: 1, Issue: 4, December 2014)
Page(s): 370 - 379
Date of Publication: 12 September 2014

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