Detection of Faults and Attacks Including False Data Injection Attack in Smart Grid Using Kalman Filter
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 mo...
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| Published in | IEEE transactions on control of network systems Vol. 1; no. 4; pp. 370 - 379 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Piscataway
IEEE
01.12.2014
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects | |
| Online Access | Get full text |
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| 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 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. |
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| AbstractList | 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. 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 [Formula Omitted]-detector or the proposed Euclidean detector. The [Formula Omitted]-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 [Formula Omitted]-detector can detect system faults/attacks, such as DoS attack, short-term, and long-term random attacks. However, the studies show that the [Formula Omitted]-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. |
| Author | Fei Hu Xiaojun Cao Yao Liu Manandhar, Kebina |
| Author_xml | – sequence: 1 givenname: Kebina surname: Manandhar fullname: Manandhar, Kebina email: kmanandhar1@cs.gsu.edu organization: Dept. of Comput. Sci., Georgia State Univ., Atlanta, GA, USA – sequence: 2 surname: Xiaojun Cao fullname: Xiaojun Cao email: cao@cs.gsu.edu organization: Dept. of Comput. Sci., Georgia State Univ., Atlanta, GA, USA – sequence: 3 surname: Fei Hu fullname: Fei Hu email: fei@eng.ua.edu organization: Dept. of Electr. & Comput. Eng., Univ. of Alabama, Tuscaloosa, AL, USA – sequence: 4 surname: Yao Liu fullname: Yao Liu email: yliu@cse.usf.edu organization: Dept. of Comput. Sci. & Eng., Univ. of South Florida, Tampa, FL, USA |
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| CODEN | ITCNAY |
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| Cites_doi | 10.1109/TSG.2011.2138172 10.1109/TAC.1987.1104658 10.1145/1952982.1952995 10.1109/EPEC.2009.5420809 10.1109/TSG.2011.2161498 10.1109/TSG.2011.2160297 10.1109/TSG.2011.2159406 10.1109/TSG.2011.2159818 10.1109/TSG.2011.2159819 10.1115/1.3662552 10.1109/CDC.2010.5718158 10.1109/45.283885 10.1109/TSG.2011.2165090 10.1007/978-3-642-00602-9_3 10.1109/TSG.2011.2167354 10.1109/TPWRS.2010.2051168 10.1109/TSG.2011.2160661 10.1109/TSG.2011.2159817 |
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| SubjectTerms | Detectors Kalman filters Mathematical model Mathematical models Network security Security Smart grids |
| Title | Detection of Faults and Attacks Including False Data Injection Attack in Smart Grid Using Kalman Filter |
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