Detection of false data injection attacks using unscented Kalman filter

Abstract It has recently been shown that state estimation (SE), which is the most important real-time function in modern energy management systems (EMSs), is vulnerable to false data injection attacks, due to the undetectability of those attacks using standard bad data detection techniques, which ar...
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Gespeichert in:

Autor*in:	ŽIVKOVIĆ, Nemanja [verfasserIn] SARIĆ, Andrija T.

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2018

Schlagwörter:	State estimation False data injection attack Bad data detection Unscented Kalman filter

Anmerkung:	© The Author(s) 2018

Übergeordnetes Werk:	Enthalten in: Journal of modern power systems and clean energy - Nanjing : NARI, 2013, 6(2018), 5 vom: 07. Mai, Seite 847-859
Übergeordnetes Werk:	volume:6 ; year:2018 ; number:5 ; day:07 ; month:05 ; pages:847-859

Links:	Volltext

DOI / URN:	10.1007/s40565-018-0413-5

Katalog-ID:	SPR036672599

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allfields	10.1007/s40565-018-0413-5 doi (DE-627)SPR036672599 (SPR)s40565-018-0413-5-e DE-627 ger DE-627 rakwb eng ŽIVKOVIĆ, Nemanja verfasserin aut Detection of false data injection attacks using unscented Kalman filter 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2018 Abstract It has recently been shown that state estimation (SE), which is the most important real-time function in modern energy management systems (EMSs), is vulnerable to false data injection attacks, due to the undetectability of those attacks using standard bad data detection techniques, which are typically based on normalized measurement residuals. Therefore, it is of the utmost importance to develop novel and efficient methods that are capable of detecting such malicious attacks. In this paper, we propose using the unscented Kalman filter (UKF) in conjunction with a weighted least square (WLS) based SE algorithm in real-time, to detect discrepancies between SV estimates and, as a consequence, to identify false data attacks. After an attack is detected and an appropriate alarm is raised, an operator can take actions to prevent or minimize the potential consequences. The proposed algorithm was successfully tested on benchmark IEEE 14-bus and 300-bus test systems, making it suitable for implementation in commercial EMS software. State estimation (dpeaa)DE-He213 False data injection attack (dpeaa)DE-He213 Bad data detection (dpeaa)DE-He213 Unscented Kalman filter (dpeaa)DE-He213 SARIĆ, Andrija T. aut Enthalten in Journal of modern power systems and clean energy Nanjing : NARI, 2013 6(2018), 5 vom: 07. Mai, Seite 847-859 (DE-627)75682821X (DE-600)2727912-1 2196-5420 nnns volume:6 year:2018 number:5 day:07 month:05 pages:847-859 https://dx.doi.org/10.1007/s40565-018-0413-5 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2018 5 07 05 847-859
spelling	10.1007/s40565-018-0413-5 doi (DE-627)SPR036672599 (SPR)s40565-018-0413-5-e DE-627 ger DE-627 rakwb eng ŽIVKOVIĆ, Nemanja verfasserin aut Detection of false data injection attacks using unscented Kalman filter 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2018 Abstract It has recently been shown that state estimation (SE), which is the most important real-time function in modern energy management systems (EMSs), is vulnerable to false data injection attacks, due to the undetectability of those attacks using standard bad data detection techniques, which are typically based on normalized measurement residuals. Therefore, it is of the utmost importance to develop novel and efficient methods that are capable of detecting such malicious attacks. In this paper, we propose using the unscented Kalman filter (UKF) in conjunction with a weighted least square (WLS) based SE algorithm in real-time, to detect discrepancies between SV estimates and, as a consequence, to identify false data attacks. After an attack is detected and an appropriate alarm is raised, an operator can take actions to prevent or minimize the potential consequences. The proposed algorithm was successfully tested on benchmark IEEE 14-bus and 300-bus test systems, making it suitable for implementation in commercial EMS software. State estimation (dpeaa)DE-He213 False data injection attack (dpeaa)DE-He213 Bad data detection (dpeaa)DE-He213 Unscented Kalman filter (dpeaa)DE-He213 SARIĆ, Andrija T. aut Enthalten in Journal of modern power systems and clean energy Nanjing : NARI, 2013 6(2018), 5 vom: 07. Mai, Seite 847-859 (DE-627)75682821X (DE-600)2727912-1 2196-5420 nnns volume:6 year:2018 number:5 day:07 month:05 pages:847-859 https://dx.doi.org/10.1007/s40565-018-0413-5 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2018 5 07 05 847-859
allfields_unstemmed	10.1007/s40565-018-0413-5 doi (DE-627)SPR036672599 (SPR)s40565-018-0413-5-e DE-627 ger DE-627 rakwb eng ŽIVKOVIĆ, Nemanja verfasserin aut Detection of false data injection attacks using unscented Kalman filter 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2018 Abstract It has recently been shown that state estimation (SE), which is the most important real-time function in modern energy management systems (EMSs), is vulnerable to false data injection attacks, due to the undetectability of those attacks using standard bad data detection techniques, which are typically based on normalized measurement residuals. Therefore, it is of the utmost importance to develop novel and efficient methods that are capable of detecting such malicious attacks. In this paper, we propose using the unscented Kalman filter (UKF) in conjunction with a weighted least square (WLS) based SE algorithm in real-time, to detect discrepancies between SV estimates and, as a consequence, to identify false data attacks. After an attack is detected and an appropriate alarm is raised, an operator can take actions to prevent or minimize the potential consequences. The proposed algorithm was successfully tested on benchmark IEEE 14-bus and 300-bus test systems, making it suitable for implementation in commercial EMS software. State estimation (dpeaa)DE-He213 False data injection attack (dpeaa)DE-He213 Bad data detection (dpeaa)DE-He213 Unscented Kalman filter (dpeaa)DE-He213 SARIĆ, Andrija T. aut Enthalten in Journal of modern power systems and clean energy Nanjing : NARI, 2013 6(2018), 5 vom: 07. Mai, Seite 847-859 (DE-627)75682821X (DE-600)2727912-1 2196-5420 nnns volume:6 year:2018 number:5 day:07 month:05 pages:847-859 https://dx.doi.org/10.1007/s40565-018-0413-5 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2018 5 07 05 847-859
allfieldsGer	10.1007/s40565-018-0413-5 doi (DE-627)SPR036672599 (SPR)s40565-018-0413-5-e DE-627 ger DE-627 rakwb eng ŽIVKOVIĆ, Nemanja verfasserin aut Detection of false data injection attacks using unscented Kalman filter 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2018 Abstract It has recently been shown that state estimation (SE), which is the most important real-time function in modern energy management systems (EMSs), is vulnerable to false data injection attacks, due to the undetectability of those attacks using standard bad data detection techniques, which are typically based on normalized measurement residuals. Therefore, it is of the utmost importance to develop novel and efficient methods that are capable of detecting such malicious attacks. In this paper, we propose using the unscented Kalman filter (UKF) in conjunction with a weighted least square (WLS) based SE algorithm in real-time, to detect discrepancies between SV estimates and, as a consequence, to identify false data attacks. After an attack is detected and an appropriate alarm is raised, an operator can take actions to prevent or minimize the potential consequences. The proposed algorithm was successfully tested on benchmark IEEE 14-bus and 300-bus test systems, making it suitable for implementation in commercial EMS software. State estimation (dpeaa)DE-He213 False data injection attack (dpeaa)DE-He213 Bad data detection (dpeaa)DE-He213 Unscented Kalman filter (dpeaa)DE-He213 SARIĆ, Andrija T. aut Enthalten in Journal of modern power systems and clean energy Nanjing : NARI, 2013 6(2018), 5 vom: 07. Mai, Seite 847-859 (DE-627)75682821X (DE-600)2727912-1 2196-5420 nnns volume:6 year:2018 number:5 day:07 month:05 pages:847-859 https://dx.doi.org/10.1007/s40565-018-0413-5 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2018 5 07 05 847-859
allfieldsSound	10.1007/s40565-018-0413-5 doi (DE-627)SPR036672599 (SPR)s40565-018-0413-5-e DE-627 ger DE-627 rakwb eng ŽIVKOVIĆ, Nemanja verfasserin aut Detection of false data injection attacks using unscented Kalman filter 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2018 Abstract It has recently been shown that state estimation (SE), which is the most important real-time function in modern energy management systems (EMSs), is vulnerable to false data injection attacks, due to the undetectability of those attacks using standard bad data detection techniques, which are typically based on normalized measurement residuals. Therefore, it is of the utmost importance to develop novel and efficient methods that are capable of detecting such malicious attacks. In this paper, we propose using the unscented Kalman filter (UKF) in conjunction with a weighted least square (WLS) based SE algorithm in real-time, to detect discrepancies between SV estimates and, as a consequence, to identify false data attacks. After an attack is detected and an appropriate alarm is raised, an operator can take actions to prevent or minimize the potential consequences. The proposed algorithm was successfully tested on benchmark IEEE 14-bus and 300-bus test systems, making it suitable for implementation in commercial EMS software. State estimation (dpeaa)DE-He213 False data injection attack (dpeaa)DE-He213 Bad data detection (dpeaa)DE-He213 Unscented Kalman filter (dpeaa)DE-He213 SARIĆ, Andrija T. aut Enthalten in Journal of modern power systems and clean energy Nanjing : NARI, 2013 6(2018), 5 vom: 07. Mai, Seite 847-859 (DE-627)75682821X (DE-600)2727912-1 2196-5420 nnns volume:6 year:2018 number:5 day:07 month:05 pages:847-859 https://dx.doi.org/10.1007/s40565-018-0413-5 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2018 5 07 05 847-859
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source	Enthalten in Journal of modern power systems and clean energy 6(2018), 5 vom: 07. Mai, Seite 847-859 volume:6 year:2018 number:5 day:07 month:05 pages:847-859
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topic_facet	State estimation False data injection attack Bad data detection Unscented Kalman filter
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author	ŽIVKOVIĆ, Nemanja
spellingShingle	ŽIVKOVIĆ, Nemanja misc State estimation misc False data injection attack misc Bad data detection misc Unscented Kalman filter Detection of false data injection attacks using unscented Kalman filter
authorStr	ŽIVKOVIĆ, Nemanja
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topic_title	Detection of false data injection attacks using unscented Kalman filter State estimation (dpeaa)DE-He213 False data injection attack (dpeaa)DE-He213 Bad data detection (dpeaa)DE-He213 Unscented Kalman filter (dpeaa)DE-He213
topic	misc State estimation misc False data injection attack misc Bad data detection misc Unscented Kalman filter
topic_unstemmed	misc State estimation misc False data injection attack misc Bad data detection misc Unscented Kalman filter
topic_browse	misc State estimation misc False data injection attack misc Bad data detection misc Unscented Kalman filter
format_facet	Elektronische Aufsätze Aufsätze Elektronische Ressource
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title	Detection of false data injection attacks using unscented Kalman filter
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title_full	Detection of false data injection attacks using unscented Kalman filter
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author_browse	ŽIVKOVIĆ, Nemanja SARIĆ, Andrija T.
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author-letter	ŽIVKOVIĆ, Nemanja
doi_str_mv	10.1007/s40565-018-0413-5
title_sort	detection of false data injection attacks using unscented kalman filter
title_auth	Detection of false data injection attacks using unscented Kalman filter
abstract	Abstract It has recently been shown that state estimation (SE), which is the most important real-time function in modern energy management systems (EMSs), is vulnerable to false data injection attacks, due to the undetectability of those attacks using standard bad data detection techniques, which are typically based on normalized measurement residuals. Therefore, it is of the utmost importance to develop novel and efficient methods that are capable of detecting such malicious attacks. In this paper, we propose using the unscented Kalman filter (UKF) in conjunction with a weighted least square (WLS) based SE algorithm in real-time, to detect discrepancies between SV estimates and, as a consequence, to identify false data attacks. After an attack is detected and an appropriate alarm is raised, an operator can take actions to prevent or minimize the potential consequences. The proposed algorithm was successfully tested on benchmark IEEE 14-bus and 300-bus test systems, making it suitable for implementation in commercial EMS software. © The Author(s) 2018
abstractGer	Abstract It has recently been shown that state estimation (SE), which is the most important real-time function in modern energy management systems (EMSs), is vulnerable to false data injection attacks, due to the undetectability of those attacks using standard bad data detection techniques, which are typically based on normalized measurement residuals. Therefore, it is of the utmost importance to develop novel and efficient methods that are capable of detecting such malicious attacks. In this paper, we propose using the unscented Kalman filter (UKF) in conjunction with a weighted least square (WLS) based SE algorithm in real-time, to detect discrepancies between SV estimates and, as a consequence, to identify false data attacks. After an attack is detected and an appropriate alarm is raised, an operator can take actions to prevent or minimize the potential consequences. The proposed algorithm was successfully tested on benchmark IEEE 14-bus and 300-bus test systems, making it suitable for implementation in commercial EMS software. © The Author(s) 2018
abstract_unstemmed	Abstract It has recently been shown that state estimation (SE), which is the most important real-time function in modern energy management systems (EMSs), is vulnerable to false data injection attacks, due to the undetectability of those attacks using standard bad data detection techniques, which are typically based on normalized measurement residuals. Therefore, it is of the utmost importance to develop novel and efficient methods that are capable of detecting such malicious attacks. In this paper, we propose using the unscented Kalman filter (UKF) in conjunction with a weighted least square (WLS) based SE algorithm in real-time, to detect discrepancies between SV estimates and, as a consequence, to identify false data attacks. After an attack is detected and an appropriate alarm is raised, an operator can take actions to prevent or minimize the potential consequences. The proposed algorithm was successfully tested on benchmark IEEE 14-bus and 300-bus test systems, making it suitable for implementation in commercial EMS software. © The Author(s) 2018
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title_short	Detection of false data injection attacks using unscented Kalman filter
url	https://dx.doi.org/10.1007/s40565-018-0413-5
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up_date	2024-07-03T19:00:53.614Z
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