Extended Distributed State Estimation: A Detection Method against Tolerable False Data Injection Attacks in Smart Grids

False data injection (FDI) is considered to be one of the most dangerous cyber-attacks in smart grids, as it may lead to energy theft from end users, false dispatch in the distribution process, and device breakdown during power generation. In this paper, a novel kind of FDI attack, named tolerable f...
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Autor*in:	Dai Wang [verfasserIn] Xiaohong Guan [verfasserIn] Ting Liu [verfasserIn] Yun Gu [verfasserIn] Chao Shen [verfasserIn] Zhanbo Xu [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2014

Schlagwörter:	smart grids security false data injection (FDI) bad data detection extended distributed state estimation (EDSE)

Übergeordnetes Werk:	In: Energies - MDPI AG, 2008, 7(2014), 3, Seite 1517-1538
Übergeordnetes Werk:	volume:7 ; year:2014 ; number:3 ; pages:1517-1538

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/en7031517

Katalog-ID:	DOAJ031453872

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allfields	10.3390/en7031517 doi (DE-627)DOAJ031453872 (DE-599)DOAJd71923d80c5c497ca533e21f46ca4fab DE-627 ger DE-627 rakwb eng Dai Wang verfasserin aut Extended Distributed State Estimation: A Detection Method against Tolerable False Data Injection Attacks in Smart Grids 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier False data injection (FDI) is considered to be one of the most dangerous cyber-attacks in smart grids, as it may lead to energy theft from end users, false dispatch in the distribution process, and device breakdown during power generation. In this paper, a novel kind of FDI attack, named tolerable false data injection (TFDI), is constructed. Such attacks exploit the traditional detector’s tolerance of observation errors to bypass the traditional bad data detection. Then, a method based on extended distributed state estimation (EDSE) is proposed to detect TFDI in smart grids. The smart grid is decomposed into several subsystems, exploiting graph partition algorithms. Each subsystem is extended outward to include the adjacent buses and tie lines, and generate the extended subsystem. The Chi-squares test is applied to detect the false data in each extended subsystem. Through decomposition, the false data stands out distinctively from normal observation errors and the detection sensitivity is increased. Extensive TFDI attack cases are simulated in the Institute of Electrical and Electronics Engineers (IEEE) 14-, 39-, 118- and 300-bus systems. Simulation results show that the detection precision of the EDSE-based method is much higher than that of the traditional method, while the proposed method significantly reduces the associated computational costs. smart grids security false data injection (FDI) bad data detection extended distributed state estimation (EDSE) Technology T Xiaohong Guan verfasserin aut Ting Liu verfasserin aut Yun Gu verfasserin aut Chao Shen verfasserin aut Zhanbo Xu verfasserin aut In Energies MDPI AG, 2008 7(2014), 3, Seite 1517-1538 (DE-627)572083742 (DE-600)2437446-5 19961073 nnns volume:7 year:2014 number:3 pages:1517-1538 https://doi.org/10.3390/en7031517 kostenfrei https://doaj.org/article/d71923d80c5c497ca533e21f46ca4fab kostenfrei http://www.mdpi.com/1996-1073/7/3/1517 kostenfrei https://doaj.org/toc/1996-1073 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 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 7 2014 3 1517-1538
spelling	10.3390/en7031517 doi (DE-627)DOAJ031453872 (DE-599)DOAJd71923d80c5c497ca533e21f46ca4fab DE-627 ger DE-627 rakwb eng Dai Wang verfasserin aut Extended Distributed State Estimation: A Detection Method against Tolerable False Data Injection Attacks in Smart Grids 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier False data injection (FDI) is considered to be one of the most dangerous cyber-attacks in smart grids, as it may lead to energy theft from end users, false dispatch in the distribution process, and device breakdown during power generation. In this paper, a novel kind of FDI attack, named tolerable false data injection (TFDI), is constructed. Such attacks exploit the traditional detector’s tolerance of observation errors to bypass the traditional bad data detection. Then, a method based on extended distributed state estimation (EDSE) is proposed to detect TFDI in smart grids. The smart grid is decomposed into several subsystems, exploiting graph partition algorithms. Each subsystem is extended outward to include the adjacent buses and tie lines, and generate the extended subsystem. The Chi-squares test is applied to detect the false data in each extended subsystem. Through decomposition, the false data stands out distinctively from normal observation errors and the detection sensitivity is increased. Extensive TFDI attack cases are simulated in the Institute of Electrical and Electronics Engineers (IEEE) 14-, 39-, 118- and 300-bus systems. Simulation results show that the detection precision of the EDSE-based method is much higher than that of the traditional method, while the proposed method significantly reduces the associated computational costs. smart grids security false data injection (FDI) bad data detection extended distributed state estimation (EDSE) Technology T Xiaohong Guan verfasserin aut Ting Liu verfasserin aut Yun Gu verfasserin aut Chao Shen verfasserin aut Zhanbo Xu verfasserin aut In Energies MDPI AG, 2008 7(2014), 3, Seite 1517-1538 (DE-627)572083742 (DE-600)2437446-5 19961073 nnns volume:7 year:2014 number:3 pages:1517-1538 https://doi.org/10.3390/en7031517 kostenfrei https://doaj.org/article/d71923d80c5c497ca533e21f46ca4fab kostenfrei http://www.mdpi.com/1996-1073/7/3/1517 kostenfrei https://doaj.org/toc/1996-1073 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 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 7 2014 3 1517-1538
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allfieldsGer	10.3390/en7031517 doi (DE-627)DOAJ031453872 (DE-599)DOAJd71923d80c5c497ca533e21f46ca4fab DE-627 ger DE-627 rakwb eng Dai Wang verfasserin aut Extended Distributed State Estimation: A Detection Method against Tolerable False Data Injection Attacks in Smart Grids 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier False data injection (FDI) is considered to be one of the most dangerous cyber-attacks in smart grids, as it may lead to energy theft from end users, false dispatch in the distribution process, and device breakdown during power generation. In this paper, a novel kind of FDI attack, named tolerable false data injection (TFDI), is constructed. Such attacks exploit the traditional detector’s tolerance of observation errors to bypass the traditional bad data detection. Then, a method based on extended distributed state estimation (EDSE) is proposed to detect TFDI in smart grids. The smart grid is decomposed into several subsystems, exploiting graph partition algorithms. Each subsystem is extended outward to include the adjacent buses and tie lines, and generate the extended subsystem. The Chi-squares test is applied to detect the false data in each extended subsystem. Through decomposition, the false data stands out distinctively from normal observation errors and the detection sensitivity is increased. Extensive TFDI attack cases are simulated in the Institute of Electrical and Electronics Engineers (IEEE) 14-, 39-, 118- and 300-bus systems. Simulation results show that the detection precision of the EDSE-based method is much higher than that of the traditional method, while the proposed method significantly reduces the associated computational costs. smart grids security false data injection (FDI) bad data detection extended distributed state estimation (EDSE) Technology T Xiaohong Guan verfasserin aut Ting Liu verfasserin aut Yun Gu verfasserin aut Chao Shen verfasserin aut Zhanbo Xu verfasserin aut In Energies MDPI AG, 2008 7(2014), 3, Seite 1517-1538 (DE-627)572083742 (DE-600)2437446-5 19961073 nnns volume:7 year:2014 number:3 pages:1517-1538 https://doi.org/10.3390/en7031517 kostenfrei https://doaj.org/article/d71923d80c5c497ca533e21f46ca4fab kostenfrei http://www.mdpi.com/1996-1073/7/3/1517 kostenfrei https://doaj.org/toc/1996-1073 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 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 7 2014 3 1517-1538
allfieldsSound	10.3390/en7031517 doi (DE-627)DOAJ031453872 (DE-599)DOAJd71923d80c5c497ca533e21f46ca4fab DE-627 ger DE-627 rakwb eng Dai Wang verfasserin aut Extended Distributed State Estimation: A Detection Method against Tolerable False Data Injection Attacks in Smart Grids 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier False data injection (FDI) is considered to be one of the most dangerous cyber-attacks in smart grids, as it may lead to energy theft from end users, false dispatch in the distribution process, and device breakdown during power generation. In this paper, a novel kind of FDI attack, named tolerable false data injection (TFDI), is constructed. Such attacks exploit the traditional detector’s tolerance of observation errors to bypass the traditional bad data detection. Then, a method based on extended distributed state estimation (EDSE) is proposed to detect TFDI in smart grids. The smart grid is decomposed into several subsystems, exploiting graph partition algorithms. Each subsystem is extended outward to include the adjacent buses and tie lines, and generate the extended subsystem. The Chi-squares test is applied to detect the false data in each extended subsystem. Through decomposition, the false data stands out distinctively from normal observation errors and the detection sensitivity is increased. Extensive TFDI attack cases are simulated in the Institute of Electrical and Electronics Engineers (IEEE) 14-, 39-, 118- and 300-bus systems. Simulation results show that the detection precision of the EDSE-based method is much higher than that of the traditional method, while the proposed method significantly reduces the associated computational costs. smart grids security false data injection (FDI) bad data detection extended distributed state estimation (EDSE) Technology T Xiaohong Guan verfasserin aut Ting Liu verfasserin aut Yun Gu verfasserin aut Chao Shen verfasserin aut Zhanbo Xu verfasserin aut In Energies MDPI AG, 2008 7(2014), 3, Seite 1517-1538 (DE-627)572083742 (DE-600)2437446-5 19961073 nnns volume:7 year:2014 number:3 pages:1517-1538 https://doi.org/10.3390/en7031517 kostenfrei https://doaj.org/article/d71923d80c5c497ca533e21f46ca4fab kostenfrei http://www.mdpi.com/1996-1073/7/3/1517 kostenfrei https://doaj.org/toc/1996-1073 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 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 7 2014 3 1517-1538
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author	Dai Wang
spellingShingle	Dai Wang misc smart grids misc security misc false data injection (FDI) misc bad data detection misc extended distributed state estimation (EDSE) misc Technology misc T Extended Distributed State Estimation: A Detection Method against Tolerable False Data Injection Attacks in Smart Grids
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topic_title	Extended Distributed State Estimation: A Detection Method against Tolerable False Data Injection Attacks in Smart Grids smart grids security false data injection (FDI) bad data detection extended distributed state estimation (EDSE)
topic	misc smart grids misc security misc false data injection (FDI) misc bad data detection misc extended distributed state estimation (EDSE) misc Technology misc T
topic_unstemmed	misc smart grids misc security misc false data injection (FDI) misc bad data detection misc extended distributed state estimation (EDSE) misc Technology misc T
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title	Extended Distributed State Estimation: A Detection Method against Tolerable False Data Injection Attacks in Smart Grids
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title_sort	extended distributed state estimation: a detection method against tolerable false data injection attacks in smart grids
title_auth	Extended Distributed State Estimation: A Detection Method against Tolerable False Data Injection Attacks in Smart Grids
abstract	False data injection (FDI) is considered to be one of the most dangerous cyber-attacks in smart grids, as it may lead to energy theft from end users, false dispatch in the distribution process, and device breakdown during power generation. In this paper, a novel kind of FDI attack, named tolerable false data injection (TFDI), is constructed. Such attacks exploit the traditional detector’s tolerance of observation errors to bypass the traditional bad data detection. Then, a method based on extended distributed state estimation (EDSE) is proposed to detect TFDI in smart grids. The smart grid is decomposed into several subsystems, exploiting graph partition algorithms. Each subsystem is extended outward to include the adjacent buses and tie lines, and generate the extended subsystem. The Chi-squares test is applied to detect the false data in each extended subsystem. Through decomposition, the false data stands out distinctively from normal observation errors and the detection sensitivity is increased. Extensive TFDI attack cases are simulated in the Institute of Electrical and Electronics Engineers (IEEE) 14-, 39-, 118- and 300-bus systems. Simulation results show that the detection precision of the EDSE-based method is much higher than that of the traditional method, while the proposed method significantly reduces the associated computational costs.
abstractGer	False data injection (FDI) is considered to be one of the most dangerous cyber-attacks in smart grids, as it may lead to energy theft from end users, false dispatch in the distribution process, and device breakdown during power generation. In this paper, a novel kind of FDI attack, named tolerable false data injection (TFDI), is constructed. Such attacks exploit the traditional detector’s tolerance of observation errors to bypass the traditional bad data detection. Then, a method based on extended distributed state estimation (EDSE) is proposed to detect TFDI in smart grids. The smart grid is decomposed into several subsystems, exploiting graph partition algorithms. Each subsystem is extended outward to include the adjacent buses and tie lines, and generate the extended subsystem. The Chi-squares test is applied to detect the false data in each extended subsystem. Through decomposition, the false data stands out distinctively from normal observation errors and the detection sensitivity is increased. Extensive TFDI attack cases are simulated in the Institute of Electrical and Electronics Engineers (IEEE) 14-, 39-, 118- and 300-bus systems. Simulation results show that the detection precision of the EDSE-based method is much higher than that of the traditional method, while the proposed method significantly reduces the associated computational costs.
abstract_unstemmed	False data injection (FDI) is considered to be one of the most dangerous cyber-attacks in smart grids, as it may lead to energy theft from end users, false dispatch in the distribution process, and device breakdown during power generation. In this paper, a novel kind of FDI attack, named tolerable false data injection (TFDI), is constructed. Such attacks exploit the traditional detector’s tolerance of observation errors to bypass the traditional bad data detection. Then, a method based on extended distributed state estimation (EDSE) is proposed to detect TFDI in smart grids. The smart grid is decomposed into several subsystems, exploiting graph partition algorithms. Each subsystem is extended outward to include the adjacent buses and tie lines, and generate the extended subsystem. The Chi-squares test is applied to detect the false data in each extended subsystem. Through decomposition, the false data stands out distinctively from normal observation errors and the detection sensitivity is increased. Extensive TFDI attack cases are simulated in the Institute of Electrical and Electronics Engineers (IEEE) 14-, 39-, 118- and 300-bus systems. Simulation results show that the detection precision of the EDSE-based method is much higher than that of the traditional method, while the proposed method significantly reduces the associated computational costs.
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title_short	Extended Distributed State Estimation: A Detection Method against Tolerable False Data Injection Attacks in Smart Grids
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up_date	2024-07-03T20:40:48.515Z
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Extended Distributed State Estimation: A Detection Method against Tolerable False Data Injection Attacks in Smart Grids

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