False Data Injection Cyber-Attacks Mitigation in Parallel DC/DC Converters based on Artificial Neural Networks

Mohammad Reza Habibi; Hamid Reza Baghaee; Tomislav Dragicevic; Frede Blaabjerg

doi:10.1109/TCSII.2020.3011324

False Data Injection Cyber-Attacks Mitigation in Parallel DC/DC Converters based on Artificial Neural Networks

Mohammad Reza Habibi, Hamid Reza Baghaee, Tomislav Dragicevic, Frede Blaabjerg

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › peer review

64 Citationer (Scopus)

Abstract

Because of the existence of communication networks and control applications, DC microgrids can be attacked by cyber-attackers. False data injection attack (FDIA) is one type of cyber-attacks where attackers try to inject false data to the target DC microgrid to destruct the control system. This brief discusses the effect of FDIAs in DC microgrids that are structured by parallel DC/DC converters and they are controlled by droop based control strategies to maintain the desired DC voltage level. Also, an effective and proper strategy based on an artificial neural network-based reference tracking application is introduced to remove the FDIAs in the DC microgrid.

Originalsprog	Engelsk
Artikelnummer	9146309
Tidsskrift	I E E E Transactions on Circuits and Systems. Part 2: Express Briefs
Vol/bind	68
Udgave nummer	2
Sider (fra-til)	717-721
Antal sider	5
ISSN	1549-7747
DOI	https://doi.org/10.1109/TCSII.2020.3011324
Status	Udgivet - feb. 2021

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10.1109/TCSII.2020.3011324

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title = "False Data Injection Cyber-Attacks Mitigation in Parallel DC/DC Converters based on Artificial Neural Networks",

abstract = "Because of the existence of communication networks and control applications, DC microgrids can be attacked by cyber-attackers. False data injection attack (FDIA) is one type of cyber-attacks where attackers try to inject false data to the target DC microgrid to destruct the control system. This brief discusses the effect of FDIAs in DC microgrids that are structured by parallel DC/DC converters and they are controlled by droop based control strategies to maintain the desired DC voltage level. Also, an effective and proper strategy based on an artificial neural network-based reference tracking application is introduced to remove the FDIAs in the DC microgrid.",

keywords = "Artificial neural networks, cyber-attack, DC microgrid, droop control, false data injection attack",

author = "Habibi, {Mohammad Reza} and Baghaee, {Hamid Reza} and Tomislav Dragicevic and Frede Blaabjerg",

year = "2021",

month = feb,

doi = "10.1109/TCSII.2020.3011324",

language = "English",

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journal = "I E E E Transactions on Circuits and Systems. Part 2: Express Briefs",

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number = "2",

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False Data Injection Cyber-Attacks Mitigation in Parallel DC/DC Converters based on Artificial Neural Networks. / Habibi, Mohammad Reza; Baghaee, Hamid Reza; Dragicevic, Tomislav et al.
I: I E E E Transactions on Circuits and Systems. Part 2: Express Briefs, Bind 68, Nr. 2, 9146309, 02.2021, s. 717-721.

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › peer review

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T1 - False Data Injection Cyber-Attacks Mitigation in Parallel DC/DC Converters based on Artificial Neural Networks

AU - Habibi, Mohammad Reza

AU - Baghaee, Hamid Reza

AU - Dragicevic, Tomislav

AU - Blaabjerg, Frede

PY - 2021/2

Y1 - 2021/2

N2 - Because of the existence of communication networks and control applications, DC microgrids can be attacked by cyber-attackers. False data injection attack (FDIA) is one type of cyber-attacks where attackers try to inject false data to the target DC microgrid to destruct the control system. This brief discusses the effect of FDIAs in DC microgrids that are structured by parallel DC/DC converters and they are controlled by droop based control strategies to maintain the desired DC voltage level. Also, an effective and proper strategy based on an artificial neural network-based reference tracking application is introduced to remove the FDIAs in the DC microgrid.

AB - Because of the existence of communication networks and control applications, DC microgrids can be attacked by cyber-attackers. False data injection attack (FDIA) is one type of cyber-attacks where attackers try to inject false data to the target DC microgrid to destruct the control system. This brief discusses the effect of FDIAs in DC microgrids that are structured by parallel DC/DC converters and they are controlled by droop based control strategies to maintain the desired DC voltage level. Also, an effective and proper strategy based on an artificial neural network-based reference tracking application is introduced to remove the FDIAs in the DC microgrid.

KW - Artificial neural networks

KW - cyber-attack

KW - DC microgrid

KW - droop control

KW - false data injection attack

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DO - 10.1109/TCSII.2020.3011324

M3 - Journal article

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JO - I E E E Transactions on Circuits and Systems. Part 2: Express Briefs

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False Data Injection Cyber-Attacks Mitigation in Parallel DC/DC Converters based on Artificial Neural Networks

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