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

Research output: Contribution to journal › Journal article › Research › peer-review

64 Citations (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.

Original language	English
Article number	9146309
Journal	I E E E Transactions on Circuits and Systems. Part 2: Express Briefs
Volume	68
Issue number	2
Pages (from-to)	717-721
Number of pages	5
ISSN	1549-7747
DOIs	https://doi.org/10.1109/TCSII.2020.3011324
Publication status	Published - Feb 2021

Keywords

Artificial neural networks
cyber-attack
DC microgrid
droop control
false data injection attack

Access to Document

10.1109/TCSII.2020.3011324

AUB Link

Search for the material in Aalborg University Library's search engine

Cite this

@article{00e7004bf2654efdb192812970e2baa5,

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",

volume = "68",

pages = "717--721",

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

issn = "1549-7747",

publisher = "IEEE",

number = "2",

}

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.
In: I E E E Transactions on Circuits and Systems. Part 2: Express Briefs, Vol. 68, No. 2, 9146309, 02.2021, p. 717-721.

Research output: Contribution to journal › Journal article › Research › peer-review

TY - JOUR

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

UR - http://www.scopus.com/inward/record.url?scp=85100252678&partnerID=8YFLogxK

U2 - 10.1109/TCSII.2020.3011324

DO - 10.1109/TCSII.2020.3011324

M3 - Journal article

SN - 1549-7747

VL - 68

SP - 717

EP - 721

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

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

IS - 2

M1 - 9146309

ER -

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

Abstract

Keywords

Access to Document

AUB Link

Other files and links

Fingerprint

Cite this