Decentralized Coordinated Cyberattack Detection and Mitigation Strategy in DC Microgrids Based on Artificial Neural Networks

Mohammad Reza Habibi; Subham Sahoo; Sebastian Rivera; Tomislav Dragicevic; Frede Blaabjerg

doi:10.1109/JESTPE.2021.3050851

Decentralized Coordinated Cyberattack Detection and Mitigation Strategy in DC Microgrids Based on Artificial Neural Networks

Mohammad Reza Habibi^*, Subham Sahoo, Sebastian Rivera, Tomislav Dragicevic, Frede Blaabjerg

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

62 Scopus citations

Abstract

DC microgrids can be considered as cyber-physical systems (CPSs) and they are vulnerable to cyberattacks. Therefore, it is highly recommended to have effective plans to detect and remove cyberattacks in dc microgrids. This article shows how artificial neural networks can help to detect and mitigate coordinated false data injection attacks (FDIAs) on current measurements as a type of cyberattacks in dc microgrids. FDIAs try to inject the false data into the system to disrupt the control application, which can make the dc microgrid shutdown. The proposed method to mitigate FDIAs is a decentralized approach and it has the capability to estimate the value of the false injected data. In addition, the proposed strategy can remove the FDIAs even for unfair attacks with high domains on all units at the same time. The proposed method is tested on a detailed simulated dc microgrid using the MATLAB/Simulink environment. Finally, real-time simulations by OPAL-RT on the simulated dc microgrid are implemented to evaluate the proposed strategy.

Original language	English
Article number	9319658
Pages (from-to)	4629-4638
Number of pages	10
Journal	IEEE Journal of Emerging and Selected Topics in Power Electronics
Volume	9
Issue number	4
DOIs	https://doi.org/10.1109/JESTPE.2021.3050851
State	Published - 1 Aug 2021

Bibliographical note

Funding Information:
Manuscript received April 15, 2020; revised September 27, 2020; accepted October 20, 2020. Date of publication January 11, 2021; date of current version July 30, 2021. The work of Sebastián Rivera was supported in part by the Advanced Center in Electrical and Electronic Engineering (AC3E) Project under Grant ANID/Basal/FB0008 and in part by the Solar Energy Research Center (SERC) Project under Grant ANID/FONDAP/15110019. Recommended for publication by Associate Editor Hao Ma. (Corresponding author: Mohammad Reza Habibi.) Mohammad Reza Habibi, Subham Sahoo, and Frede Blaabjerg are with the Department of Energy Technology, Aalborg University, 9220 Aalborg, Denmark (e-mail: [email protected]; [email protected]; [email protected]).

Publisher Copyright:
© 2013 IEEE.

Keywords

Artificial neural networks
cyberattack mitigation
dc microgrid
false data injection attack (FDIA)

Access to Document

10.1109/JESTPE.2021.3050851

Cite this

@article{691c2188c743446fae784f932c9e9391,

title = "Decentralized Coordinated Cyberattack Detection and Mitigation Strategy in DC Microgrids Based on Artificial Neural Networks",

abstract = "DC microgrids can be considered as cyber-physical systems (CPSs) and they are vulnerable to cyberattacks. Therefore, it is highly recommended to have effective plans to detect and remove cyberattacks in dc microgrids. This article shows how artificial neural networks can help to detect and mitigate coordinated false data injection attacks (FDIAs) on current measurements as a type of cyberattacks in dc microgrids. FDIAs try to inject the false data into the system to disrupt the control application, which can make the dc microgrid shutdown. The proposed method to mitigate FDIAs is a decentralized approach and it has the capability to estimate the value of the false injected data. In addition, the proposed strategy can remove the FDIAs even for unfair attacks with high domains on all units at the same time. The proposed method is tested on a detailed simulated dc microgrid using the MATLAB/Simulink environment. Finally, real-time simulations by OPAL-RT on the simulated dc microgrid are implemented to evaluate the proposed strategy. ",

keywords = "Artificial neural networks, cyberattack mitigation, dc microgrid, false data injection attack (FDIA)",

author = "Habibi, {Mohammad Reza} and Subham Sahoo and Sebastian Rivera and Tomislav Dragicevic and Frede Blaabjerg",

note = "Funding Information: Manuscript received April 15, 2020; revised September 27, 2020; accepted October 20, 2020. Date of publication January 11, 2021; date of current version July 30, 2021. The work of Sebasti{\'a}n Rivera was supported in part by the Advanced Center in Electrical and Electronic Engineering (AC3E) Project under Grant ANID/Basal/FB0008 and in part by the Solar Energy Research Center (SERC) Project under Grant ANID/FONDAP/15110019. Recommended for publication by Associate Editor Hao Ma. (Corresponding author: Mohammad Reza Habibi.) Mohammad Reza Habibi, Subham Sahoo, and Frede Blaabjerg are with the Department of Energy Technology, Aalborg University, 9220 Aalborg, Denmark (e-mail: [email protected]; [email protected]; [email protected]). Publisher Copyright: {\textcopyright} 2013 IEEE.",

year = "2021",

month = aug,

day = "1",

doi = "10.1109/JESTPE.2021.3050851",

language = "English",

volume = "9",

pages = "4629--4638",

journal = "IEEE Journal of Emerging and Selected Topics in Power Electronics",

issn = "2168-6777",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

number = "4",

}

Decentralized Coordinated Cyberattack Detection and Mitigation Strategy in DC Microgrids Based on Artificial Neural Networks. / Habibi, Mohammad Reza; Sahoo, Subham; Rivera, Sebastian et al.
In: IEEE Journal of Emerging and Selected Topics in Power Electronics, Vol. 9, No. 4, 9319658, 01.08.2021, p. 4629-4638.

Research output: Contribution to journal › Article › peer-review

TY - JOUR

T1 - Decentralized Coordinated Cyberattack Detection and Mitigation Strategy in DC Microgrids Based on Artificial Neural Networks

AU - Habibi, Mohammad Reza

AU - Sahoo, Subham

AU - Rivera, Sebastian

AU - Dragicevic, Tomislav

AU - Blaabjerg, Frede

N1 - Funding Information: Manuscript received April 15, 2020; revised September 27, 2020; accepted October 20, 2020. Date of publication January 11, 2021; date of current version July 30, 2021. The work of Sebastián Rivera was supported in part by the Advanced Center in Electrical and Electronic Engineering (AC3E) Project under Grant ANID/Basal/FB0008 and in part by the Solar Energy Research Center (SERC) Project under Grant ANID/FONDAP/15110019. Recommended for publication by Associate Editor Hao Ma. (Corresponding author: Mohammad Reza Habibi.) Mohammad Reza Habibi, Subham Sahoo, and Frede Blaabjerg are with the Department of Energy Technology, Aalborg University, 9220 Aalborg, Denmark (e-mail: [email protected]; [email protected]; [email protected]). Publisher Copyright: © 2013 IEEE.

PY - 2021/8/1

Y1 - 2021/8/1

N2 - DC microgrids can be considered as cyber-physical systems (CPSs) and they are vulnerable to cyberattacks. Therefore, it is highly recommended to have effective plans to detect and remove cyberattacks in dc microgrids. This article shows how artificial neural networks can help to detect and mitigate coordinated false data injection attacks (FDIAs) on current measurements as a type of cyberattacks in dc microgrids. FDIAs try to inject the false data into the system to disrupt the control application, which can make the dc microgrid shutdown. The proposed method to mitigate FDIAs is a decentralized approach and it has the capability to estimate the value of the false injected data. In addition, the proposed strategy can remove the FDIAs even for unfair attacks with high domains on all units at the same time. The proposed method is tested on a detailed simulated dc microgrid using the MATLAB/Simulink environment. Finally, real-time simulations by OPAL-RT on the simulated dc microgrid are implemented to evaluate the proposed strategy.

AB - DC microgrids can be considered as cyber-physical systems (CPSs) and they are vulnerable to cyberattacks. Therefore, it is highly recommended to have effective plans to detect and remove cyberattacks in dc microgrids. This article shows how artificial neural networks can help to detect and mitigate coordinated false data injection attacks (FDIAs) on current measurements as a type of cyberattacks in dc microgrids. FDIAs try to inject the false data into the system to disrupt the control application, which can make the dc microgrid shutdown. The proposed method to mitigate FDIAs is a decentralized approach and it has the capability to estimate the value of the false injected data. In addition, the proposed strategy can remove the FDIAs even for unfair attacks with high domains on all units at the same time. The proposed method is tested on a detailed simulated dc microgrid using the MATLAB/Simulink environment. Finally, real-time simulations by OPAL-RT on the simulated dc microgrid are implemented to evaluate the proposed strategy.

KW - Artificial neural networks

KW - cyberattack mitigation

KW - dc microgrid

KW - false data injection attack (FDIA)

UR - http://www.scopus.com/inward/record.url?scp=85099577560&partnerID=8YFLogxK

U2 - 10.1109/JESTPE.2021.3050851

DO - 10.1109/JESTPE.2021.3050851

M3 - Article

AN - SCOPUS:85099577560

SN - 2168-6777

VL - 9

SP - 4629

EP - 4638

JO - IEEE Journal of Emerging and Selected Topics in Power Electronics

JF - IEEE Journal of Emerging and Selected Topics in Power Electronics

IS - 4

M1 - 9319658

ER -

Decentralized Coordinated Cyberattack Detection and Mitigation Strategy in DC Microgrids Based on Artificial Neural Networks

Abstract

Bibliographical note

Keywords

Access to Document

Other files and links

Fingerprint

Cite this