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Reinforcement Learning-Based Method to Exploit Vulnerabilities of False Data Injection Attack Detectors in Modular Multilevel Converters

Burgos-Mellado, Claudio; Zuñiga-Bauerle, Claudio; Muñoz-Carpintero, Diego; Arias-Esquivel, Yeiner; Càrdenas-Dobson, Roberto; Dragičević, Tomislav; Donoso, Felipe; Watson, Alan

Authors

Claudio Burgos-Mellado

Claudio Zuñiga-Bauerle

Diego Muñoz-Carpintero

Yeiner Arias-Esquivel

Roberto Càrdenas-Dobson

Tomislav Dragičević

Felipe Donoso

Profile image of ALAN WATSON

ALAN WATSON ALAN.WATSON@NOTTINGHAM.AC.UK
Associate Professor



Abstract

Implementing control schemes for modular multilevel converters (M2Cs) involves both a cyber and a physical level, leading to a cyber-physical system (CPS). At the cyber level, a communication network enables the data exchange between sensors, control platforms, and monitoring systems. Meanwhile, at the physical level, the semiconductor devices that comprise the M2C are switched ON/OFF by the control system. In this context, almost all published works in this research area assume that the CPS always reports correct information. However, this may not be the case when the M2C is affected by cyber-attacks, such as the one named false data injection attack (FDIA), where the data seen by the control system is corrupted through illegitimate data intrusion into the CPS. To deal with this situation, FDIA detectors for the M2C are recently starting to be studied, where the goal is to detect and mitigate the attacks and the attacked sub-modules. This paper proposes a reinforcement learning (RL)-based method to uncover the deficiencies of existing FDIAs detectors used for M2C applications. The proposed method auto-generates complex attack sequences able to bypass FDIA detectors. Therefore, it points out the weaknesses of current detectors: This valuable information can be used later to improve the performance of the detectors, establishing more reliable cybersecurity solutions for M2Cs. The RL environment is developed in Matlab/Simulink augmented by PLECS/blockset, and it is made available to researchers on a website to motivate future research efforts in this area. Hardware-in-the-loop (HIL) studies verify the proposal's effectiveness.

Citation

Burgos-Mellado, C., Zuñiga-Bauerle, C., Muñoz-Carpintero, D., Arias-Esquivel, Y., Càrdenas-Dobson, R., Dragičević, T., …Watson, A. (2023). Reinforcement Learning-Based Method to Exploit Vulnerabilities of False Data Injection Attack Detectors in Modular Multilevel Converters. IEEE Transactions on Power Electronics, 38(7), 8907-8921. https://doi.org/10.1109/TPEL.2023.3263728

Journal Article Type Article
Acceptance Date Mar 29, 2023
Online Publication Date Mar 31, 2023
Publication Date 2023-07
Deposit Date May 3, 2023
Publicly Available Date May 16, 2023
Journal IEEE Transactions on Power Electronics
Print ISSN 0885-8993
Electronic ISSN 1941-0107
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 38
Issue 7
Pages 8907-8921
DOI https://doi.org/10.1109/TPEL.2023.3263728
Keywords Electrical and Electronic Engineering
Public URL https://nottingham-repository.worktribe.com/output/19454356
Publisher URL https://ieeexplore.ieee.org/document/10089555

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