A comparative analysis of different transmission line fault detectors and classifiers during normal conditions and cyber-attacks

Tusher, AS; Rahman, MA; Islam, MR; Hossain, MJ

A comparative analysis of different transmission line fault detectors and classifiers during normal conditions and cyber-attacks

Tusher, AS Rahman, MA Islam, MR Hossain, MJ

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Publisher:: WILEY
Publication Type:: Journal Article
Citation:: JOURNAL OF ENGINEERING-JOE, 2024, 2024, (7)
Issue Date:: 2024-07

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Field	Value	Language
dc.contributor.author	Tusher, AS
dc.contributor.author	Rahman, MA
dc.contributor.author	Islam, MR
dc.contributor.author	Hossain, MJ
dc.date.accessioned	2025-02-03T23:59:51Z
dc.date.available	2025-02-03T23:59:51Z
dc.date.issued	2024-07
dc.identifier.citation	JOURNAL OF ENGINEERING-JOE, 2024, 2024, (7)
dc.identifier.issn	2314-4904
dc.identifier.issn	2051-3305
dc.identifier.uri	http://hdl.handle.net/10453/184897
dc.description.abstract	<jats:title>Abstract</jats:title><jats:p>Transmission lines, the core part of the transmission and distribution system in the smart grid, require effective, efficient, and reliable protective measures against faults to avoid severe damage to physical infrastructure and financial losses. Due to their growing popularity, machine learning models are used in fault detection and classification, whose performances can be severely affected by cyber‐attacks due to their data dependency, posing a critical concern. Hence, this paper introduces false data injection attacks to address the vulnerability of machine learning‐based fault detectors and classifiers. A comparative study of 9 detection models and 6 classification models under normal conditions and during a combination of two models of false data injection attacks is presented to evaluate the severity of cyber‐attacks. Experimental results show that highly accurate models in normal conditions are more susceptible to cyber‐attacks, with up to 69% and 28% degradations in accuracy for fault detectors and classifiers, respectively. Furthermore, the detection models are found to be more vulnerable to cyber‐attacks than the classification models. With no robust detectors and classifiers being found, this work addresses the importance of developing attack‐resilient fault detection and classification schemes considering their academic and industrial significance.</jats:p>
dc.language	English
dc.publisher	WILEY
dc.relation.ispartof	JOURNAL OF ENGINEERING-JOE
dc.relation.isbasedon	10.1049/tje2.12412
dc.rights	info:eu-repo/semantics/openAccess
dc.subject.classification	40 Engineering
dc.title	A comparative analysis of different transmission line fault detectors and classifiers during normal conditions and cyber-attacks
dc.type	Journal Article
utslib.citation.volume	2024
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/
dc.date.updated	2025-02-03T23:59:50Z
pubs.issue	7
pubs.publication-status	Published
pubs.volume	2024
utslib.citation.issue	7

Abstract:

AbstractTransmission lines, the core part of the transmission and distribution system in the smart grid, require effective, efficient, and reliable protective measures against faults to avoid severe damage to physical infrastructure and financial losses. Due to their growing popularity, machine learning models are used in fault detection and classification, whose performances can be severely affected by cyber‐attacks due to their data dependency, posing a critical concern. Hence, this paper introduces false data injection attacks to address the vulnerability of machine learning‐based fault detectors and classifiers. A comparative study of 9 detection models and 6 classification models under normal conditions and during a combination of two models of false data injection attacks is presented to evaluate the severity of cyber‐attacks. Experimental results show that highly accurate models in normal conditions are more susceptible to cyber‐attacks, with up to 69% and 28% degradations in accuracy for fault detectors and classifiers, respectively. Furthermore, the detection models are found to be more vulnerable to cyber‐attacks than the classification models. With no robust detectors and classifiers being found, this work addresses the importance of developing attack‐resilient fault detection and classification schemes considering their academic and industrial significance.

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http://hdl.handle.net/10453/184897