Incentive edge-based federated learning for false data injection attack detection on power grid state estimation: A novel mechanism design approach

Lin, WT; Chen, G; Huang, Y

Incentive edge-based federated learning for false data injection attack detection on power grid state estimation: A novel mechanism design approach

Lin, WT Chen, G Huang, Y

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Publisher:: Elsevier BV
Publication Type:: Journal Article
Citation:: Applied Energy, 2022, 314, pp. 118828
Issue Date:: 2022-05-15

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Field	Value	Language
dc.contributor.author	Lin, WT
dc.contributor.author	Chen, G
dc.contributor.author	Huang, Y https://orcid.org/0000-0001-9800-8788
dc.date.accessioned	2022-05-09T04:30:36Z
dc.date.available	2022-05-09T04:30:36Z
dc.date.issued	2022-05-15
dc.identifier.citation	Applied Energy, 2022, 314, pp. 118828
dc.identifier.issn	0306-2619
dc.identifier.uri	http://hdl.handle.net/10453/157148
dc.description.abstract	With the growing concern in security and privacy of smart grid, false data injection attack detection on power grid state estimation now faces new challenges including unknown system parameters and small decentralized data sets with strategic data owners. To deal with these technical bottlenecks, this paper proposes a novel edge-based federated learning framework for false data injection attack detection on power grid state estimation, which has great potential in real-world applications with unknown system parameters. Furthermore, to seek a high detection accuracy with small decentralized data set and strategic data owners, an incentive mechanism is designed to encourage the desired data owners contributing to false data injection attack detection. To explore the impact of the incentive mechanism on the detection accuracy, a bi-level model depicting the data owners’ participation in false data injection attack detection is formulated, based on which the impact is quantified. Moreover, a novel preference criterion is proposed for optimal mechanism design. It can achieve the optimal detection accuracy under a certain incentive budget. The incentive mechanism is designed and tested for 100 Monte Carlo scenarios. Simulations of false data injection attack detection on power grid state estimation show that the proposed framework outperforms the existing works without mechanism design.
dc.language	en
dc.publisher	Elsevier BV
dc.relation.ispartof	Applied Energy
dc.relation.isbasedon	10.1016/j.apenergy.2022.118828
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.subject	09 Engineering, 14 Economics
dc.subject.classification	Energy
dc.title	Incentive edge-based federated learning for false data injection attack detection on power grid state estimation: A novel mechanism design approach
dc.type	Journal Article
utslib.citation.volume	314
utslib.for	09 Engineering
utslib.for	14 Economics
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 Civil and Environmental Engineering
utslib.copyright.status	recently_added	*
dc.date.updated	2022-05-09T04:30:12Z
pubs.publication-status	Accepted
pubs.volume	314

Abstract:

With the growing concern in security and privacy of smart grid, false data injection attack detection on power grid state estimation now faces new challenges including unknown system parameters and small decentralized data sets with strategic data owners. To deal with these technical bottlenecks, this paper proposes a novel edge-based federated learning framework for false data injection attack detection on power grid state estimation, which has great potential in real-world applications with unknown system parameters. Furthermore, to seek a high detection accuracy with small decentralized data set and strategic data owners, an incentive mechanism is designed to encourage the desired data owners contributing to false data injection attack detection. To explore the impact of the incentive mechanism on the detection accuracy, a bi-level model depicting the data owners’ participation in false data injection attack detection is formulated, based on which the impact is quantified. Moreover, a novel preference criterion is proposed for optimal mechanism design. It can achieve the optimal detection accuracy under a certain incentive budget. The incentive mechanism is designed and tested for 100 Monte Carlo scenarios. Simulations of false data injection attack detection on power grid state estimation show that the proposed framework outperforms the existing works without mechanism design.

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http://hdl.handle.net/10453/157148