research-article

Detecting stealthy false data injection attacks in the smart grid using ensemble-based machine learning

Authors:

Mohammad Ashrafuzzaman,

Yacine Chakhchoukh,

Frederick T. SheldonAuthors Info & Claims

Volume 97, Issue C

https://doi.org/10.1016/j.cose.2020.101994

Published: 01 October 2020 Publication History

Highlights

•

Detection of stealthy false data injection attacks in the smart grid.

•

Supervised and unsupervised machine learning methods.

•

Ensemble-based machine learning methods.

Abstract

Stealthy false data injection attacks target state estimation in energy management systems in smart power grids to adversely affect operations of the power transmission systems. This paper presents a data-driven machine learning based scheme to detect stealthy false data injection attacks on state estimation. The scheme employs ensemble learning, where multiple classifiers are used and decisions by individual classifiers are further classified. Two ensembles are used in this scheme, one uses supervised classifiers while the other uses unsupervised classifiers. The scheme is validated using simulated data on the standard IEEE 14-bus system. Experimental results show that the performance of both supervised individual and ensemble models are comparable. However, for unsupervised models, the ensembles performed better than the individual classifiers.

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Cited By

Wang YZhou YMa Jjin Q(2024)A locational false data injection attack detection method in smart grid based on adversarial variational autoencoders ▪Applied Soft Computing10.1016/j.asoc.2023.111169151:COnline publication date: 17-Apr-2024
https://dl.acm.org/doi/10.1016/j.asoc.2023.111169
Rajendran DSanthanam P(2024)Leveraging ensemble learning for stealth assessment model with game-based learning environmentSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-023-09605-828:4(3509-3517)Online publication date: 1-Feb-2024
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Published In

cover image Computers and Security

Computers and Security Volume 97, Issue C

Oct 2020

747 pages

ISSN:0167-4048

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Elsevier Ltd.

Publisher

Elsevier Advanced Technology Publications

United Kingdom

Publication History

Published: 01 October 2020

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Cited By

Wang YZhou YMa Jjin Q(2024)A locational false data injection attack detection method in smart grid based on adversarial variational autoencoders ▪Applied Soft Computing10.1016/j.asoc.2023.111169151:COnline publication date: 17-Apr-2024
https://dl.acm.org/doi/10.1016/j.asoc.2023.111169
Rajendran DSanthanam P(2024)Leveraging ensemble learning for stealth assessment model with game-based learning environmentSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-023-09605-828:4(3509-3517)Online publication date: 1-Feb-2024
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Kern DKrauß CHollick M(2023)Detection of Anomalies in Electric Vehicle Charging SessionsProceedings of the 39th Annual Computer Security Applications Conference10.1145/3627106.3627127(298-309)Online publication date: 4-Dec-2023
https://dl.acm.org/doi/10.1145/3627106.3627127
Canonico RSperlì G(2023)Industrial cyber-physical systems protectionComputers and Security10.1016/j.cose.2023.103531135:COnline publication date: 1-Dec-2023
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Yu PWei JJia RWang JGuo J(2023)Sampled‐data control for cyber physical systems via identifying false data injection attacksInternational Journal of Adaptive Control and Signal Processing10.1002/acs.367437:12(3094-3110)Online publication date: 3-Dec-2023
https://dl.acm.org/doi/10.1002/acs.3674
Jahromi MJahromi AKundur DSanner SKassouf M(2022)Data analytics for cybersecurity enhancement of transformer protectionACM SIGEnergy Energy Informatics Review10.1145/3508467.35084691:1(12-19)Online publication date: 3-Jan-2022
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