A New False Data Injection Attack Detection Model for Cyberattack Resilient Energy Forecasting

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dc.authorid0000-0003-3019-8962en_US
dc.authorid0000-0001-7154-9445en_US
dc.authorid0000-0002-0253-9443en_US
dc.authorid0000-0002-0255-8353en_US
dc.authorid0000-0002-0779-8727en_US
dc.contributor.authorAhmadi, Amirhossein
dc.contributor.authorNabipour, Mojtaba
dc.contributor.authorTaheri, Saman
dc.contributor.authorMohammadi-Ivatloo, Behnam
dc.contributor.authorVahidinasab, Vahid
dc.date.accessioned2024-03-25T12:27:14Z
dc.date.available2024-03-25T12:27:14Z
dc.date.issued2023en_US
dc.departmentRektörlük, Bilişim Teknolojileri Uygulama ve Araştırma Merkezien_US
dc.description.abstractAs power systems are gradually evolving into more efficient and intelligent cyber-physical energy systems with the large-scale penetration of renewable energies and information technology, they become increasingly reliant upon more accurate and complex forecasting. The accuracy and generalizability of the forecasting rest, to a great extent, upon the data quality, which is very susceptible to cyberattacks. False data injection (FDI) attacks constitute a class of cyberattacks that could maliciously alter a large portion of supposedly protected data, which may not be easily detected by existing operational practices, thereby deteriorating the forecasting performance causing catastrophic consequences in the power system. This article proposes a novel data-driven FDI attack detection mechanism to automatically detect the intrusions and thus enrich the reliability and resiliency of energy forecasting systems. The proposed mechanism is based on cross-validation, least-squares, and z-score metric providing accurate detections with low computational cost and high scalability without utilizing either system's models or parameters. The effectiveness of the proposed detector is corroborated through six representative tree-based wind power forecasting models. Experiments indicate that corrupted data injected into input, output, and input-output data is properly located and removed, whereby the accuracy and generalizability of the final forecasts are recovered.en_US
dc.identifier.doi10.1109/TII.2022.3151748en_US
dc.identifier.endpage381en_US
dc.identifier.issue1en_US
dc.identifier.scopus2-s2.0-85124847715en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage371en_US
dc.identifier.urihttps://hdl.handle.net/11467/7183
dc.identifier.urihttps://doi.org/10.1109/TII.2022.3151748
dc.identifier.volume19en_US
dc.identifier.wosWOS:000880654600038en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.relation.ispartofIEEE TRANSACTIONS ON INDUSTRIAL INFORMATICSen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Başka Kurum Yazarıen_US
dc.rightsinfo:eu-repo/semantics/embargoedAccessen_US
dc.subjectCross-validation, cyberattack, false data injection (FDI), forecasting, machine learning (ML)en_US
dc.titleA New False Data Injection Attack Detection Model for Cyberattack Resilient Energy Forecastingen_US
dc.typeArticleen_US

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