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Ahmadi, A., Nabipour, M., Taheri, S., Mohammadi-Ivatloo, B. and Vahidinasab, V. 
ORCID: 0000-0002-0779-8727,
2022.
A new false data injection attack detection model for cyberattack resilient energy forecasting.
IEEE Transactions on Industrial Informatics.
ISSN 1551-3203
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1519769_Vahidinasab.pdf - Post-print Download (29MB) | Preview |
Abstract
As 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 on a more accurate 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 paper proposes a novel data-driven FDI attack detection mechanism to automatically detect the intrusions and thus enrich the reliability and resilience of the energy forecasting systems. The proposed mechanism is based on cross-validation and least-squares providing accurate detection with low computational cost and high scalability without utilizing the model and parameters of the system. Effectiveness of the proposed detector is corroborated through six representative tree-based wind power forecasting models including decision tree, bagging, random forest, boosting, gradient boosting, and XGboost. Experiments indicate that corrupted data is properly located and removed, whereby the accuracy and generalizability of the final forecasts is recovered.
| Item Type: | Journal article | ||||||
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| Publication Title: | IEEE Transactions on Industrial Informatics | ||||||
| Creators: | Ahmadi, A., Nabipour, M., Taheri, S., Mohammadi-Ivatloo, B. and Vahidinasab, V. | ||||||
| Publisher: | IEEE | ||||||
| Date: | 15 February 2022 | ||||||
| ISSN: | 1551-3203 | ||||||
| Identifiers: |
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| Rights: | © Copyright 2022 IEEE - All rights reserved. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | ||||||
| Divisions: | Schools > School of Science and Technology | ||||||
| Record created by: | Linda Sullivan | ||||||
| Date Added: | 23 Feb 2022 10:58 | ||||||
| Last Modified: | 23 Feb 2022 10:58 | ||||||
| URI: | https://irep.ntu.ac.uk/id/eprint/45741 |
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