Enhanced Detection of False Data Injection Attacks Using Hybrid Clustering‐Classification for Various Penetration and Distribution Levels of Renewables.
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| Title: | Enhanced Detection of False Data Injection Attacks Using Hybrid Clustering‐Classification for Various Penetration and Distribution Levels of Renewables. |
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| Authors: | Pirhadi, Farhad1 (AUTHOR), Seifi, Hossein1 (AUTHOR) seifi_ho@modares.ac.ir, Delkhosh, Hamed1 (AUTHOR) |
| Source: | IET Renewable Power Generation (Wiley-Blackwell). Jan2025, Vol. 19 Issue 1, p1-20. 20p. |
| Subject Terms: | *Renewable energy sources, Anomaly detection (Computer security), Electric power system security measures, Feature selection, Cyberterrorism, Clustering algorithms, Machine learning, Classification algorithms |
| Abstract: | State estimation (SE) is a crucial tool for the secure operation of transmission systems, which are susceptible to false data injection attacks (FDIAs) that can bypass the bad data detection mechanism. The increasing penetration and distribution of renewable energy sources (RESs) reduce the predictability of normal grid behaviour, thereby weakening the performance of conventional detection methods by providing the opportunity for stealthy attacks. This paper presents a new FDIA detection method based on hybrid machine learning (HML), leveraging soft and hard clustering before the classification‐based anomaly detection. It effectively distinguishes between attack and normal samples in grids with various types (wind and solar), distributions, and penetration levels of RESs. The suggested feature engineering enables clustering, using a combination of fuzzy C‐means (FCM) and K‐means, to better organise the database so that a desirable performance can be achieved by the random forest (RF)‐based classifier. The model is comprehensively tested on the IEEE 14‐bus system under 22 RES scenarios, showing robust accuracy across diverse grid conditions. Additionally, the scalability of the method is validated on the IEEE 118‐bus system. The method outperforms recent approaches with average detection accuracies of 99.66% and 99.04% on the small and large systems, respectively. [ABSTRACT FROM AUTHOR] |
| Copyright of IET Renewable Power Generation (Wiley-Blackwell) is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | GreenFILE |
| FullText | Text: Availability: 0 |
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| Header | DbId: 8gh DbLabel: GreenFILE An: 190416594 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Enhanced Detection of False Data Injection Attacks Using Hybrid Clustering‐Classification for Various Penetration and Distribution Levels of Renewables. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Pirhadi%2C+Farhad%22">Pirhadi, Farhad</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Seifi%2C+Hossein%22">Seifi, Hossein</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> seifi_ho@modares.ac.ir</i><br /><searchLink fieldCode="AR" term="%22Delkhosh%2C+Hamed%22">Delkhosh, Hamed</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22IET+Renewable+Power+Generation+%28Wiley-Blackwell%29%22">IET Renewable Power Generation (Wiley-Blackwell)</searchLink>. Jan2025, Vol. 19 Issue 1, p1-20. 20p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Renewable+energy+sources%22">Renewable energy sources</searchLink><br /><searchLink fieldCode="DE" term="%22Anomaly+detection+%28Computer+security%29%22">Anomaly detection (Computer security)</searchLink><br /><searchLink fieldCode="DE" term="%22Electric+power+system+security+measures%22">Electric power system security measures</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+selection%22">Feature selection</searchLink><br /><searchLink fieldCode="DE" term="%22Cyberterrorism%22">Cyberterrorism</searchLink><br /><searchLink fieldCode="DE" term="%22Clustering+algorithms%22">Clustering algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Classification+algorithms%22">Classification algorithms</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: State estimation (SE) is a crucial tool for the secure operation of transmission systems, which are susceptible to false data injection attacks (FDIAs) that can bypass the bad data detection mechanism. The increasing penetration and distribution of renewable energy sources (RESs) reduce the predictability of normal grid behaviour, thereby weakening the performance of conventional detection methods by providing the opportunity for stealthy attacks. This paper presents a new FDIA detection method based on hybrid machine learning (HML), leveraging soft and hard clustering before the classification‐based anomaly detection. It effectively distinguishes between attack and normal samples in grids with various types (wind and solar), distributions, and penetration levels of RESs. The suggested feature engineering enables clustering, using a combination of fuzzy C‐means (FCM) and K‐means, to better organise the database so that a desirable performance can be achieved by the random forest (RF)‐based classifier. The model is comprehensively tested on the IEEE 14‐bus system under 22 RES scenarios, showing robust accuracy across diverse grid conditions. Additionally, the scalability of the method is validated on the IEEE 118‐bus system. The method outperforms recent approaches with average detection accuracies of 99.66% and 99.04% on the small and large systems, respectively. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of IET Renewable Power Generation (Wiley-Blackwell) is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1049/rpg2.70157 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 20 StartPage: 1 Subjects: – SubjectFull: Renewable energy sources Type: general – SubjectFull: Anomaly detection (Computer security) Type: general – SubjectFull: Electric power system security measures Type: general – SubjectFull: Feature selection Type: general – SubjectFull: Cyberterrorism Type: general – SubjectFull: Clustering algorithms Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Classification algorithms Type: general Titles: – TitleFull: Enhanced Detection of False Data Injection Attacks Using Hybrid Clustering‐Classification for Various Penetration and Distribution Levels of Renewables. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Pirhadi, Farhad – PersonEntity: Name: NameFull: Seifi, Hossein – PersonEntity: Name: NameFull: Delkhosh, Hamed IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: Jan2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 17521416 Numbering: – Type: volume Value: 19 – Type: issue Value: 1 Titles: – TitleFull: IET Renewable Power Generation (Wiley-Blackwell) Type: main |
| ResultId | 1 |
