Cyber-Resilient Detection of Power Quality Events with NSCT and PCA-SVM
<p dir="ltr">The increasing reliance on smart grids, coupled with the integration of renewable energy and growing cyber-physical interactions, has heightened the vulnerability of power systems to both power quality (PQ) disturbances and cyber-attacks. This paper presents an innovativ...
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| منشور في: |
2025
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| _version_ | 1864513533666918400 |
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| author | Sayanti Chatterjee (1311438) |
| author2 | Pampa Sinha (19864778) Ranjith Kumar Gatla (22467163) Devineni Gireesh Kumar (22467166) D. S. Naga Malleswara Rao (22467169) Kaushik Paul (19864781) Taha Selim Ustun (16869915) Ahmet Onen (20838293) |
| author2_role | author author author author author author author |
| author_facet | Sayanti Chatterjee (1311438) Pampa Sinha (19864778) Ranjith Kumar Gatla (22467163) Devineni Gireesh Kumar (22467166) D. S. Naga Malleswara Rao (22467169) Kaushik Paul (19864781) Taha Selim Ustun (16869915) Ahmet Onen (20838293) |
| author_role | author |
| dc.creator.none.fl_str_mv | Sayanti Chatterjee (1311438) Pampa Sinha (19864778) Ranjith Kumar Gatla (22467163) Devineni Gireesh Kumar (22467166) D. S. Naga Malleswara Rao (22467169) Kaushik Paul (19864781) Taha Selim Ustun (16869915) Ahmet Onen (20838293) |
| dc.date.none.fl_str_mv | 2025-04-21T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/access.2025.3562606 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Cyber-Resilient_Detection_of_Power_Quality_Events_with_NSCT_and_PCA-SVM/30448091 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Electrical engineering Information and computing sciences Artificial intelligence Cybersecurity and privacy Power quality (PQ) Nonsubsampled contourlet transform (NSCT) Morphological component analysis (MCA) Split augmented Lagrangian shrinkage approach (SALSA) Frequency-adaptive detection index (FADI) Cyber-physical security False data injection (FDI) Denial of service (DoS) Multi-class support vector machine (SVM) Smart grids |
| dc.title.none.fl_str_mv | Cyber-Resilient Detection of Power Quality Events with NSCT and PCA-SVM |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">The increasing reliance on smart grids, coupled with the integration of renewable energy and growing cyber-physical interactions, has heightened the vulnerability of power systems to both power quality (PQ) disturbances and cyber-attacks. This paper presents an innovative detection framework that combines Nonsubsampled Contourlet Transform (NSCT) with Principal Component Analysis (PCA) and Support Vector Machine (SVM) classification to accurately detect and classify PQ disturbances under the influence of cyber threats, such as False Data Injection (FDI) and Denial of Service (DoS) attacks. The proposed methodology leverages NSCT’s multiscale decomposition capabilities to extract fine-grained signal features, while PCA optimizes feature selection for enhanced computational efficiency. Comprehensive experiments conducted on synthetic and real-world datasets validate the framework’s effectiveness, demonstrating superior detection accuracy, robustness to noise, and resilience against cyber-attacks. The proposed NSCT-PCA- SVM approach represents a significant step forward in ensuring secure and reliable smart grid operations.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" rel="noreferrer" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2025.3562606" target="_blank">https://dx.doi.org/10.1109/access.2025.3562606</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_0b611b10fbada3ea4c856d34d8cfd9c1 |
| identifier_str_mv | 10.1109/access.2025.3562606 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/30448091 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Cyber-Resilient Detection of Power Quality Events with NSCT and PCA-SVMSayanti Chatterjee (1311438)Pampa Sinha (19864778)Ranjith Kumar Gatla (22467163)Devineni Gireesh Kumar (22467166)D. S. Naga Malleswara Rao (22467169)Kaushik Paul (19864781)Taha Selim Ustun (16869915)Ahmet Onen (20838293)EngineeringElectrical engineeringInformation and computing sciencesArtificial intelligenceCybersecurity and privacyPower quality (PQ)Nonsubsampled contourlet transform (NSCT)Morphological component analysis (MCA)Split augmented Lagrangian shrinkage approach (SALSA)Frequency-adaptive detection index (FADI)Cyber-physical securityFalse data injection (FDI)Denial of service (DoS)Multi-class support vector machine (SVM)Smart grids<p dir="ltr">The increasing reliance on smart grids, coupled with the integration of renewable energy and growing cyber-physical interactions, has heightened the vulnerability of power systems to both power quality (PQ) disturbances and cyber-attacks. This paper presents an innovative detection framework that combines Nonsubsampled Contourlet Transform (NSCT) with Principal Component Analysis (PCA) and Support Vector Machine (SVM) classification to accurately detect and classify PQ disturbances under the influence of cyber threats, such as False Data Injection (FDI) and Denial of Service (DoS) attacks. The proposed methodology leverages NSCT’s multiscale decomposition capabilities to extract fine-grained signal features, while PCA optimizes feature selection for enhanced computational efficiency. Comprehensive experiments conducted on synthetic and real-world datasets validate the framework’s effectiveness, demonstrating superior detection accuracy, robustness to noise, and resilience against cyber-attacks. The proposed NSCT-PCA- SVM approach represents a significant step forward in ensuring secure and reliable smart grid operations.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" rel="noreferrer" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2025.3562606" target="_blank">https://dx.doi.org/10.1109/access.2025.3562606</a></p>2025-04-21T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/access.2025.3562606https://figshare.com/articles/journal_contribution/Cyber-Resilient_Detection_of_Power_Quality_Events_with_NSCT_and_PCA-SVM/30448091CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/304480912025-04-21T03:00:00Z |
| spellingShingle | Cyber-Resilient Detection of Power Quality Events with NSCT and PCA-SVM Sayanti Chatterjee (1311438) Engineering Electrical engineering Information and computing sciences Artificial intelligence Cybersecurity and privacy Power quality (PQ) Nonsubsampled contourlet transform (NSCT) Morphological component analysis (MCA) Split augmented Lagrangian shrinkage approach (SALSA) Frequency-adaptive detection index (FADI) Cyber-physical security False data injection (FDI) Denial of service (DoS) Multi-class support vector machine (SVM) Smart grids |
| status_str | publishedVersion |
| title | Cyber-Resilient Detection of Power Quality Events with NSCT and PCA-SVM |
| title_full | Cyber-Resilient Detection of Power Quality Events with NSCT and PCA-SVM |
| title_fullStr | Cyber-Resilient Detection of Power Quality Events with NSCT and PCA-SVM |
| title_full_unstemmed | Cyber-Resilient Detection of Power Quality Events with NSCT and PCA-SVM |
| title_short | Cyber-Resilient Detection of Power Quality Events with NSCT and PCA-SVM |
| title_sort | Cyber-Resilient Detection of Power Quality Events with NSCT and PCA-SVM |
| topic | Engineering Electrical engineering Information and computing sciences Artificial intelligence Cybersecurity and privacy Power quality (PQ) Nonsubsampled contourlet transform (NSCT) Morphological component analysis (MCA) Split augmented Lagrangian shrinkage approach (SALSA) Frequency-adaptive detection index (FADI) Cyber-physical security False data injection (FDI) Denial of service (DoS) Multi-class support vector machine (SVM) Smart grids |