Anomaly Detection on Smart Grids With Optimized Convolutional Long Short-Term Memory Model
<p dir="ltr">The integration of digital technologies into traditional power systems has increased the efficiency and sustainability of power grids, transforming traditional grids into smart grids. However, this transformation has also introduced new vulnerabilities, such as susceptib...
محفوظ في:
| المؤلف الرئيسي: | |
|---|---|
| مؤلفون آخرون: | , , |
| منشور في: |
2025
|
| الموضوعات: | |
| الوسوم: |
إضافة وسم
لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!
|
| _version_ | 1864513537593835520 |
|---|---|
| author | Ahmad N. Alkuwari (22392226) |
| author2 | Saif Al-Kuwari (16904610) Abdullatif Albaseer (16904607) Marwa Qaraqe (10135172) |
| author2_role | author author author |
| author_facet | Ahmad N. Alkuwari (22392226) Saif Al-Kuwari (16904610) Abdullatif Albaseer (16904607) Marwa Qaraqe (10135172) |
| author_role | author |
| dc.creator.none.fl_str_mv | Ahmad N. Alkuwari (22392226) Saif Al-Kuwari (16904610) Abdullatif Albaseer (16904607) Marwa Qaraqe (10135172) |
| dc.date.none.fl_str_mv | 2025-03-10T12:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/access.2025.3547037 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Anomaly_Detection_on_Smart_Grids_With_Optimized_Convolutional_Long_Short-Term_Memory_Model/30306163 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Electrical engineering Electronics, sensors and digital hardware Information and computing sciences Cybersecurity and privacy Anomaly detection energy consumption energy theft detection operational technology smart grid Smart grids Long short term memory Energy consumption Smart meters Analytical models Data models Logic gates Electricity Accuracy |
| dc.title.none.fl_str_mv | Anomaly Detection on Smart Grids With Optimized Convolutional Long Short-Term Memory Model |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">The integration of digital technologies into traditional power systems has increased the efficiency and sustainability of power grids, transforming traditional grids into smart grids. However, this transformation has also introduced new vulnerabilities, such as susceptibility to false data injection (FDI) attacks, which can lead to significant energy theft. Recent reports estimate that these attacks cost utility providers approximately 101 billion dollars annually. This study presents an approach for anomaly detection in smart grids through energy consumption readings from smart meters on the customer side using an optimized lightweight convolutional long short-term memory (ConvLSTM) model. This study benchmarks and evaluates different machine learning models against seven FDI attacks, which are multi-class labeled. The evaluated machine learning models include traditional shallow detectors, deep learning-based detectors, and hybrid models that employ both horizontal and vertical detection strategies. Through extensive experimentation, the optimized ConvLSTM model is shown to demonstrate superior performance in detecting attacks; it achieves a high accuracy of 91.3% compared with other models in classifying these attacks. The results indicate that the proposed model provides a robust solution for improving the security and reliability of smart grids, and it offers significant benefits to utility providers who seek to mitigate energy theft and enhance grid resilience.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" 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.2025.3547037" target="_blank">https://dx.doi.org/10.1109/access.2025.3547037</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_583bd13dfde6bba48d254c683b04ed22 |
| identifier_str_mv | 10.1109/access.2025.3547037 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/30306163 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Anomaly Detection on Smart Grids With Optimized Convolutional Long Short-Term Memory ModelAhmad N. Alkuwari (22392226)Saif Al-Kuwari (16904610)Abdullatif Albaseer (16904607)Marwa Qaraqe (10135172)EngineeringElectrical engineeringElectronics, sensors and digital hardwareInformation and computing sciencesCybersecurity and privacyAnomaly detectionenergy consumptionenergy theft detectionoperational technologysmart gridSmart gridsLong short term memoryEnergy consumptionSmart metersAnalytical modelsData modelsLogic gatesElectricityAccuracy<p dir="ltr">The integration of digital technologies into traditional power systems has increased the efficiency and sustainability of power grids, transforming traditional grids into smart grids. However, this transformation has also introduced new vulnerabilities, such as susceptibility to false data injection (FDI) attacks, which can lead to significant energy theft. Recent reports estimate that these attacks cost utility providers approximately 101 billion dollars annually. This study presents an approach for anomaly detection in smart grids through energy consumption readings from smart meters on the customer side using an optimized lightweight convolutional long short-term memory (ConvLSTM) model. This study benchmarks and evaluates different machine learning models against seven FDI attacks, which are multi-class labeled. The evaluated machine learning models include traditional shallow detectors, deep learning-based detectors, and hybrid models that employ both horizontal and vertical detection strategies. Through extensive experimentation, the optimized ConvLSTM model is shown to demonstrate superior performance in detecting attacks; it achieves a high accuracy of 91.3% compared with other models in classifying these attacks. The results indicate that the proposed model provides a robust solution for improving the security and reliability of smart grids, and it offers significant benefits to utility providers who seek to mitigate energy theft and enhance grid resilience.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" 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.2025.3547037" target="_blank">https://dx.doi.org/10.1109/access.2025.3547037</a></p>2025-03-10T12:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/access.2025.3547037https://figshare.com/articles/journal_contribution/Anomaly_Detection_on_Smart_Grids_With_Optimized_Convolutional_Long_Short-Term_Memory_Model/30306163CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/303061632025-03-10T12:00:00Z |
| spellingShingle | Anomaly Detection on Smart Grids With Optimized Convolutional Long Short-Term Memory Model Ahmad N. Alkuwari (22392226) Engineering Electrical engineering Electronics, sensors and digital hardware Information and computing sciences Cybersecurity and privacy Anomaly detection energy consumption energy theft detection operational technology smart grid Smart grids Long short term memory Energy consumption Smart meters Analytical models Data models Logic gates Electricity Accuracy |
| status_str | publishedVersion |
| title | Anomaly Detection on Smart Grids With Optimized Convolutional Long Short-Term Memory Model |
| title_full | Anomaly Detection on Smart Grids With Optimized Convolutional Long Short-Term Memory Model |
| title_fullStr | Anomaly Detection on Smart Grids With Optimized Convolutional Long Short-Term Memory Model |
| title_full_unstemmed | Anomaly Detection on Smart Grids With Optimized Convolutional Long Short-Term Memory Model |
| title_short | Anomaly Detection on Smart Grids With Optimized Convolutional Long Short-Term Memory Model |
| title_sort | Anomaly Detection on Smart Grids With Optimized Convolutional Long Short-Term Memory Model |
| topic | Engineering Electrical engineering Electronics, sensors and digital hardware Information and computing sciences Cybersecurity and privacy Anomaly detection energy consumption energy theft detection operational technology smart grid Smart grids Long short term memory Energy consumption Smart meters Analytical models Data models Logic gates Electricity Accuracy |