Novel Evasion Attacks Against Adversarial Training Defense for Smart Grid Federated Learning
<h3>Abstract</h3><p dir="ltr">In the advanced metering infrastructure (AMI) of the smart grid, smart meters (SMs) are deployed to collect fine-grained electricity consumption data, enabling billing, load monitoring, and efficient energy management. However, some consumers...
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| مؤلفون آخرون: | , , , , |
| منشور في: |
2023
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إضافة وسم
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| _version_ | 1864513507129556992 |
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| author | Atef H. Bondok (19482352) |
| author2 | Mohamed Mahmoud (4544233) Mahmoud M. Badr (19482355) Mostafa M. Fouda (14768509) Mohamed Abdallah (3073191) Maazen Alsabaan (17714529) |
| author2_role | author author author author author |
| author_facet | Atef H. Bondok (19482352) Mohamed Mahmoud (4544233) Mahmoud M. Badr (19482355) Mostafa M. Fouda (14768509) Mohamed Abdallah (3073191) Maazen Alsabaan (17714529) |
| author_role | author |
| dc.creator.none.fl_str_mv | Atef H. Bondok (19482352) Mohamed Mahmoud (4544233) Mahmoud M. Badr (19482355) Mostafa M. Fouda (14768509) Mohamed Abdallah (3073191) Maazen Alsabaan (17714529) |
| dc.date.none.fl_str_mv | 2023-10-11T12:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/access.2023.3323617 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Novel_Evasion_Attacks_Against_Adversarial_Training_Defense_for_Smart_Grid_Federated_Learning/26830168 |
| 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 Cybersecurity and privacy Data management and data science Machine learning Security evasion attacks federated learning and smart power grid Training Data models Smart grids Detectors Machine learning Smart meters Servers Federated learning Power grids |
| dc.title.none.fl_str_mv | Novel Evasion Attacks Against Adversarial Training Defense for Smart Grid Federated Learning |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <h3>Abstract</h3><p dir="ltr">In the advanced metering infrastructure (AMI) of the smart grid, smart meters (SMs) are deployed to collect fine-grained electricity consumption data, enabling billing, load monitoring, and efficient energy management. However, some consumers engage in fraudulent behavior by hacking their meters, leading to either traditional electricity theft or more sophisticated evasion attacks (EAs). EAs aim to illegally reduce electricity bills while deceiving theft detection mechanisms. The current methods for identifying such attacks raise privacy concerns due to the need for access to consumers’ detailed consumption data to train detection mechanisms. To address privacy concerns, federated learning (FL) is proposed as a collaborative training approach across multiple consumers. Adversarial training (AT) has shown promise in countering evasion threats on machine learning models. This paper, first, investigates the susceptibility of traditional electricity theft classifiers trained by FL to EAs for both independent and identically distributed (IID) and Non-IID consumption data. Then, it investigates the effectiveness of AT in securing the global electricity theft detector against EAs, assuming no misbehavior from the participant consumers in the FL process. After that, we introduce three novel attacks, namely Distillation, No-Adversarial-Sample-Training, and False-Labeling, which can be launched during the AT process to make the global model susceptible to evasion at inference time. Finally, extensive experiments are conducted to validate the severity of these proposed attacks. Our findings reveal that the AT can counter EAs effectively when the FL participants are honest, but it fails when they act maliciously and launch our attacks. This work lays the foundation for future endeavors in exploring additional countermeasures, in conjunction with AT, to bolster the security and resilience of FL machine learning models against adversarial attacks in the context of electricity theft detection.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0" 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.2023.3323617" target="_blank">https://dx.doi.org/10.1109/access.2023.3323617</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_5559b4c7b000f604b8f7e25e5cca7568 |
| identifier_str_mv | 10.1109/access.2023.3323617 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/26830168 |
| publishDate | 2023 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Novel Evasion Attacks Against Adversarial Training Defense for Smart Grid Federated LearningAtef H. Bondok (19482352)Mohamed Mahmoud (4544233)Mahmoud M. Badr (19482355)Mostafa M. Fouda (14768509)Mohamed Abdallah (3073191)Maazen Alsabaan (17714529)EngineeringElectrical engineeringInformation and computing sciencesCybersecurity and privacyData management and data scienceMachine learningSecurityevasion attacksfederated learningand smart power gridTrainingData modelsSmart gridsDetectorsMachine learningSmart metersServersFederated learningPower grids<h3>Abstract</h3><p dir="ltr">In the advanced metering infrastructure (AMI) of the smart grid, smart meters (SMs) are deployed to collect fine-grained electricity consumption data, enabling billing, load monitoring, and efficient energy management. However, some consumers engage in fraudulent behavior by hacking their meters, leading to either traditional electricity theft or more sophisticated evasion attacks (EAs). EAs aim to illegally reduce electricity bills while deceiving theft detection mechanisms. The current methods for identifying such attacks raise privacy concerns due to the need for access to consumers’ detailed consumption data to train detection mechanisms. To address privacy concerns, federated learning (FL) is proposed as a collaborative training approach across multiple consumers. Adversarial training (AT) has shown promise in countering evasion threats on machine learning models. This paper, first, investigates the susceptibility of traditional electricity theft classifiers trained by FL to EAs for both independent and identically distributed (IID) and Non-IID consumption data. Then, it investigates the effectiveness of AT in securing the global electricity theft detector against EAs, assuming no misbehavior from the participant consumers in the FL process. After that, we introduce three novel attacks, namely Distillation, No-Adversarial-Sample-Training, and False-Labeling, which can be launched during the AT process to make the global model susceptible to evasion at inference time. Finally, extensive experiments are conducted to validate the severity of these proposed attacks. Our findings reveal that the AT can counter EAs effectively when the FL participants are honest, but it fails when they act maliciously and launch our attacks. This work lays the foundation for future endeavors in exploring additional countermeasures, in conjunction with AT, to bolster the security and resilience of FL machine learning models against adversarial attacks in the context of electricity theft detection.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0" 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.2023.3323617" target="_blank">https://dx.doi.org/10.1109/access.2023.3323617</a></p>2023-10-11T12:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/access.2023.3323617https://figshare.com/articles/journal_contribution/Novel_Evasion_Attacks_Against_Adversarial_Training_Defense_for_Smart_Grid_Federated_Learning/26830168CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/268301682023-10-11T12:00:00Z |
| spellingShingle | Novel Evasion Attacks Against Adversarial Training Defense for Smart Grid Federated Learning Atef H. Bondok (19482352) Engineering Electrical engineering Information and computing sciences Cybersecurity and privacy Data management and data science Machine learning Security evasion attacks federated learning and smart power grid Training Data models Smart grids Detectors Machine learning Smart meters Servers Federated learning Power grids |
| status_str | publishedVersion |
| title | Novel Evasion Attacks Against Adversarial Training Defense for Smart Grid Federated Learning |
| title_full | Novel Evasion Attacks Against Adversarial Training Defense for Smart Grid Federated Learning |
| title_fullStr | Novel Evasion Attacks Against Adversarial Training Defense for Smart Grid Federated Learning |
| title_full_unstemmed | Novel Evasion Attacks Against Adversarial Training Defense for Smart Grid Federated Learning |
| title_short | Novel Evasion Attacks Against Adversarial Training Defense for Smart Grid Federated Learning |
| title_sort | Novel Evasion Attacks Against Adversarial Training Defense for Smart Grid Federated Learning |
| topic | Engineering Electrical engineering Information and computing sciences Cybersecurity and privacy Data management and data science Machine learning Security evasion attacks federated learning and smart power grid Training Data models Smart grids Detectors Machine learning Smart meters Servers Federated learning Power grids |