A Secure Object Detection Technique for Intelligent Transportation Systems
Federated Learning is a decentralized machine learning technique that creates a global model by aggregating local models from multiple edge devices without a need to access the local data. However, due to the distributed nature of federated learning, there is a larger attack surface, making cyber-at...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Open Journal of Intelligent Transportation Systems |
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| Online Access: | https://ieeexplore.ieee.org/document/10630660/ |
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| author | Jueal Mia M. Hadi Amini |
| author_facet | Jueal Mia M. Hadi Amini |
| author_sort | Jueal Mia |
| collection | DOAJ |
| description | Federated Learning is a decentralized machine learning technique that creates a global model by aggregating local models from multiple edge devices without a need to access the local data. However, due to the distributed nature of federated learning, there is a larger attack surface, making cyber-attack detection and defense challenging. Although prior works developed various defense strategies to address security issues in federated learning settings, most approaches fail to mitigate cyber-attacks due to the diverse characteristics of the attack, edge devices, and data distribution. To address this issue, this paper develops a hybrid privacy-preserving algorithm to safeguard federated learning methods against malicious attacks in Intelligent Transportation Systems, considering object detection as a downstream machine learning task. This algorithm involves the edge devices (e.g., autonomous vehicles) and road side units to collaboratively train their model while maintaining the privacy of their respective data. Furthermore, this hybrid algorithm provides robust security against data poisoning-based model replacement and inference attacks throughout the training phase. We evaluated our model using the CIFAR10 and LISA traffic light dataset, demonstrating its ability to mitigate malicious attacks with minimal impact on the performance of main tasks. |
| format | Article |
| id | doaj-art-2231ae17b10f4a4cb3e1752cbe85b312 |
| institution | Kabale University |
| issn | 2687-7813 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of Intelligent Transportation Systems |
| spelling | doaj-art-2231ae17b10f4a4cb3e1752cbe85b3122025-01-24T00:02:49ZengIEEEIEEE Open Journal of Intelligent Transportation Systems2687-78132024-01-01549550810.1109/OJITS.2024.344087610630660A Secure Object Detection Technique for Intelligent Transportation SystemsJueal Mia0https://orcid.org/0000-0002-9383-8739M. Hadi Amini1https://orcid.org/0000-0002-2768-3601Knight Foundation School of Computing and Information Sciences, Florida International University, Miami, FL, USASustainability, Optimization, and Learning for InterDependent Networks Laboratory (Solid Lab), Florida International University, Miami, FL, USAFederated Learning is a decentralized machine learning technique that creates a global model by aggregating local models from multiple edge devices without a need to access the local data. However, due to the distributed nature of federated learning, there is a larger attack surface, making cyber-attack detection and defense challenging. Although prior works developed various defense strategies to address security issues in federated learning settings, most approaches fail to mitigate cyber-attacks due to the diverse characteristics of the attack, edge devices, and data distribution. To address this issue, this paper develops a hybrid privacy-preserving algorithm to safeguard federated learning methods against malicious attacks in Intelligent Transportation Systems, considering object detection as a downstream machine learning task. This algorithm involves the edge devices (e.g., autonomous vehicles) and road side units to collaboratively train their model while maintaining the privacy of their respective data. Furthermore, this hybrid algorithm provides robust security against data poisoning-based model replacement and inference attacks throughout the training phase. We evaluated our model using the CIFAR10 and LISA traffic light dataset, demonstrating its ability to mitigate malicious attacks with minimal impact on the performance of main tasks.https://ieeexplore.ieee.org/document/10630660/Object detectioncyber-attacksprivacyintelligent transportation systemsdata poisoning-based model replacement attackinference attack |
| spellingShingle | Jueal Mia M. Hadi Amini A Secure Object Detection Technique for Intelligent Transportation Systems IEEE Open Journal of Intelligent Transportation Systems Object detection cyber-attacks privacy intelligent transportation systems data poisoning-based model replacement attack inference attack |
| title | A Secure Object Detection Technique for Intelligent Transportation Systems |
| title_full | A Secure Object Detection Technique for Intelligent Transportation Systems |
| title_fullStr | A Secure Object Detection Technique for Intelligent Transportation Systems |
| title_full_unstemmed | A Secure Object Detection Technique for Intelligent Transportation Systems |
| title_short | A Secure Object Detection Technique for Intelligent Transportation Systems |
| title_sort | secure object detection technique for intelligent transportation systems |
| topic | Object detection cyber-attacks privacy intelligent transportation systems data poisoning-based model replacement attack inference attack |
| url | https://ieeexplore.ieee.org/document/10630660/ |
| work_keys_str_mv | AT juealmia asecureobjectdetectiontechniqueforintelligenttransportationsystems AT mhadiamini asecureobjectdetectiontechniqueforintelligenttransportationsystems AT juealmia secureobjectdetectiontechniqueforintelligenttransportationsystems AT mhadiamini secureobjectdetectiontechniqueforintelligenttransportationsystems |