FedAV: Federated learning for cyberattack vulnerability and resilience of cooperative driving automation
Cooperative driving automation (CDA) has gained attention over the years because of its cooperative driving capability that provides solution to individual automated driving challenges. Although reliance on communication and automation enables cooperative driving, it also introduces new cybersecurit...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-12-01
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| Series: | Communications in Transportation Research |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772424725000150 |
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| author | Guanyu Lin Sean Qian Zulqarnain H. Khattak |
| author_facet | Guanyu Lin Sean Qian Zulqarnain H. Khattak |
| author_sort | Guanyu Lin |
| collection | DOAJ |
| description | Cooperative driving automation (CDA) has gained attention over the years because of its cooperative driving capability that provides solution to individual automated driving challenges. Although reliance on communication and automation enables cooperative driving, it also introduces new cybersecurity threats. This study introduces a federated learning concept for autonomous and connected vehicles, known as the federated agents on vehicle platooning (FedAV) framework, which is designed to address the challenges of cyberattack simulations and anomaly detection in cooperative vehicle platooning systems. The federated learning approach was adopted because of its decentralized nature, which allows each vehicle to learn independently with the ability to overcome adversarial attacks. First, FedAV employs a mixed cyberattack simulation approach to capture complex attack patterns effectively. We tested the scalability of our approach against several attacks, including spoofing, message falsification, and replay attacks, as well as on anomalies, including short anomalies, noise anomalies, bias anomalies, and gradual shifts. In addition, our approach integrates federated learning for decentralized anomaly detection, ensuring data privacy and reducing communication overhead. The anomaly detection performance was enhanced by average and weighted aggregation strategies. Real-world scenarios from cooperative driving experiments and simulations validated the framework's effectiveness and demonstrated its potential to improve the safety, privacy, and efficiency of CDA. |
| format | Article |
| id | doaj-art-90e29b2617c44809aa880cb558e7b345 |
| institution | DOAJ |
| issn | 2772-4247 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Communications in Transportation Research |
| spelling | doaj-art-90e29b2617c44809aa880cb558e7b3452025-08-20T03:06:43ZengElsevierCommunications in Transportation Research2772-42472025-12-01510017510.1016/j.commtr.2025.100175FedAV: Federated learning for cyberattack vulnerability and resilience of cooperative driving automationGuanyu Lin0Sean Qian1Zulqarnain H. Khattak2Civil and Environmental Engineering, Carnegie Mellon University, Pittsburgh, PA, 15213, USACivil and Environmental Engineering, Carnegie Mellon University, Pittsburgh, PA, 15213, USATransportation and Urban Infrastructure Studies, Morgan State University, Baltimore, MD, 21251, USA; Corresponding author.Cooperative driving automation (CDA) has gained attention over the years because of its cooperative driving capability that provides solution to individual automated driving challenges. Although reliance on communication and automation enables cooperative driving, it also introduces new cybersecurity threats. This study introduces a federated learning concept for autonomous and connected vehicles, known as the federated agents on vehicle platooning (FedAV) framework, which is designed to address the challenges of cyberattack simulations and anomaly detection in cooperative vehicle platooning systems. The federated learning approach was adopted because of its decentralized nature, which allows each vehicle to learn independently with the ability to overcome adversarial attacks. First, FedAV employs a mixed cyberattack simulation approach to capture complex attack patterns effectively. We tested the scalability of our approach against several attacks, including spoofing, message falsification, and replay attacks, as well as on anomalies, including short anomalies, noise anomalies, bias anomalies, and gradual shifts. In addition, our approach integrates federated learning for decentralized anomaly detection, ensuring data privacy and reducing communication overhead. The anomaly detection performance was enhanced by average and weighted aggregation strategies. Real-world scenarios from cooperative driving experiments and simulations validated the framework's effectiveness and demonstrated its potential to improve the safety, privacy, and efficiency of CDA.http://www.sciencedirect.com/science/article/pii/S2772424725000150CybersecurityCyberattackConnected automated vehiclesAnomaly detectionFederated learningAgent |
| spellingShingle | Guanyu Lin Sean Qian Zulqarnain H. Khattak FedAV: Federated learning for cyberattack vulnerability and resilience of cooperative driving automation Communications in Transportation Research Cybersecurity Cyberattack Connected automated vehicles Anomaly detection Federated learning Agent |
| title | FedAV: Federated learning for cyberattack vulnerability and resilience of cooperative driving automation |
| title_full | FedAV: Federated learning for cyberattack vulnerability and resilience of cooperative driving automation |
| title_fullStr | FedAV: Federated learning for cyberattack vulnerability and resilience of cooperative driving automation |
| title_full_unstemmed | FedAV: Federated learning for cyberattack vulnerability and resilience of cooperative driving automation |
| title_short | FedAV: Federated learning for cyberattack vulnerability and resilience of cooperative driving automation |
| title_sort | fedav federated learning for cyberattack vulnerability and resilience of cooperative driving automation |
| topic | Cybersecurity Cyberattack Connected automated vehicles Anomaly detection Federated learning Agent |
| url | http://www.sciencedirect.com/science/article/pii/S2772424725000150 |
| work_keys_str_mv | AT guanyulin fedavfederatedlearningforcyberattackvulnerabilityandresilienceofcooperativedrivingautomation AT seanqian fedavfederatedlearningforcyberattackvulnerabilityandresilienceofcooperativedrivingautomation AT zulqarnainhkhattak fedavfederatedlearningforcyberattackvulnerabilityandresilienceofcooperativedrivingautomation |