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Large-scale vehicle anomaly attack behavior perception and real-time early warning based on federated learning and edge computing.

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Title: Large-scale vehicle anomaly attack behavior perception and real-time early warning based on federated learning and edge computing.
Authors: Li, J. L.1 Lijinling20251230@163.com, Liu, D. L.2 ldl@cuit.edu.cn
Source: Advances in Transportation Studies. Jul2026, Vol. 69, p77-90. 14p.
Subjects: Federated learning, Edge computing, Anomaly detection (Computer security), Intelligent transportation systems, Security management
Abstract: This study proposes a collaborative perception and early warning framework integrating federated learning and edge computing, aiming to achieve real-time detection of large-scale abnormal vehicle attack behaviors. At the perception level, federated learning is used to achieve distributed abnormal behavior modeling and cross-node perception; at the early warning level, edge computing is used for data preprocessing to achieve hierarchical response. Experimental findings show that the introduced model achieves recognition accuracies of 93.1% and 93.8% on public datasets. For real-time early warning, the average latency is only 15.2ms, and the system throughput reaches 1250 requests/s. In real-world road scenarios, a recall rate of 97.2% is achieved, with false positive and false negative rates below 2.1% and 1.8%, respectively, validating the effectiveness of the strategy. This research provides a feasible path for anomaly detection in vehicle-to-everything systems and has practical significance for improving the security of intelligent transportation systems. [ABSTRACT FROM AUTHOR]
Copyright of Advances in Transportation Studies is the property of Advances in Transportation Studies and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: <searchLink fieldCode="JN" term="%22Advances+in+Transportation+Studies%22">Advances in Transportation Studies</searchLink>. Jul2026, Vol. 69, p77-90. 14p.
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  Data: <searchLink fieldCode="DE" term="%22Federated+learning%22">Federated learning</searchLink><br /><searchLink fieldCode="DE" term="%22Edge+computing%22">Edge computing</searchLink><br /><searchLink fieldCode="DE" term="%22Anomaly+detection+%28Computer+security%29%22">Anomaly detection (Computer security)</searchLink><br /><searchLink fieldCode="DE" term="%22Intelligent+transportation+systems%22">Intelligent transportation systems</searchLink><br /><searchLink fieldCode="DE" term="%22Security+management%22">Security management</searchLink>
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  Label: Abstract
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  Data: This study proposes a collaborative perception and early warning framework integrating federated learning and edge computing, aiming to achieve real-time detection of large-scale abnormal vehicle attack behaviors. At the perception level, federated learning is used to achieve distributed abnormal behavior modeling and cross-node perception; at the early warning level, edge computing is used for data preprocessing to achieve hierarchical response. Experimental findings show that the introduced model achieves recognition accuracies of 93.1% and 93.8% on public datasets. For real-time early warning, the average latency is only 15.2ms, and the system throughput reaches 1250 requests/s. In real-world road scenarios, a recall rate of 97.2% is achieved, with false positive and false negative rates below 2.1% and 1.8%, respectively, validating the effectiveness of the strategy. This research provides a feasible path for anomaly detection in vehicle-to-everything systems and has practical significance for improving the security of intelligent transportation systems. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Advances in Transportation Studies is the property of Advances in Transportation Studies and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Text: English
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        PageCount: 14
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    Subjects:
      – SubjectFull: Federated learning
        Type: general
      – SubjectFull: Edge computing
        Type: general
      – SubjectFull: Anomaly detection (Computer security)
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      – SubjectFull: Intelligent transportation systems
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      – SubjectFull: Security management
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              Text: Jul2026
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              Y: 2026
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