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

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Bibliographic Details
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]
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Database:	Engineering Source

Description
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]
ISSN:	18245463
DOI:	10.53136/97912218273545