Fedcross‐VAN: Federated Cross‐Domain Behavior Alignment for VANET Intrusion Detection

ABSTRACT Vehicular ad hoc networks (VANETs) enable the exchange of safety‐critical messages, but their open and dynamic nature makes them vulnerable to message falsification and denial‐of‐service attacks. Federated learning (FL) offers a distributed defense mechanism, yet conventional approaches such as FedAvg degrade severely under non‐IID client data and are highly sensitive to adversarial updates. To address these limitations, we propose FedCross‐VAN, a novel FL framework that incorporates cross‐domain behavioral priors with a similarity‐weighted aggregation scheme . On the client side, FedCross‐VAN employs a dual‐objective loss that balances anomaly classification with alignment to external priors, improving generalization under heterogeneous data. On the server side, updates are aggregated proportionally to their embedding similarity with the priors, suppressing the influence of poisoned or noisy clients. Experiments on two benchmark datasets show that FedCross‐VAN achieves up to approximately 2%–4% higher accuracy on HAR and 13% on KWS, converges in fewer rounds, and exhibits stronger robustness than FedAvg and No‐Transfer FL. These findings establish FedCross‐VAN as a practical and resilient framework for anomaly detection in next‐generation intelligent transportation systems.