Attack Detection of Federated Learning Model Based on Attention Mechanism Optimization in Connected Vehicles

To address the problem of decreased model accuracy and poor global aggregation performance among existing methods in non-independent and identically distributed (non-IID) data backgrounds, the author proposes a method for attack detection in the Internet of Vehicles based on the attention mechanism optimization of federated learning models. The author uses a combination of CNN and LSTM as the basic detection framework, integrating self-attention modules to optimize the spatiotemporal feature modeling effect. At the same time, an adaptive aggregation algorithm based on attention weights was designed in the federated aggregation stage, providing the model with stronger stability and generalization ability when dealing with data differences among nodes. In order to comprehensively evaluate the performance of the model, the experimental part is based on real datasets such as CICDDoS2019. The experimental results show that the federated learning model based on attention mechanism optimization proposed by the author demonstrates significant advantages in the task of detecting vehicle networking attacks. Compared with traditional methods, the new model improves attack detection accuracy by more than 5% in non-IID data environments, accelerates aggregation convergence speed, reduces aggregation epochs by more than 20%, and achieves stronger data privacy protection and real-time defense capabilities. Conclusion: This method not only improves the adaptability of the model in complex vehicle networking environments, but also effectively reduces the overall computational and communication overhead of the system.