Detection and Mitigation of SQL and Jamming Attacks on Switched Beam Antenna in V2V Networks Using Federated Learning

This research article proposes a federated learning approach for detecting and mitigating SQL injection and jamming attacks on switched beam antenna in V2V networks. The proposed approach utilizes the collective intelligence of multiple nodes in the network to train a machine learning model for detecting malicious traffic patterns. We evaluate the performance of the proposed approach using both simulated and real-world V2V network data, and demonstrate its effectiveness in improving network security and performance. Our results show that the proposed approach can significantly reduce the latency and throughput overhead associated with conventional security mechanisms, while maintaining high levels of accuracy in detecting attacks.