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The absence of robust security protocols renders the VANET (Vehicle ad-hocNetworks) network open to cyber threats by compromising passengers and roadsafety. Intrusion Detection Systems (IDS) are widely employed to detect networksecurity threats. With vehicles' high mobility on the road and diverseenvironments, VANETs devise ever-changing network topologies, lack privacy andsecurity, and have limited bandwidth efficiency. The absence of privacyprecautions, End-to-End Encryption methods, and Local Data Processing systemsin VANET also present many privacy and security difficulties. So, assessingwhether a novel real-time processing IDS approach can be utilized for thisemerging technology is crucial. The present study introduces a novel approachfor intrusion detection using Federated Learning (FL) capabilities inconjunction with the BERT model for sequence classification (FL-BERT). Thesignificance of data privacy is duly recognized. According to FL methodology,each client has its own local model and dataset. They train their modelslocally and then send the model's weights to the server. After aggregation, theserver aggregates the weights from all clients to update a global model. Afteraggregation, the global model's weights are shared with the clients. Thispractice guarantees the secure storage of sensitive raw data on individualclients' devices, effectively protecting privacy. After conducting thefederated learning procedure, we assessed our models' performance using aseparate test dataset. The FL-BERT technique has yielded promising results,opening avenues for further investigation in this particular area of research.We reached the result of our approaches by comparing existing research worksand found that FL-BERT is more effective for privacy and security concerns. Ourresults suggest that FL-BERT is a promising technique for enhancing attackdetection.

Privacy-Preserving Intrusion Detection in Software-defined VANET using Federated Learning with BERT