  The absence of robust security protocols renders the VANET (Vehicle ad-hoc
Networks) network open to cyber threats by compromising passengers and road
safety. Intrusion Detection Systems (IDS) are widely employed to detect network
security threats. With vehicles' high mobility on the road and diverse
environments, VANETs devise ever-changing network topologies, lack privacy and
security, and have limited bandwidth efficiency. The absence of privacy
precautions, End-to-End Encryption methods, and Local Data Processing systems
in VANET also present many privacy and security difficulties. So, assessing
whether a novel real-time processing IDS approach can be utilized for this
emerging technology is crucial. The present study introduces a novel approach
for intrusion detection using Federated Learning (FL) capabilities in
conjunction with the BERT model for sequence classification (FL-BERT). The
significance of data privacy is duly recognized. According to FL methodology,
each client has its own local model and dataset. They train their models
locally and then send the model's weights to the server. After aggregation, the
server aggregates the weights from all clients to update a global model. After
aggregation, the global model's weights are shared with the clients. This
practice guarantees the secure storage of sensitive raw data on individual
clients' devices, effectively protecting privacy. After conducting the
federated learning procedure, we assessed our models' performance using a
separate 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 works
and found that FL-BERT is more effective for privacy and security concerns. Our
results suggest that FL-BERT is a promising technique for enhancing attack
detection.
