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

TL;DR

This paper proposes a novel privacy-preserving intrusion detection system for VANETs using federated learning combined with BERT, demonstrating improved effectiveness in detecting cyber threats while maintaining data privacy.

Contribution

Introduces FL-BERT, a federated learning-based intrusion detection approach utilizing BERT for sequence classification in VANETs, enhancing privacy and security.

Findings

FL-BERT outperforms existing methods in attack detection accuracy.

The approach effectively preserves data privacy by local training.

Promising results suggest potential for real-time intrusion detection in VANETs.

Abstract

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.