Blockchain and Explainable Federated Learning-based V2X Communication Framework for ITS in 6G

Abstract

One of the main issues with the Industrial Internet of Things (IIoT) in V2X communication is the threat of attacks. A comprehensive Intrusion Detection System (IDS) and a transparent ledger are important for providing an Intelligent Transportation System (ITS) beyond 5G. However, another major problem is that it is centralized and lacks a clear explanation of traditional IDS. By integrating Federated Learning (FL) to make it distributed and Explainable AI (XAI) to add a brain to the black box model to add the explanation factor, we make the model more robust and suitable for real-life situations. In this approach, we experimented using the X-IIOTID dataset. This dataset is a real-time indicator of the attack in an IIoT network such as V2X. It provides difficult and real-time scenarios that highlight the complexity of IDS. Furthermore, the benign data the model classifies is stored in the blockchain to make the system secure and transparent. Our FL-XAI-based technique provides an accuracy of $98 \%$ results than previous models. The proposed approach provides a clear and brief view of factors that affect classification actions, which helps users make security decisions. Evaluation of Pravah based on latency, accuracy, precision, recall, F1-score, and ROC-AUC confirms its effectiveness. This study contributes towards a more secure and interpretable ITS, bridging the gap between model performance and real-world applicability.