Blockchain-based multi-layered federated extreme learning networks in connected vehicles

Intelligent and networked vehicles help build an efficient vehicular network's infrastructure. The widespread use of electronic software exposes these networks to cyber-attacks. Intrusion detection systems (IDS) are useful for preventing vehicle network assaults. IDS have been customized using machine and deep learning networks for greater real-time performance. Current learning-based intrusion detection systems demand substantial processing capabilities to train and update intricate training models in vehicular devices, resulting in decreased efficiency and ability to defend against assaults. This study presents Blockchain-based Multi-Layer Federated Extreme Learning Machines (MLFEM) enabled IDS (BEF-IDS) for safe data transfers. The proposed IDS leverages federated learning to generate Multi-Layered Extreme Learning Machines, which are offloaded to dispersed vehicular edge devices such as Road-Side Units (RSU) and connected vehicles. This federated strategy decreases resource use without sacrificing security. Blockchain technology records and shares training models, assuring network security. Using real-time data sets, the suggested algorithm's performance under different attack scenarios were extensively tested. The suggested method obtained 98% accuracy and Recall, 97.9% Precision, and 97.9% F1 Score performance, which suggests it's incredibly secure and costs very little to transmit.