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

Abstract

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.

Blockchain-Enabled Federated Learning Approach for Vehicular Networks

“…The primary objective is to augment the security of data-sharing among autonomous vehicles. The proposed model also demonstrates the efficacy of validation in [6] SVM+ Federated Learning × 82.45% Kakkar et al [29] Random Forest + Blockchain × 93% Otoum et al [9] Federated Learning + Blockchain × 97% Rajan et al [30] Multi-layered federated extreme learning + Blockchain × 98% Chen et al [31] Federated The model presented in our study is not merely a training algorithm, but rather a network configuration strategy designed to train models while preserving data privacy and maintaining consistent global accuracy, even in challenging conditions. Moving forward, we plan to refine the algorithm, improve concurrent processing, and better manage threads.…”

Blockchain-Enabled Federated Learning Approach for Vehicular Networks

“…The primary objective is to augment the security of data-sharing among autonomous vehicles. The proposed model also demonstrates the efficacy of validation in [6] SVM+ Federated Learning × 82.45% Kakkar et al [29] Random Forest + Blockchain × 93% Otoum et al [9] Federated Learning + Blockchain × 97% Rajan et al [30] Multi-layered federated extreme learning + Blockchain × 98% Chen et al [31] Federated The model presented in our study is not merely a training algorithm, but rather a network configuration strategy designed to train models while preserving data privacy and maintaining consistent global accuracy, even in challenging conditions. Moving forward, we plan to refine the algorithm, improve concurrent processing, and better manage threads.…”