Enhancing Security in Vehicular Networks based on Blockchain Technology

Abstract

The rapid evolution of Vehicular Ad Hoc Networks (VANETs) has revolutionized transportation systems, enabling real-time communication between vehicles and infrastructure for enhanced safety, traffic management, and autonomous driving. However, the open and dynamic nature of VANETs makes them highly vulnerable to cyber-attacks, such as Sybil attacks, Wormhole attacks, and Denial of Service (DoS). These threats can compromise the integrity, availability, and confidentiality of the network, posing significant risks to road safety and user privacy.To address these challenges, the integration of Deep Learning (DL) and Machine Learning (ML) with Blockchain technology has emerged as a promising solution. DL/ML models, such as LSTM, CNN, and Naive Bayes, have demonstrated exceptional performance in detecting and mitigating attacks, achieving accuracies of up to 96%. These models excel in identifying complex attack patterns and adapting to the dynamic nature of VANETs. Meanwhile, Blockchain provides a decentralized, tamper-proof, and transparent framework for secure data sharing and storage, ensuring the integrity and trustworthiness of the network.This study explores the synergy between DL/ML and Blockchain to create a robust security framework for VANETs. By leveraging the strengths of both technologies, we aim to enhance real-time attack detection, secure data management, and overall network resilience. The findings highlight the potential of this integrated approach to address the growing security challenges in vehicular networks and pave the way for safer and more reliable intelligent transportation systems.