A Blockchain Framework for Securing Connected and Autonomous Vehicles

TLDR

Connected and autonomous vehicles are rapidly emerging, demanding secure, seamless information exchange, yet malicious actors threaten communication by compromising smart devices. The study aims to secure smart sensors in connected vehicles by proposing a blockchain framework. The framework employs blockchain technology to provide real‑time secrecy and protection for vehicle control systems, safeguarding against fake requests, device compromise, probabilistic authentication failures, and rating alterations. Evaluation against existing methods shows the framework achieves a 79 % success rate in mitigating these security threats.

Abstract

Recently, connected vehicles (CV) are becoming a promising research area leading to the concept of CV as a Service (CVaaS). With the increase of connected vehicles and an exponential growth in the field of online cab booking services, new requirements such as secure, seamless and robust information exchange among vehicles of vehicular networks are emerging. In this context, the original concept of vehicular networks is being transformed into a new concept known as connected and autonomous vehicles. Autonomous vehicular use yields a better experience and helps in reducing congestion by allowing current information to be obtained by the vehicles instantly. However, malicious users in the internet of vehicles may mislead the whole communication where intruders may compromise smart devices with the purpose of executing a malicious ploy. In order to prevent these issues, a blockchain technique is considered the best technique that provides secrecy and protection to the control system in real time conditions. In this paper, the issue of security in smart sensors of connected vehicles that can be compromised by expert intruders is addressed by proposing a blockchain framework. This study has further identified and validated the proposed mechanism based on various security criteria, such as fake requests of the user, compromise of smart devices, probabilistic authentication scenarios and alteration in stored user's ratings. The results have been analyzed against some existing approach and validated with improved simulated results that offer 79% success rate over the above-mentioned issues.