Cloud-based Sybil Attack Detection Scheme for Connected Vehicles

Automated and connected vehicle technologies are among the most heavily researched automotive technologies. As a part of an Intelligent Transportation System (ITS), connected vehicles provide useful information to drivers and the infrastructure to help make safer and more informed decisions. However, vehicle connectivity has made the ITS more vulnerable to security attacks that can endanger vehicle's security as well as driver's safety. Sybil attack is a very common attack, considered dangerous in a distributed network with no centralized authority. When launched against connected vehicles, it consists of controlling a set of vehicles with forged or fake identities to try to alter the measurements and data collected by the ITS, leading to sub-optimal decisions. In this paper, we provide a cloud-based detection scheme for connected vehicles against such an attack. Contrary to the previous distributed solutions in the literature, this paper presents a cloud-based solution that integrates a cloud-based authorization unit to authenticate legitimate nodes using symmetric cryptography and real-time location tracking. As a centralized authentication system, cloud computing is more reliable and secure in managing the vehicle as a device than any other infrastructure in the vehicular network and can provide real-time visibility. A trust evaluation approach is also integrated into the scheme to drive the decisions of the vehicles concerning potential collaborations. The performed experiment and security analysis show the efficacy of our proposed cloud-based solution in terms of detection rate, complexity and system requirements.