Secure Data Dissemination Framework for Internet of Vehicles Environment

Abstract

Smart cities implement sustainable transport ecosystems by connecting intelligent vehicles through sensors and networking units. The Internet of Vehicles (IoVs) is one of the crucial infrastructure, which transmit messages related to road safety, precise location sharing, road incidents, traffic blocks, collision warnings, driver assistance, network congestion, and toll payments among vehicle-to-everything (V2X) units. This communication primarily relies on exchanging basic safety messages (BSMs) to convey kinematic information. However, the susceptibility of BSMs to false data injection attacks poses a significant threat to the normal operation of intelligent transportation systems (ITS), potentially resulting in data collisions and severe network congestion. Thus, due to the mission-critical nature of the IoVs ecosystem, there is a need for reliable, lightweight, and real time communication for vehicle-to-vehicle (V2V), which enables vehicles to exchange information with nearby vehicles and V2X units, which aim to connect vehicles with everything around them to enhance safety and efficiency in IoVs environment. However, insecure wireless channels such as Wireless Fidelity (Wi-Fi), Bluetooth, and cellular networks (4G, 5G, and 6G) introduce a high possibility of security attacks, such as replay, password guessing, masquerade, message tampering, and man-in-the-middle attacks (MitM), leading to potential disruptions in the IoVs ecosystem. The broadcasting of BSMs and other relevant data to neighbouring vehicles is carried out using the message dissemination process. Message dissemination involves sharing critical information for the safety and convenience of the network. It is crucial to secure the message dissemination process in the IoVs environment due to potential cyber-attacks, traffic disrup tions, and privacy breaches. To tackle the aforementioned security threats in resource-constrained IoVs, lightweight security and protocols are crucial for efficient communication, enhancing safety, and transportation efficiency. This thesis offers a dual approach, i.e., an machine-learning (ML)- based intelligent network management scheme and a secure and lightweight message dissemination framework to confront security threats in resource constrained IoVs.