<![CDATA[Vehicular Ad Hoc Networks (VANETs) play a crucial role in enhancing road safety, traffic management, and driving efficiency through real-time communication between vehicles and infrastructure. However, VANETs are vulnerable to various security threats, one of which is the “ghost car” attack. In this attack, a malicious entity injects false information into the network, simulating the presence of a non-existent or “ghost” vehicle. This can lead to severe consequences such as traffic disruptions, accidents, and a compromised trust in the system’s reliability. This study aims to simulate and analyze the impacts of ghost car attacks on Vehicular Ad Hoc Networks (VANETs), focusing specifically on intersection waiting times and overall traffic flow. We used Simulation of Urban Mobility (SUMO) integrated with ns-3 for realistic VANET simulations, introducing varying numbers of ghost vehicles. Results indicate significant increases in waiting times and vehicle counts at intersections due to ghost cars, leading to traffic disruptions. This study evaluates ghost car attacks within realistic urban scenarios and proposes targeted detection and mitigation strategies, leveraging authentication, machine learning, and blockchain technologies.]]>
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