Resumen
The development of advanced Intelligent Transportation Systems has been made possible by the rapid expansion of autonomous vehicles (AVs) and networking technology (ITS). The in-vehicle users' increased data needs from AVs put the vehicle's trajectory data in danger and make it more susceptible to security threats. In this paper, Autonomous vehicles (AVs) transform the intelligent transportation system by exchanging real-time and seamless data with other AVs and the network (ITS). Transportation that is automated has many advantages for people. However, worries about safety, security, and privacy continue to grow. The AVs need to exchange sensory data with other AVs and with their own for navigation and trajectory planning. When an unreliable sensor-equipped AV or one that is malicious enters connectivity in such circumstances, the results could be disruptive. To effectively detect anomalies and mitigate cyberattacks in AVs, this study suggests the Efficient Anomaly Detection (EAD) method. The EAD technique finds and isolates rogue AVs using the Multi-Agent Reinforcement Learning (MARL) algorithm, which operates over the 6G network to thwart modern cyberattacks and provide a quick and accurate anomaly detection mechanism. The expected outcomes demonstrate the value of EAD and have an accuracy rate that is 8.01% greater than that of the current systems.