An AI-Enabled Multi-Agent Reinforcement Learning Framework for Secure and Adaptive Routing in Vehicular Ad Hoc Networks

Abstract

Vehicular Ad Hoc Networks (VANETs) are an essential component of intelligent transportation systems, enabling communication among vehicles and roadside infrastructure for traffic safety and efficient transportation management. However, the highly dynamic topology, rapid mobility, and growing cybersecurity threats make secure and reliable routing a major challenge. Traditional routing protocols fail to adapt efficiently to changing network conditions and malicious attacks. To address these issues, this paper proposes an Artificial Intelligence (AI)-enabled secure adaptive routing framework using Multi-Agent Reinforcement Learning (MARL). In the proposed model, each vehicle acts as an intelligent autonomous agent capable of learning optimal routing decisions through continuous interaction with the environment. The framework integrates AI-based trust evaluation and anomaly detection mechanisms to identify malicious nodes and improve routing security. Reinforcement learning techniques optimize routing performance using parameters such as packet delivery ratio, delay, throughput, link stability, and trust values. Simulation results demonstrate that the proposed framework significantly improves packet delivery ratio, reduces end-to-end delay, increases throughput, and achieves high malicious node detection accuracy compared to conventional routing protocols. The study highlights the effectiveness of integrating AI and cybersecurity techniques for next-generation intelligent vehicular communication systems.