Communication-Aware Quantum-Inspired Reinforcement Learning for Cyber-Resilient V2X Intrusion Detection and Mitigation

Abstract

Smart cities rely on Internet of Vehicles (IoV) networks for critical services. However, this vast connectivity enlarges the attack surface, exposing vehicular systems to evolving cyber threats. Conventional static defenses struggle to autonomously adapt to these dynamic, multi-stage intrusions. To address this, we propose the Communication Aware Quantum Inspired Reinforcement Learning (CA-QIRL) framework, built on a lightweight deep Q-Network architecture for autonomous cyber defense. V2X defense is formulated as a communication-aware Markov Decision Process (MDP). The agent observes intrusion, mobility, Road Side Unit (RSU), and communication metrics to select optimal mitigation actions. CA-QIRL integrates quantum-inspired encoding, rotation exploration, and an interference reward, combined with a cost function penalizing false negatives, false positives, delay, packet loss, and RSU overload. Experimental evaluations on vehicular intrusion datasets and a mobility-aware V2X simulation demonstrate robust performance. CA-QIRL achieves competitive detection accuracies of 97.89% on CICIDS2017 and 80.31% on CAN-MIRGU, outperforming state-of-the-art ensemble methods in inference latency. Furthermore, end-to-end delay and Channel Busy Ratio (CBR) drop by up to 95.7% and 90%. Statistical significance is confirmed on ROAD and VeReMi. These findings establish CA-QIRL as a highly practical and resilient defense mechanism for next-generation V2X and IoV networks.