Quantum-Inspired Reinforcement Learning for Low-Latency Intrusion Detection in V2X and Internet-of-Vehicles Networks

• URL: http://arxiv.org/abs/2606.07804v1
• Date: Fri, 05 Jun 2026 19:33:15 GMT
• Title: Quantum-Inspired Reinforcement Learning for Low-Latency Intrusion Detection in V2X and Internet-of-Vehicles Networks
• Abstract summary: This paper proposes QIRL, a Quantum-Inspired Reinforcement Learning framework for next-generation autonomous cyber defense.<n>QIRL combines amplitude-phase quantum state encoding, rotation-gate-based exploration, and quantum interference reward augmentation within a cost-sensitive Markov Decision Process formulation.<n>It attains ultra-low inference latencies of 32.5 and 45.7 microseconds per sample, corresponding to 67.77 times and 51.77 times speedups over ensemble baselines.
• Score: 0.5098144539782359
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Smart cities increasingly depend on dense edge, IoT, and vehicular networks to deliver critical urban services, including traffic control, connected mobility, infrastructure monitoring, and energy management. In this ecosystem, the Internet of Vehicles (IoV) is central to intelligent transportation, enabling continuous communication among vehicles, roadside infrastructure, and cloud-edge platforms. This connectivity, however, also enlarges the attack surface and exposes smart city and vehicular systems to evolving cyber threats that can compromise safety, privacy, data integrity, and service continuity. Conventional static defenses are often inadequate because they cannot autonomously adapt to changing attack behaviors or multi-stage intrusion patterns. This paper proposes QIRL, a Quantum-Inspired Reinforcement Learning framework built on a lightweight Deep Q-Network architecture for next-generation autonomous cyber defense. QIRL combines amplitude-phase quantum state encoding, rotation-gate-based exploration, and quantum interference reward augmentation within a cost-sensitive Markov Decision Process formulation. It further addresses class imbalance through training-only SMOTE balancing and asymmetric cost-sensitive reward shaping, while sequential MDP modeling captures temporal dependencies in multi-stage attack campaigns. The framework is evaluated on CICIDS2017 and UNSW-NB15. QIRL achieves accuracies of 97.89\% and 91.04\%, F1-scores of 95.22\% and 91.66\%, AUC-ROC values of 0.9945 and 0.9713, and True Skill Statistics of 0.9443 and 0.8244, respectively. It also attains ultra-low inference latencies of 32.5 and 45.7 microseconds per sample, corresponding to 67.77 times and 51.77 times speedups over ensemble baselines. These results show that QIRL offers a lightweight, latency-aware, and adaptive defense for smart city and IoV infrastructures.

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