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Belief-Space Quantum-Inspired Reinforcement Learning for Partially Observable Autonomous Cyber Defense in the Internet of Vehicles
arXiv:2606.07796v1 Announce Type: new Abstract: The Internet of Vehicles (IoV) faces a dynamic, adversarial security environment where attackers adapt to defenses. Existing intrusion detection systems rely on static classifiers that fail to capture sequential decision-making, attacker adaptation, and uncertainty. We formulate IoV security as a sequential attacker-defender interaction and model defense as a reinforcement learning problem under partial observability.