Belief-Space Quantum-Inspired Reinforcement Learning for Partially Observable Autonomous Cyber Defense in the Internet of Vehicles
For IoV security, Q-BIRD offers a novel approach to handle uncertainty in adversarial sequential decision-making, achieving substantial improvements over classical Bayesian methods.
The paper tackles autonomous cyber defense in the Internet of Vehicles under partial observability, proposing a quantum-inspired belief representation (Q-BIRD) that reduces cumulative damage by 60.4%, damage variance by 90.2%, and attack success rate by 50.0% compared to baselines.
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. We propose Quantum Belief-Integrated Reinforcement Defense (Q-BIRD), using quantum-inspired belief representation to encode defender uncertainty about hidden attacker intent via amplitude-based states, enabling non-Bayesian belief evolution. Integrated into a Proximal Policy Optimization (PPO) defender, Q-BIRD selects cost-aware mitigation actions. In simulated environments with adaptive, probing attackers, Q-BIRD reduced cumulative mean damage, damage variance, and attack success rate (ASR) by 60.4%, 90.2%, and 50.0%, respectively, while increasing survival probability by 46.4%. Compared to classical Bayesian PPO, damage variance reduction and ASR improved by 10.2 times and 50%. Ablation and explainability analyses confirm that amplitude-based belief is the primary decision signal during strategy transitions when classical belief collapses, providing superior IoV security without additional hardware.