Amplitude-Belief Reinforcement Learning for Adaptive Cyber Defense in Partially Observable V2X Networks
The Internet of Vehicles (IoV) creates a partially observable and adversarial V2X communication environment in which malicious vehicles may evade defensive mechanisms. Existing IoV intrusion-detection methods provide limited support for sequential mitigation under adaptive attacker behavior. This paper formulates IoV cyber defense as a partially observable sequential decision problem and proposes Quantum Belief-Integrated Reinforcement Defense (Q-BIRD), an amplitude-belief reinforcement learning framework. Q-BIRD represents uncertainty over hidden attacker intent through a normalized complex-valued belief state and converts amplitudes into intent probabilities. The resulting belief features are used by a Proximal Policy Optimization defender to select cost-aware mitigation actions. Experiments are conducted in a SUMO-OMNeT++ and Veins V2X co-simulation environment. Q-BIRD reduces mean cumulative damage from 36.0 +- 5.5 to 28.0 +- 3.0 and damage variance from 12.0 +- 2.8 to 6.0 +- 1.5 compared with PPO using classical Bayesian belief. The attack success rate decreases to 0.05 +- 0.02, while survival probability increases to 0.96 +- 0.02. Communication-level results show that Q-BIRD maintains a packet delivery ratio of 0.94 +- 0.02, latency of 45 +- 6 ms, throughput of 3.60 +- 0.15 Mbps, and service availability of 0.95 +- 0.02. Explainability analysis using SHAP, LIME, and Grad-CAM suggests that belief-related features contribute strongly to mitigation decisions. These results indicate that amplitude-based belief modeling can improve both cyber-defense stability and V2X communication reliability under partial observability.