Adaptive Deep Reinforcement Learning for Resilient Cybersecurity in Smart-Logistics V2X Networks

Smart-city logistics increasingly depend on autonomous vehicles, unmanned aerial vehicles (UAVs), and roadside infrastructure that exchange safety beacons, sensor data, and control messages over vehicle-to-everything (V2X) links. These communications must satisfy strict latency and reliability constraints while remaining robust against evolving network behaviour and adaptive cyber-attacks. Conventional intrusion-detection or rule-based security mechanisms often degrade under concept drift and require frequent retraining. This paper proposes a lightweight, resource-aware cybersecurity framework for resilient logistics communication. A supervised CNN–BiLSTM encoder is trained offline on labeled traffic to learn temporal–spatial flow representations and then frozen to provide a stable 128-dimensional embedding space. On top of these embeddings, a contextual-bandit Deep Q-Network (DQN) acts as an adaptive policy head that classifies traffic efficiently using a cost-aware reward function. Warm-start imitation from the encoder’s soft outputs and an annealed auxiliary loss enable fast, stable convergence. Experiments on the UAVIDS-2025 dataset achieve a test accuracy of 99.14 %, precision of 99.33 %, recall of 99.58 %, and an AUC of 0.999. These results confirm that the proposed hybrid CNN–BiLSTM + DQN model delivers intelligent, resilient, and adaptive protection for secure autonomous-logistics communication in smart-city environments.