An AI-Enhanced Quantum Key Management for Cloud-Based Aviation Communication Systems
Received: 22 August 2025 | Revised: 12 September 2025 and 1 October 2025 | Accepted: 6 October 2025 | Online: 28 October 2025
Corresponding author: Lara Mohammad Hamza Shhab
Abstract
The emerging complexity of edge-based and autonomous UAV communication networks requires smart, secure, and adaptive architectures to defend against emerging cyber threats. Traditional access control and cryptographic key management systems, inherently static and rule-based, cannot support the demands of both real-time responsiveness and contextual decision-making. This paper presents a modular AI-enabled system that integrates Support Vector Machines (SVMs) for access control and Deep Q-Networks (DQNs) for adaptive encryption key rotation within a simulated quantum-secure communication channel. The proposed system was implemented with Docker and Kubernetes, with testing on NS-3 and SimulaQron platforms to allow scalable deployment and modularity. The SVM classifier achieved 96.8% accuracy and 99.2% recall in anomalous traffic, proving it to be effective in edge-based access control. The DQN agent is trained on the best key rotation policies and achieves 92.5% accuracy in the simulated environments of reinforcement learning, with stable convergence. These findings are in agreement with existing studies that recommend the use of AI to improve security in cloud-edge systems. The proposed framework provides an effective design blueprint of intelligent UAV communications based on low-latency inference and adaptive cryptographic policy control. Future work involves real-world implementation using Quantum Key Distribution (QKD) equipment, along with federated learning extensions to support collaborative intelligence across decentralized UAV swarms and edge devices.
Keywords:
Quantum communication, UAV networks, support vector machine, deep Q-network, edge AIDownloads
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