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Unmanned Aerial Vehicle (UAV) swarms offer remarkable capabilities across numerous fields, performing complex tasks with high efficiency and adaptability. However, safeguarding these swarms from cyber threats poses a significant challenge. This paper addresses “Challenge 4: Enhanced Communication and Active Protection Framework”. We aim to solve key objectives by introducing a comprehensive framework aimed at bolstering the security and coordination of UAV swarms. Our framework incorporates communications-aware trajectory planning, the use of heterogeneous communication networks, advanced physical layer security measures, and Artificial Intelligence (AI)-driven strategies for detecting and mitigating attacks. By combining Optical Camera Communications (OCC) with conventional Radio Frequency (RF) systems and utilizing Reinforcement Learning (RL) and Federated Learning (FL), the proposed framework provides a robust, efficient, and secure operational environment for UAV swarms.

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Enhanced Security and Coordination Framework for UAV Swarms Using Heterogeneous Communication Networks

  • 会议论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年

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  • Daniel Bonilla Licea,
  • Giuseppe Silano,
  • Martin Saska

摘要

Unmanned Aerial Vehicle (UAV) swarms offer remarkable capabilities across numerous fields, performing complex tasks with high efficiency and adaptability. However, safeguarding these swarms from cyber threats poses a significant challenge. This paper addresses “Challenge 4: Enhanced Communication and Active Protection Framework”. We aim to solve key objectives by introducing a comprehensive framework aimed at bolstering the security and coordination of UAV swarms. Our framework incorporates communications-aware trajectory planning, the use of heterogeneous communication networks, advanced physical layer security measures, and Artificial Intelligence (AI)-driven strategies for detecting and mitigating attacks. By combining Optical Camera Communications (OCC) with conventional Radio Frequency (RF) systems and utilizing Reinforcement Learning (RL) and Federated Learning (FL), the proposed framework provides a robust, efficient, and secure operational environment for UAV swarms.