Secure Data Transmission Based on Reinforcement Learning and Position Confusion for Internet of UAVs

文献类型： 外文期刊

第一作者： Zhu, Xiuzhen

作者： Zhu, Xiuzhen;Lin, Limei;Wang, Xiaoding;Huang, Yanze;Wang, Xiaoding;Que, Youxiong;Que, Youxiong;Jedari, Behrouz;Piran, Md. Jalil

作者机构：

关键词： Autonomous aerial vehicles; Security; Task analysis; Routing; Network topology; Electronic mail; Reinforcement learning; Message authentication; position confusion; reinforcement learning; unmanned aerial vehicles (UAVs)

期刊名称：IEEE INTERNET OF THINGS JOURNAL （ 影响因子：8.2； 五年影响因子：9.0 ）

ISSN： 2327-4662

年卷期： 2024 年 11 卷 12 期

页码：

收录情况： SCI

摘要： Ensuring the stability and security of unmanned aerial vehicle (UAV) communication, especially during long-distance missions, is essential for safeguarding against potential attacks. Large-scale UAV communication faces challenges, including eavesdropping threat, data tampering, replay threat, and man-in-the-middle threat. We propose a security information transmission solution based on reinforcement learning and location confusion algorithm (RLPC-SIT) to achieve a secure data transmission between UAVs. First, we leverage the principles of reinforcement learning to identify the most stable transmission routes. Second, we employ location confusion techniques to blur each location of the transmitting UAV with respect to other UAVs. Furthermore, we utilize the concept of message authentication to encrypt the transmitted data, thus making it inaccessible to malicious nodes and preventing forgery. The results of our theoretical analysis and simulation-based experiments indicate that our approach outperforms other security schemes.

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