Drone security is currently a major topic of discussion among researchers and industrialists. Although there are multiple applications of drones, if the security challenges are not anticipated and required architectural changes are not made, the upcoming drone applications will not be able to serve their actual purpose. Therefore, in this paper, we present a detailed review of the security-critical drone applications, and security-related challenges in drone communication such as DoS attacks, Man-in-the-middle attacks, De-Authentication attacks, and so on. Furthermore, as part of solution architectures, the use of Blockchain, Software Defined Networks (SDN), Machine Learning, and Fog/Edge computing are discussed as these are the most emerging technologies. Drones are highly resource-constrained devices and therefore it is not possible to deploy heavy security algorithms on board. Blockchain can be used to cryptographically store all the data that is sent to/from the drones, thereby saving it from tampering and eavesdropping. Various ML algorithms can be used to detect malicious drones in the network and to detect safe routes. Additionally, the SDN technology can be used to make the drone network reliable by allowing the controller to keep a close check on data traffic, and fog computing can be used to keep the computation capabilities closer to the drones without overloading them.

中文翻译：

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快速、可靠和安全的无人机通信：一项综合调查

无人机安全目前是研究人员和工业家讨论的主要话题。虽然无人机有多种应用，但如果没有预见到安全挑战，不进行所需的架构改变，即将到来的无人机应用将无法满足其实际用途。因此，在本文中，我们详细回顾了安全关键的无人机应用程序，以及无人机通信中与安全相关的挑战，例如 DoS 攻击、中间人攻击、解除身份验证攻击等。此外，作为解决方案架构的一部分，讨论了区块链、软件定义网络 (SDN)、机器学习和雾/边缘计算的使用，因为这些是最新兴的技术。无人机是资源高度受限的设备，因此不可能在机上部署重型安全算法。区块链可用于加密存储发送到/从无人机发送的所有数据，从而避免篡改和窃听。各种 ML 算法可用于检测网络中的恶意无人机并检测安全路线。此外，SDN技术可用于通过允许控制器密切检查数据流量来使无人机网络可靠，雾计算可用于使计算能力更接近无人机而不会使其过载。各种 ML 算法可用于检测网络中的恶意无人机并检测安全路线。此外，SDN技术可用于通过允许控制器密切检查数据流量来使无人机网络可靠，雾计算可用于使计算能力更接近无人机而不会使其过载。各种 ML 算法可用于检测网络中的恶意无人机并检测安全路线。此外，SDN技术可用于通过允许控制器密切检查数据流量来使无人机网络可靠，雾计算可用于使计算能力更接近无人机而不会使其过载。