The proliferation of unmanned aerial vehicles (UAVs) leads to various applications in different fields. Due to the easy deployment and dynamic reconfigurability of UAVs, they can provide and support multiple services for users, such as surveillance, sensing, and logistics. However, the increasing attention to UAV applications exposes it to security threats. The openness and multi-connectivity characteristics make UAV networks more vulnerable to malicious attacks. In this article, to protect the security of UAV networks, we present a deep reinforcement learning approach to detect malicious attacks in UAV aerial computing networks. We first provide the framework of UAV aerial computing networks and potential applications. Intrusion threats in UAV aerial computing networks are then discussed. Next, we present a case study of deep-reinforcement-learning-em-powered intrusion detection to protect the security services. Finally, we present the conclusion and several promising research directions.

中文翻译：

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航空计算网络中基于深度强化学习的入侵检测


无人机（UAV）的激增导致了不同领域的各种应用。由于无人机易于部署和动态可重构性，它们可以为用户提供和支持多种服务，例如监视、传感和物流。然而，对无人机应用的日益关注使其面临安全威胁。开放性和多连接性的特点使得无人机网络更容易受到恶意攻击。在本文中，为了保护无人机网络的安全，我们提出了一种深度强化学习方法来检测无人机航空计算网络中的恶意攻击。我们首先提供无人机航空计算网络的框架和潜在应用。然后讨论了无人机空中计算网络中的入侵威胁。接下来，我们介绍一个基于深度强化学习的入侵检测保护安全服务的案例研究。最后，我们提出了结论和几个有前景的研究方向。