Deep-Reinforcement-Learning-Based Intrusion Detection in Aerial Computing Networks
Deep-Reinforcement-Learning-Based Intrusion Detection in Aerial Computing Networks
复制标题
航空计算网络中基于深度强化学习的入侵检测
DOI：
10.1109/mnet.011.2100068
复制
发表时间：
2021
期刊：
影响因子：
9.3
通讯作者：
and Ruidong Li
中科院分区：
文献类型：
--
作者：
Jing Tao;Ting Han;and Ruidong Li
无人驾驶飞行器（UAV）的激增导致在不同领域中的各种应用。由于无人机易于部署和动态可重新配置，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。无人机网络的开放性和多连通性使得其更容易受到恶意攻击。为了保护无人机网络的安全，本文提出了一种深度强化学习方法来检测无人机空中计算网络中的恶意攻击。我们首先提供了无人机空中计算网络的框架和潜在的应用。然后讨论了无人机空中计算网络中的入侵威胁。接下来，我们将介绍一个深度强化学习EM入侵检测的案例研究，以保护安全服务。最后，给出了结论和几个有前景的研究方向.
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.