The unmanned aerial vehicle (UAV) network is popular these years due to its various applications. In the UAV network, routing is significantly affected by the distributed network topology, leading to the issue that UAVs are vulnerable to deliberate damage. Hence, this paper focuses on the routing plan and recovery for UAV networks with attacks. In detail, a deliberate attack model based on the importance of nodes is designed to represent enemy attacks. Then, a node importance ranking mechanism is presented, considering the degree of nodes and link importance. However, it is intractable to handle the routing problem by traditional methods for UAV networks, since link connections change with the UAV availability. Hence, an intelligent algorithm based on reinforcement learning is proposed to recover the routing path when UAVs are attacked. Simulations are conducted and numerical results verify the proposed mechanism performs better than other referred methods.

翻译：近年来，无人机网络因其广泛应用而备受关注。在无人机网络中，路由受到分布式网络拓扑的显著影响，导致无人机易受蓄意破坏的威胁。为此，本文聚焦于受攻击无人机网络的路由规划与恢复问题。具体而言，设计了一种基于节点重要性的蓄意攻击模型来模拟敌方攻击，并提出了同时考虑节点度与链路重要性的节点重要性排序机制。然而，由于链路连接随无人机可用性动态变化，传统方法难以处理无人机网络的路由问题。因此，提出了一种基于强化学习的智能算法，在无人机遭受攻击时恢复路由路径。仿真实验与数值结果表明，所提机制性能优于其他对比方法。