The proliferation of UAVs has enabled a wide range of mission-critical applications and is becoming a cornerstone of low-altitude networks, supporting smart cities, emergency response, and more. However, the open wireless environment, dynamic topology, and resource constraints of UAVs expose low-altitude networks to severe DoS threats. Traditional defense approaches, which rely on fixed configurations or centralized decision-making, cannot effectively respond to the rapidly changing conditions in UAV swarm environments. To address these challenges, we propose a novel federated multi-agent deep reinforcement learning (FMADRL)-driven moving target defense (MTD) framework for proactive DoS mitigation in low-altitude networks. Specifically, we design lightweight and coordinated MTD mechanisms, including leader switching, route mutation, and frequency hopping, to disrupt attacker efforts and enhance network resilience. The defense problem is formulated as a multi-agent partially observable Markov decision process, capturing the uncertain nature of UAV swarms under attack. Each UAV is equipped with a policy agent that autonomously selects MTD actions based on partial observations and local experiences. By employing a policy gradient-based algorithm, UAVs collaboratively optimize their policies via reward-weighted aggregation. Extensive simulations demonstrate that our approach significantly outperforms state-of-the-art baselines, achieving up to a 34.6% improvement in attack mitigation rate, a reduction in average recovery time of up to 94.6%, and decreases in energy consumption and defense cost by as much as 29.3% and 98.3%, respectively, under various DoS attack strategies. These results highlight the potential of intelligent, distributed defense mechanisms to protect low-altitude networks, paving the way for reliable and scalable low-altitude economy.

翻译：无人机的广泛应用已支撑起一系列关键任务应用，并正成为低空网络的基石，服务于智慧城市、应急响应等领域。然而，开放的无线环境、动态拓扑结构以及无人机自身的资源限制，使得低空网络面临严重的DoS威胁。传统的防御方法依赖固定配置或集中式决策，难以有效应对无人机集群环境中快速变化的条件。为应对这些挑战，本文提出一种新颖的基于联邦多智能体深度强化学习（FMADRL）的移动目标防御（MTD）框架，用于低空网络中的主动DoS缓解。具体而言，我们设计了轻量级且协调的MTD机制，包括领导者切换、路由变异和跳频，以干扰攻击者并增强网络韧性。该防御问题被建模为一个多智能体部分可观测马尔可夫决策过程，以捕捉受攻击下无人机集群的不确定性。每架无人机配备一个策略智能体，基于部分观测和局部经验自主选择MTD动作。通过采用基于策略梯度的算法，无人机通过奖励加权聚合协同优化其策略。大量仿真实验表明，我们的方法显著优于现有先进基线，在各种DoS攻击策略下，攻击缓解率提升最高达34.6%，平均恢复时间减少最高达94.6%，能耗和防御成本分别降低最高达29.3%和98.3%。这些结果凸显了智能分布式防御机制在保护低空网络方面的潜力，为可靠且可扩展的低空经济铺平了道路。