# From Static to Adaptive Defense: Federated Multi-Agent Deep Reinforcement Learning-Driven Moving Target Defense Against DoS Attacks in UAV Swarm Networks

arXiv: https://arxiv.org/abs/2506.07392 · HTML: https://mlsanity.com/paper/2506.07392/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 54 references that are in the map.
- First posted on arXiv 9 Jun 2025.

## Builds on (most cited first, 6 of 6)

- [Generative AI based Secure Wireless Sensing for ISAC Networks](https://mlsanity.com/paper/2408.11398/) (2024)
- [Generative AI for Integrated Sensing and Communication: Insights from the Physical Layer Perspective](https://mlsanity.com/paper/2310.01036/) (2023)
- [Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning](https://mlsanity.com/paper/2502.05824/) (2025)
- [RL and Fingerprinting to Select Moving Target Defense Mechanisms for Zero-day Attacks in IoT](https://mlsanity.com/paper/2212.14647/) (2022)
- [Disrupting Vision-Language Model-Driven Navigation Services via Adversarial Object Fusion](https://mlsanity.com/paper/2505.23266/) (2025)
- [Two-layer consensus based on master-slave consortium chain data sharing for Internet of Vehicles](https://mlsanity.com/paper/2411.10680/) (2024)

## Built on by (most cited first, 1 of 1)

- [Reinforcement Learning for Decision-Level Interception Prioritization in Drone Swarm Defense](https://mlsanity.com/paper/2508.00641/) (2025)

## Abstract (arXiv)

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.