arXiv:2506.07392v4 Announce Type: replace-cross 
Abstract: 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.

أدى انتشار الطائرات بدون طيار إلى تسهيل مجموعة متنوعة من التطبيقات الحيوية، لكنه يعرض الشبكات ذات الارتفاع المنخفض لتهديدات هجوم الحرمان من الخدمة (DoS) بسبب بيئتها اللاسلكية المفتوحة وتضاريسها الديناميكية. الطرق التقليدية للدفاع غير كافية لمواجهة هذه الظروف المتغيرة بسرعة. تم اقتراح إطار جديد للدفاع المتحرك المدفوع بالتعلم العميق المعزز متعدد الوكلاء الفيدرالي (FMADRL) للتخفيف من هجمات DoS بشكل استباقي، مع دمج آليات مثل تبديل القادة والقفز الترددي لتعزيز مرونة الشبكة.

El auge de los UAV ha facilitado diversas aplicaciones críticas, pero expone a las redes de baja altitud a amenazas significativas de DoS debido a su entorno inalámbrico abierto y su topología dinámica. Los métodos de defensa tradicionales son inadecuados para estas condiciones en rápida evolución. Se ha propuesto un nuevo marco de defensa móvil impulsado por el aprendizaje profundo de refuerzo multiagente federado (FMADRL) para mitigar proactivamente los ataques DoS, incorporando mecanismos como el cambio de líder y el salto de frecuencia para mejorar la resiliencia de la red.

L'essor des UAV a facilité diverses applications critiques, mais expose les réseaux de basse altitude à des menaces DoS importantes en raison de leur environnement sans fil ouvert et de leur topologie dynamique. Les méthodes de défense traditionnelles sont inadéquates face à ces conditions en évolution rapide. Un nouveau cadre de défense mobile basé sur l'apprentissage par renforcement profond multi-agent fédéré (FMADRL) a été proposé pour atténuer proactivement les attaques DoS, intégrant des mécanismes tels que le changement de leader et le saut de fréquence pour renforcer la résilience du r…

The rise of UAVs has facilitated various critical applications but exposes low-altitude networks to significant DoS threats due to their open wireless environment and dynamic topology. Traditional defense methods are inadequate for these rapidly changing conditions. A new federated multi-agent deep reinforcement learning (FMADRL)-driven moving target defense (MTD) framework has been proposed to proactively mitigate DoS attacks, incorporating mechanisms like leader switching and frequency hopping to enhance network resilience.

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

arXiv:2512.08639v1 Announce Type: new 
Abstract: Aerial Vision-and-Language Navigation (VLN) aims to enable unmanned aerial vehicles (UAVs) to interpret natural language instructions and navigate complex urban environments using onboard visual observation. This task holds promise for real-world applications such as low-altitude inspection, search-and-rescue, and autonomous aerial delivery. Existing methods often rely on panoramic images, depth inputs, or odometry to support spatial reasoning and action planning. These requirements increase system cost and integration complexity, thus hindering practical deployment for lightweight UAVs. We present a unified aerial VLN framework that operates solely on egocentric monocular RGB observations and natural language instructions. The model formulates navigation as a next-token prediction problem, jointly optimizing spatial perception, trajectory reasoning, and action prediction through prompt-guided multi-task learning. Moreover, we propose a keyframe selection strategy to reduce visual redundancy by retaining semantically informative frames, along with an action merging and label reweighting mechanism that mitigates long-tailed supervision imbalance and facilitates stable multi-task co-training. Extensive experiments on the Aerial VLN benchmark validate the effectiveness of our method. Under the challenging monocular RGB-only setting, our model achieves strong results across both seen and unseen environments. It significantly outperforms existing RGB-only baselines and narrows the performance gap with state-of-the-art panoramic RGB-D counterparts. Comprehensive ablation studies further demonstrate the contribution of our task design and architectural choices.

تم تقديم إطار عمل جديد للتنقل الجوي باستخدام الرؤية واللغة (VLN) يمكّن الطائرات بدون طيار (UAV) من تفسير التعليمات باللغة الطبيعية والتنقل في البيئات الحضرية باستخدام ملاحظات RGB أحادية العين فقط. تبسط هذه الطريقة عملية التنقل من خلال تحسين الإدراك المكاني، والتفكير في المسار، وتوقع الإجراءات من خلال التعلم متعدد المهام الموجه بالمحفزات.

Se ha introducido un nuevo marco para la navegación aérea mediante visión y lenguaje (VLN), que permite a los vehículos aéreos no tripulados (UAV) interpretar instrucciones en lenguaje natural y navegar por entornos urbanos utilizando únicamente observaciones RGB monoculares egocéntricas. Este enfoque simplifica el proceso de navegación al optimizar la percepción espacial, el razonamiento de trayectorias y la predicción de acciones a través de un aprendizaje multitarea guiado por indicaciones.

Un nouveau cadre pour la navigation aérienne par vision et langage (VLN) a été introduit, permettant aux véhicules aériens sans pilote (UAV) d'interpréter des instructions en langage naturel et de naviguer dans des environnements urbains en utilisant uniquement des observations RGB monoculaires égocentriques. Cette approche simplifie le processus de navigation en optimisant la perception spatiale, le raisonnement de trajectoire et la prédiction d'action grâce à un apprentissage multitâche guidé par des invites.

A new framework for Aerial Vision-and-Language Navigation (VLN) has been introduced, enabling unmanned aerial vehicles (UAVs) to interpret natural language instructions and navigate urban environments using only egocentric monocular RGB observations. This approach simplifies the navigation process by optimizing spatial perception, trajectory reasoning, and action prediction through prompt-guided multi-task learning.

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

Was this article worth reading? Share it

LucidQuery AI

Cato Networks

Dyad