Low-altitude wireless networks (LAWNs) have the potential to revolutionize communications by supporting a range of applications, including urban parcel delivery, aerial inspections and air taxis. However, compared with traditional wireless networks, LAWNs face unique security challenges due to low-altitude operations, frequent mobility and reliance on unlicensed spectrum, making it more vulnerable to some malicious attacks. In this paper, we investigate some large artificial intelligence model (LAM)-enabled solutions for secure communications in LAWNs. Specifically, we first explore the amplified security risks and important limitations of traditional AI methods in LAWNs. Then, we introduce the basic concepts of LAMs and delve into the role of LAMs in addressing these challenges. To demonstrate the practical benefits of LAMs for secure communications in LAWNs, we propose a novel LAM-based optimization framework that leverages large language models (LLMs) to generate enhanced state features on top of handcrafted representations, and to design intrinsic rewards accordingly, thereby improving reinforcement learning performance for secure communication tasks. Through a typical case study, simulation results validate the effectiveness of the proposed framework. Finally, we outline future directions for integrating LAMs into secure LAWN applications.

翻译：低空无线网络（LAWNs）具有通过支持城市包裹配送、空中巡检和空中出租车等一系列应用来革新通信的潜力。然而，与传统无线网络相比，LAWNs由于低空运行、频繁移动以及对非授权频谱的依赖而面临独特的安全挑战，使其更容易受到某些恶意攻击。本文研究了LAWNs中一些由大型人工智能模型（LAM）赋能的安全通信解决方案。具体而言，我们首先探讨了LAWNs中传统AI方法所放大的安全风险和重要局限性。接着，我们介绍了LAM的基本概念，并深入探讨了LAM在应对这些挑战中的作用。为了展示LAM在LAWNs安全通信中的实际效益，我们提出了一种新颖的基于LAM的优化框架，该框架利用大型语言模型（LLMs）在人工设计的表征之上生成增强的状态特征，并据此设计内在奖励，从而提升安全通信任务中强化学习的性能。通过一个典型案例研究，仿真结果验证了所提框架的有效性。最后，我们展望了将LAM集成到安全LAWN应用中的未来方向。