The rapid expansion of low-altitude economy Internet of Things (LAE-IoT) networks has created unprecedented security challenges due to dynamic three-dimensional mobility patterns, distributed autonomous operations, and severe resource constraints. Traditional intrusion detection systems designed for static ground-based networks prove inadequate for tackling the unique characteristics of aerial IoT environments, including frequent topology changes, real-time detection requirements, and energy limitations. In this article, we analyze the intrusion detection requirements for LAE-IoT networks, complemented by a comprehensive review of evaluation metrics that cover detection effectiveness, response time, and resource consumption. Then, we investigate transformative potential of agentic artificial intelligence (AI) paradigms and introduce a large language model (LLM)-enabled agentic AI framework for enhancing intrusion detection in LAE-IoT networks. This leads to our proposal of a novel multi-agent collaborative intrusion detection framework that leverages specialized LLM-enhanced agents for intelligent data processing and adaptive classification. Through experimental validation, our framework demonstrates superior performance of over 90\% classification accuracy across multiple benchmark datasets. These results highlight the transformative potential of combining agentic AI principles with LLMs for next-generation LAE-IoT security systems.

翻译：低空经济物联网网络的快速扩张，因其动态的三维移动模式、分布式自主操作以及严重的资源限制，带来了前所未有的安全挑战。为静态地面网络设计的传统入侵检测系统，在应对空中物联网环境的独特特性（包括频繁的拓扑变化、实时检测需求和能源限制）方面显得力不从心。本文分析了低空经济物联网网络的入侵检测需求，并辅以对涵盖检测效能、响应时间和资源消耗的评估指标的全面综述。随后，我们探讨了智能体人工智能范式的变革潜力，并引入了一个基于大语言模型的智能体AI框架，以增强低空经济物联网网络中的入侵检测能力。在此基础上，我们提出了一种新颖的多智能体协同入侵检测框架，该框架利用专门的LLM增强智能体进行智能数据处理和自适应分类。通过实验验证，我们的框架在多个基准数据集上表现出超过90%的分类准确率，展现了卓越的性能。这些结果凸显了将智能体AI原理与大语言模型相结合，对于构建下一代低空经济物联网安全系统所具有的变革性潜力。