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This paper addresses the security challenges in Low-Altitude Economy IoT (LAE-IoT) networks by proposing a multi-agent collaborative intrusion detection framework. The framework leverages specialized, LLM-enhanced agents for intelligent data processing and adaptive classification to overcome limitations of traditional intrusion detection systems in dynamic aerial environments. Experimental results demonstrate the framework achieves over 90% classification accuracy across multiple benchmark datasets, highlighting the potential of LLM-enhanced agentic AI for LAE-IoT security.
LLM-enhanced multi-agent systems can achieve over 90% accuracy in detecting intrusions within dynamic low-altitude IoT networks, offering a significant leap over traditional methods.
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.
