Multi-Agent Collaborative Intrusion Detection for Low-Altitude Economy IoT: An LLM-Enhanced Agentic AI Framework

2026-01-25

AI Summary

This study addresses the challenges of securing Low-Altitude Economy Internet of Things (LAE-IoT) systems, whose three-dimensional dynamic topology, resource constraints, and stringent real-time requirements render traditional intrusion detection systems ineffective. To overcome these limitations, this work proposes a novel multi-agent collaborative adaptive intrusion detection framework, which for the first time integrates large language model (LLM)-enhanced artificial intelligence into the LAE-IoT domain. In this architecture, lightweight agents cooperatively perform data processing and leverage LLM-driven adaptive classification to detect threats. Evaluated on multiple benchmark datasets, the proposed framework achieves detection accuracy exceeding 90%, significantly outperforming conventional approaches in terms of precision, response latency, and energy efficiency, thereby transcending the constraints of static detection paradigms.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Application Domains: Internet of Things, Sensor Networks & Smart Cities
• Multiagent Systems: Modeling other Agents

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

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.

Problem

• Low-Altitude Economy IoT
• Intrusion Detection
• Multi-Agent Collaboration
• Resource Constraints
• Dynamic Topology

Innovation

• LLM-enhanced Agentic AI
• Multi-Agent Collaborative Intrusion Detection
• Low-Altitude Economy IoT
• Adaptive Classification
• Intelligent Data Processing