The rapid growth of low-altitude economy Internet of Things (LAE-IoT) networks, such as those used in drone-based delivery services and aerial monitoring, has introduced unique security challenges. Traditional intrusion detection systems, designed for static ground-based networks, struggle to adapt to the dynamic and resource-constrained nature of aerial IoT environments. A team of researchers from various institutions, including City University of Hong Kong, Nanyang Technological University, and the University of Technology Sydney, has proposed a novel solution to enhance security in these networks.
The researchers, led by Hongjuan Li and including Hui Kang, Jiahui Li, Geng Sun, Ruichen Zhang, Jiacheng Wang, Dusit Niyato, Wei Ni, and Abbas Jamalipour, have developed a multi-agent collaborative intrusion detection framework. This framework leverages specialized agents enhanced with large language models (LLMs) for intelligent data processing and adaptive classification. The study was published in the IEEE Internet of Things Journal.
The research begins by analyzing the specific intrusion detection requirements for LAE-IoT networks, which include handling frequent topology changes, real-time detection, and energy limitations. The authors also provide a comprehensive review of evaluation metrics that cover detection effectiveness, response time, and resource consumption.
The proposed framework introduces an agentic artificial intelligence (AI) paradigm, where specialized agents work collaboratively to detect and respond to intrusions. These agents are enhanced with LLMs, which enable them to process and classify data more intelligently and adaptively. The framework aims to address the unique challenges of LAE-IoT networks by providing a more dynamic and efficient intrusion detection system.
Experimental validation of the framework demonstrated superior performance, achieving over 90% classification accuracy across multiple benchmark datasets. This indicates that the combination of agentic AI principles with LLMs holds significant promise for enhancing the security of next-generation LAE-IoT networks.
For the energy sector, this research could have practical applications in securing aerial IoT networks used for monitoring and managing energy infrastructure, such as power lines, wind farms, and solar installations. By improving the security of these networks, the energy industry can ensure more reliable and efficient operations, ultimately leading to better energy management and distribution.
This article is based on research available at arXiv.