Leveraging Large Language Models for Autonomous Threat Detection in IoT Networks

The advent of large language models (LLMs) represents a significant paradigm shift in autonomous threat detection within IoT networks. This paper explores the application of Large Language Models (LLMs) for autonomous threat detection in IoT networks. We present a comprehensive review of existing research in IoT security, identifying key limitations and opportunities for improvement. In support of our investigation, we have prepared a dataset of 65,000 entries, which captures a wide array of threat scenarios and device behaviors in IoT networks. Then, we design a novel threat detection architecture that leverages the capabilities of LLMs to identify and respond to threats autonomously. Our architecture is specifically tailored to meet the unique demands of IoT networks, including real-time detection and lightweight processing. Through a series of experiments, we compare our LLM-based architecture against existing threat detection frameworks, demonstrating its superior performance in terms of accuracy, speed, and adaptability to evolving threats. The findings of this research highlight the potential of LLMs to significantly enhance the security of IoT networks, paving the way for more robust and responsive threat detection systems.