Integrating Large Language Models with Internet of Things Applications

TL;DR

This paper explores how Large Language Models can enhance IoT applications by improving security, automation, and data processing, demonstrating significant accuracy and efficiency gains through three practical case studies.

Contribution

It introduces novel integrations of GPT models with IoT systems, showcasing their ability to detect attacks, generate scripts, and analyze sensor data effectively.

Findings

GPT achieves 87.6% detection accuracy in DDoS detection

Fine-tuned GPT reaches 94.9% accuracy

GPT effectively processes large sensor datasets

Abstract

This paper identifies and analyzes applications in which Large Language Models (LLMs) can make Internet of Things (IoT) networks more intelligent and responsive through three case studies from critical topics: DDoS attack detection, macroprogramming over IoT systems, and sensor data processing. Our results reveal that the GPT model under few-shot learning achieves 87.6% detection accuracy, whereas the fine-tuned GPT increases the value to 94.9%. Given a macroprogramming framework, the GPT model is capable of writing scripts using high-level functions from the framework to handle possible incidents. Moreover, the GPT model shows efficacy in processing a vast amount of sensor data by offering fast and high-quality responses, which comprise expected results and summarized insights. Overall, the model demonstrates its potential to power a natural language interface. We hope that researchers will find these case studies inspiring to develop further.