# Integrating Large Language Models with Internet of Things Applications

arXiv: https://arxiv.org/abs/2410.19223 · HTML: https://mlsanity.com/paper/2410.19223/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 45 references that are in the map.
- First posted on arXiv 25 Oct 2024.

## Builds on (most cited first, 13 of 13)

- [Lost in the Middle: How Language Models Use Long Contexts](https://mlsanity.com/paper/2307.03172/) (2023)
- [Voyager: An Open-Ended Embodied Agent with Large Language Models](https://mlsanity.com/paper/2305.16291/) (2023)
- [When LLMs Meet Cybersecurity: A Systematic Literature Review](https://mlsanity.com/paper/2405.03644/) (2024)
- [Revolutionizing Cyber Threat Detection with Large Language Models: A privacy-preserving BERT-based Lightweight Model for IoT/IIoT Devices](https://mlsanity.com/paper/2306.14263/) (2023)
- [Automatic Program Repair with OpenAI's Codex: Evaluating QuixBugs](https://mlsanity.com/paper/2111.03922/) (2021)
- [IoT Security Techniques Based on Machine Learning](https://mlsanity.com/paper/1801.06275/) (2018)
- [LOCALINTEL: Generating Organizational Threat Intelligence from Global and Local Cyber Knowledge](https://mlsanity.com/paper/2401.10036/) (2024)
- [IoT Data Analytics in Dynamic Environments: From An Automated Machine Learning Perspective](https://mlsanity.com/paper/2209.08018/) (2022)
- [AGIR: Automating Cyber Threat Intelligence Reporting with Natural Language Generation](https://mlsanity.com/paper/2310.02655/) (2023)
- [An Empirical Study on Using Large Language Models to Analyze Software Supply Chain Security Failures](https://mlsanity.com/paper/2308.04898/) (2023)
- [CUPID: Leveraging ChatGPT for More Accurate Duplicate Bug Report Detection](https://mlsanity.com/paper/2308.10022/) (2023)
- [Macroprogramming: Concepts, State of the Art, and Opportunities of Macroscopic Behaviour Modelling](https://mlsanity.com/paper/2201.03473/) (2022)
- [Advancing TTP Analysis: Harnessing the Power of Large Language Models with Retrieval Augmented Generation](https://mlsanity.com/paper/2401.00280/) (2023)

## Built on by (most cited first, 7 of 7)

- [LLMs meet Federated Learning for Scalable and Secure IoT Management](https://mlsanity.com/paper/2504.16032/) (2025)
- [A Survey of Foundation Models for IoT: Taxonomy and Criteria-Based Analysis](https://mlsanity.com/paper/2506.12263/) (2025)
- [RRTL: Red Teaming Reasoning Large Language Models in Tool Learning](https://mlsanity.com/paper/2505.17106/) (2025)
- [Intelligence of Things: A Spatial Context-Aware Control System for Smart Devices](https://mlsanity.com/paper/2504.13942/) (2025)
- [Method Decoration (DeMe): A Framework for LLM-Driven Adaptive Method Generation in Dynamic IoT Environments](https://mlsanity.com/paper/2512.21817/) (2025)
- [Going Beyond the Edge: Distributed Inference of Transformer Models on Ultra-Low-Power Wireless Devices](https://mlsanity.com/paper/2605.15694/) (2026)
- [Symbolic Separation: Grounding Deep Agents in Knowledge Graphs for Trustworthy Operational Data Analytics](https://mlsanity.com/paper/2609.17107/) (2026)

## Abstract (arXiv)

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.