# LLM-Based Threat Detection and Prevention Framework for IoT Ecosystems

arXiv: https://arxiv.org/abs/2505.00240 · HTML: https://mlsanity.com/paper/2505.00240/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 27 references that are in the map.
- First posted on arXiv 1 May 2025.

## Builds on (most cited first, 8 of 8)

- [TinyBERT: Distilling BERT for Natural Language Understanding](https://mlsanity.com/paper/1909.10351/) (2019)
- [Learning Graph Structures with Transformer for Multivariate Time Series Anomaly Detection in IoT](https://mlsanity.com/paper/2104.03466/) (2021)
- [LLMs for Cyber Security: New Opportunities](https://mlsanity.com/paper/2404.11338/) (2024)
- [Efficient Federated Intrusion Detection in 5G ecosystem using optimized BERT-based model](https://mlsanity.com/paper/2409.19390/) (2024)
- [Distributed Threat Intelligence at the Edge Devices: A Large Language Model-Driven Approach](https://mlsanity.com/paper/2405.08755/) (2024)
- [LLMs meet Federated Learning for Scalable and Secure IoT Management](https://mlsanity.com/paper/2504.16032/) (2025)
- [A Unified Framework for Context-Aware IoT Management and State-of-the-Art IoT Traffic Anomaly Detection](https://mlsanity.com/paper/2412.19830/) (2024)
- [Application of Deep Reinforcement Learning for Intrusion Detection in Internet of Things: A Systematic Review](https://mlsanity.com/paper/2504.14436/) (2025)

## Built on by (most cited first, 6 of 6)

- [From Large AI Models to Agentic AI: A Tutorial on Future Intelligent Communications](https://mlsanity.com/paper/2505.22311/) (2025)
- [Hybrid LLM-Enhanced Intrusion Detection for Zero-Day Threats in IoT Networks](https://mlsanity.com/paper/2507.07413/) (2025)
- [Advancing LLM-Based Security Automation with Customized Group Relative Policy Optimization for Zero-Touch Networks](https://mlsanity.com/paper/2512.09485/) (2025)
- [RRTL: Red Teaming Reasoning Large Language Models in Tool Learning](https://mlsanity.com/paper/2505.17106/) (2025)
- [Phishing Detection in the Gen-AI Era: Quantized LLMs vs Classical Models](https://mlsanity.com/paper/2507.07406/) (2025)
- [Autonomous AI-based Cybersecurity Framework for Critical Infrastructure: Real-Time Threat Mitigation](https://mlsanity.com/paper/2507.07416/) (2025)

## Abstract (arXiv)

The increasing complexity and scale of the Internet of Things (IoT) have made security a critical concern. This paper presents a novel Large Language Model (LLM)-based framework for comprehensive threat detection and prevention in IoT environments. The system integrates lightweight LLMs fine-tuned on IoT-specific datasets (IoT-23, TON_IoT) for real-time anomaly detection and automated, context-aware mitigation strategies optimized for resource-constrained devices. A modular Docker-based deployment enables scalable and reproducible evaluation across diverse network conditions. Experimental results in simulated IoT environments demonstrate significant improvements in detection accuracy, response latency, and resource efficiency over traditional security methods. The proposed framework highlights the potential of LLM-driven, autonomous security solutions for future IoT ecosystems.