mlsanityData to 14 Oct 2026

LLM-Based Threat Detection and Prevention Framework for IoT Ecosystems

arXiv 2505.00240 ↗First posted 1 May 2025cs.CR cs.AI cs.ETcited by 6

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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.

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

8 of 27 references.

References · 8

  1. 2019TinyBERT: Distilling BERT for Natural Language Understandingcited by 1,167
  2. 2021Learning Graph Structures with Transformer for Multivariate Time Series Anomaly Detection in IoTcited by 104
  3. 2024LLMs for Cyber Security: New Opportunitiescited by 11
  4. 2024Efficient Federated Intrusion Detection in 5G ecosystem using optimized BERT-based modelcited by 8
  5. 2024Distributed Threat Intelligence at the Edge Devices: A Large Language Model-Driven Approachcited by 6
  6. 2025LLMs meet Federated Learning for Scalable and Secure IoT Managementcited by 6
  7. 2024A Unified Framework for Context-Aware IoT Management and State-of-the-Art IoT Traffic Anomaly Detectioncited by 2
  8. 2025Application of Deep Reinforcement Learning for Intrusion Detection in Internet of Things: A Systematic Reviewcited by 2

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Cited by year

Citing papers per year: 2025: 6.

6 papers.

  1. 2025From Large AI Models to Agentic AI: A Tutorial on Future Intelligent Communicationscited by 2
  2. 2025Hybrid LLM-Enhanced Intrusion Detection for Zero-Day Threats in IoT Networkscited by 2
  3. 2025Advancing LLM-Based Security Automation with Customized Group Relative Policy Optimization for Zero-Touch Networkscited by 2
  4. 2025RRTL: Red Teaming Reasoning Large Language Models in Tool Learningcited by 1
  5. 2025Phishing Detection in the Gen-AI Era: Quantized LLMs vs Classical Modelscited by 1
  6. 2025Autonomous AI-based Cybersecurity Framework for Critical Infrastructure: Real-Time Threat Mitigationcited by 0