LLM-Based Threat Detection and Prevention Framework for IoT Ecosystems
Yazan Otoum, Arghavan Asad, Amiya Nayak·May 01, 2025
Summary
An LLM-based framework for IoT security integrates lightweight, IoT-specific fine-tuned LLMs for real-time anomaly detection and context-aware mitigation. Docker-based modular deployment supports scalable, reproducible evaluation. This system significantly improves detection accuracy, response latency, and resource efficiency over traditional methods, highlighting the potential of LLM-driven autonomous security solutions for future IoT ecosystems.
Introduction
Background
Overview of IoT security challenges
Importance of real-time anomaly detection
Role of context-aware mitigation in IoT security
Objective
To present an LLM-based framework for enhancing IoT security
To demonstrate improvements in detection accuracy, response latency, and resource efficiency
Method
Data Collection
Types of data relevant for IoT security
Methods for collecting data in real-time
Data Preprocessing
Techniques for preparing data for LLM models
Importance of data quality in model performance
Model Training
Selection and fine-tuning of LLMs for IoT security
Training methodologies for context-aware anomaly detection
Deployment
Docker-based modular deployment for scalability
Reproducibility in evaluation and testing
Results
Performance Evaluation
Metrics for assessing detection accuracy
Analysis of response latency improvements
Resource efficiency comparison with traditional methods
Case Studies
Real-world applications of the LLM-based framework
Success stories and case studies demonstrating effectiveness
Discussion
Challenges and Limitations
Technical challenges in LLM integration for IoT
Scalability issues with large IoT ecosystems
Future Directions
Potential advancements in LLM technology for IoT security
Integration with emerging IoT technologies
Conclusion
Summary of Contributions
Key findings and improvements over traditional methods
Implications for Future IoT Security
The role of LLM-driven autonomous security solutions
Potential for enhancing future IoT ecosystems
Basic info
papers
cryptography and security
emerging technologies
machine learning
artificial intelligence
Advanced features
Insights
What innovative aspects of the LLM-driven autonomous security solutions are highlighted for future IoT ecosystems?
What are the key implementation strategies used to fine-tune LLMs for real-time anomaly detection in IoT environments?
In what ways does the LLM-based framework improve resource efficiency compared to traditional IoT security methods?
How does the Docker-based modular deployment enhance the scalability and reproducibility of the IoT security framework?