A Unified Framework for Context-Aware IoT Management and State-of-the-Art IoT Traffic Anomaly Detection
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Abstract (arXiv)
The rapid expansion of Internet of Things (IoT) ecosystems has introduced growing complexities in device management and network security. To address these challenges, we present a unified framework that combines context-driven large language models (LLMs) for IoT administrative tasks with a fine-tuned anomaly detection module for network traffic analysis. The framework streamlines administrative processes such as device management, troubleshooting, and security enforcement by harnessing contextual knowledge from IoT manuals and operational data. The anomaly detection model achieves state-of-the-art performance in identifying irregularities and threats within IoT traffic, leveraging fine-tuning to deliver exceptional accuracy. Evaluations demonstrate that incorporating relevant contextual information significantly enhances the precision and reliability of LLM-based responses for diverse IoT administrative tasks. Additionally, resource usage metrics such as execution time, memory consumption, and response efficiency demonstrate the framework's scalability and suitability for real-world IoT deployments.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 17 of its 32 references that are in the map. First posted on arXiv 19 Dec 2024.
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17 of 32 references.
References · 17
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5 more
- 2024Efficient Prompting for LLM-based Generative Internet of Thingscited by 12
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