CLAF-IoT: Context-Aware LLMs-Enhanced Authentication Framework for Internet of Things

Abstract

The significant increase in the number of Internet of Things (IoT) devices in various domains requires robust and adaptive authentication mechanisms. Existing methods often fail to address the dynamic and heterogeneous nature of the IoT ecosystem, resulting in significant security vulnerabilities. This article presents a context-aware LLM-enhanced authentication framework (CLAF-IoT) that dynamically adjusts authentication protocols based on real-time environmental and user-specific contexts. Using the advanced contextual understanding and generation capabilities of large language models (LLMs), the proposed framework enhances both security and usability in highly dynamic IoT environments. Key components include environmental context sensing, user behavior analysis, adaptive authentication protocols, real-time threat detection, and federated learning integration for continuous improvement and privacy preservation. Experimental evaluations demonstrate that CLAF-IoT achieves higher authentication accuracy in different scenarios, 11.11% false acceptance rate and 9.09% false rejection rate.