用于零信任物联网架构的联邦学习和大语言模型驱动的威胁情报
Federated Learning and LLM-Driven Threat Intelligence for Zero Trust IoT Architecture
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中文总结 AI 辅助
针对物联网安全隐私挑战，提出联邦学习和大语言模型驱动的零信任物联网架构，集成多种技术，通过特定方式在各通信层强化零信任，实验表明该框架能有效为资源受限物联网设备实现隐私保护异常检测。
中文摘要 AI 辅助
物联网已变得至关重要，但带来了严重的安全和隐私挑战，尤其是在关键任务环境中。传统设备易受病毒、数据泄露和未经授权访问的影响，更新成本过高。本文提出一种用于零信任物联网架构的联邦学习和大语言模型驱动的威胁情报，将用于异常检测的联邦学习与隐私保护分布式学习、持续身份验证和大语言模型驱动的自主威胁响应集成到统一管道中。通过在MQTT上使用相互TLS在每个通信层强制执行零信任。实验在树莓派和各种传感器上实现了F1分数0.9091和ROC-AUC为1.0，凸显了该框架在为资源受限物联网设备实现隐私保护异常检测方面的有效性。
英文摘要
While the Internet of Things (IoT) has become essential, they introduced serious security and privacy challenges, especially for mission-critical environments. Legacy devices are vulnerable to viruses, data breaches, and unauthorized access, and updating these devices would be infeasibly costly. As a solution, this paper presents a Federated Learning and LLM-Driven Threat Intelligence for Zero Trust IoT Architecture, with FL for anomaly detection integrating privacy-preserving distributed learning, continuous identity verification, and LLM-driven autonomous threat response into a unified pipeline. Unlike existing solutions, our framework enforces Zero Trust at every communication layer via mutual TLS (mTLS) over MQTT, ensuring no device or message is implicitly trusted. Our experiments with Raspberry Pis and various sensors achieve an F1 score of 0.9091 and an ROC-AUC of 1.0, highlighting the effectiveness of the proposed framework in enabling privacy-preserving anomaly detection for resource-constrained IoT devices.
