用轻量大模型实现物联网实时安全防护
LLM-Based Threat Detection and Prevention Framework for IoT Ecosystems
- 基于物联网数据微调轻量大模型，实现异常检测
- 检测准确率与响应延迟优于传统方法，资源消耗更低
- 适合边缘设备部署，适合智能安防与工业物联网场景
物联网（IoT）规模与复杂性的增加使其安全问题日益突出。本文提出一种基于大型语言模型（LLM）的新型综合威胁检测与防护框架，通过在IoT-23和TON_IoT等物联网专用数据集上微调轻量级LLM，实现实时异常检测与上下文感知的自动缓解策略，适用于资源受限设备。采用模块化Docker部署方式，支持在多种网络条件下可复现的规模化评估。在模拟物联网环境中，实验结果表明该框架在检测准确率、响应延迟和资源效率方面显著优于传统安全方法。研究凸显了大模型驱动的自主安全方案在未来物联网生态系统中的潜力。
原文摘要 · Abstract (English)
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.
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