用优化的BERT模型在5G物联网中实现高效联邦入侵检测
Efficient Federated Intrusion Detection in 5G ecosystem using optimized BERT-based model
- 基于BERT的联邦学习模型，适配边缘设备资源限制
- 中心化测试准确率达97.79%，联邦学习下仅降0.02%
- 支持隐私保护部署，适合5G物联网安全场景
第五代（5G）网络提供智能交通、互联医疗和智慧城市等先进服务，但随之带来日益复杂的网络安全挑战。本文提出一种基于联邦学习与大语言模型（LLMs）的鲁棒入侵检测系统（IDS），核心为改进的BERT模型，用于识别恶意网络流量。该模型经优化，可在计算与存储受限的边缘设备上运行。实验在集中式与联邦学习环境下进行：集中式设置下模型推理准确率达97.79%；联邦学习中，在独立同分布（IID）与非独立同分布（non-IID）数据场景下跨多设备训练，保障数据隐私并符合监管要求。同时采用线性量化压缩模型，使模型尺寸减少28.74%，准确率仅下降0.02%。结果表明，大语言模型可有效部署于资源受限的物联网生态中。
原文摘要 · Abstract (English)
The fifth-generation (5G) offers advanced services, supporting applications such as intelligent transportation, connected healthcare, and smart cities within the Internet of Things (IoT). However, these advancements introduce significant security challenges, with increasingly sophisticated cyber-attacks. This paper proposes a robust intrusion detection system (IDS) using federated learning and large language models (LLMs). The core of our IDS is based on BERT, a transformer model adapted to identify malicious network flows. We modified this transformer to optimize performance on edge devices with limited resources. Experiments were conducted in both centralized and federated learning contexts. In the centralized setup, the model achieved an inference accuracy of 97.79%. In a federated learning context, the model was trained across multiple devices using both IID (Independent and Identically Distributed) and non-IID data, based on various scenarios, ensuring data privacy and compliance with regulations. We also leveraged linear quantization to compress the model for deployment on edge devices. This reduction resulted in a slight decrease of 0.02% in accuracy for a model size reduction of 28.74%. The results underscore the viability of LLMs for deployment in IoT ecosystems, highlighting their ability to operate on devices with constrained computational and storage resources.
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