融合深度学习与联邦学习，实现物联网5G边缘网络的高效隐私保护入侵检测。
Hybrid Deep Learning-Federated Learning Powered Intrusion Detection System for IoT/5G Advanced Edge Computing Network
- 用CNN-BiLSTM捕捉局部特征交互，自编码器增强异常敏感性。
- 在UNSW-NB15数据集上达到99.59% AUC和97.36% F1值。
- 推理延迟仅0.0476毫秒，适合超低时延通信场景部署。
物联网与5G-Advanced应用的指数级增长扩大了分布式拒绝服务（DDoS）、恶意软件及零日攻击的威胁面。本文提出一种融合卷积神经网络（CNN）、双向LSTM（BiLSTM）与自编码器（AE）瓶颈的入侵检测系统，基于隐私保护的联邦学习（FL）框架训练。CNN-BiLSTM分支捕获局部与门控跨特征交互，自编码器侧重重建误差驱动的异常敏感性。训练过程在边缘设备间完成，不共享原始数据。在UNSW-NB15（二分类）数据集上，融合模型达到99.59% AUC与97.36% F1值；混淆矩阵分析显示错误率均衡，且精度与召回率均高。测试硬件下平均推理时间约0.0476毫秒/样本，远低于10毫秒的URLLC时延预算，支持边缘部署。同时讨论了可解释性、概念漂移容忍度及联邦学习相关考量，以保障5G-Advanced物联网安全的合规性与可扩展性。
原文摘要 · Abstract (English)
The exponential expansion of IoT and 5G-Advanced applications has enlarged the attack surface for DDoS, malware, and zero-day intrusions. We propose an intrusion detection system that fuses a convolutional neural network (CNN), a bidirectional LSTM (BiLSTM), and an autoencoder (AE) bottleneck within a privacy-preserving federated learning (FL) framework. The CNN-BiLSTM branch captures local and gated cross-feature interactions, while the AE emphasizes reconstruction-based anomaly sensitivity. Training occurs across edge devices without sharing raw data. On UNSW-NB15 (binary), the fused model attains AUC 99.59 percent and F1 97.36 percent; confusion-matrix analysis shows balanced error rates with high precision and recall. Average inference time is approximately 0.0476 ms per sample on our test hardware, which is well within the less than 10 ms URLLC budget, supporting edge deployment. We also discuss explainability, drift tolerance, and FL considerations for compliant, scalable 5G-Advanced IoT security.
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