Hybrid Deep Learning-Federated Learning Powered Intrusion Detection System for IoT/5G Advanced Edge Computing Network
This addresses security challenges for IoT and 5G-Advanced applications, offering a privacy-preserving and efficient solution, though it is incremental as it builds on existing deep learning and federated learning techniques.
The paper tackles intrusion detection for IoT/5G-Advanced edge networks by proposing a hybrid deep learning model combined with federated learning, achieving AUC 99.59% and F1 97.36% on the UNSW-NB15 dataset with an inference time of 0.0476 ms per sample.
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.