HKD-Net: Hierarchical Knowledge Distillation Based on Multi-Domain Feature Fusion for Efficient Network Intrusion Detection

We propose HKD-Net¹, a hierarchical knowledge distillation network based on multi-domain feature fusion, for efficient network intrusion detection on resource-constrained edge devices. The framework incorporates dedicated feature extraction modules across temporal, frequency, and spatial domains, and introduces a dynamic gating mechanism for adaptive feature fusion, resulting in a more discriminative and comprehensive feature representation. Moreover, a hierarchical distillation mechanism is designed that not only preserves soft labels from the output layer but also aligns intermediate features from spatial, temporal, frequency, and fused domains, enabling efficient knowledge transfer from a large teacher model to a compact student model. Through knowledge distillation, the final lightweight model requires only 278,580 parameters, reducing the number of parameters by approximately 74.68% compared to the teacher, while maintaining high detection accuracy. Extensive experiments on three public datasets (Kitsune, CIRA-CIC-DoHBrw2020, and CICIoT2023) demonstrate that HKD-Net outperforms five state-of-the-art methods, achieving accuracies of 96.72%, 97.19%, and 87.19%, respectively, while reducing parameters by 74.68% and maintaining low computational cost.