Advanced Few-Shot Network Intrusion Detection Method Using Lightweight Transfer Learning
Xixi Zhang, Yu Wang, Guangjie Han, Guan Gui · IEEE Internet of Things Journal · 2025
Network intrusion detection (NID) is a critical area of research in network security. While deep learning based NID methods have recently achieved advanced detection performance, they often struggle with limited labeled traffic and the resource constraints of edge Internet of Things (IoT) devices. To address these challenges, we propose an advanced few-shot NID method using lightweight transfer learning (LTL), termed NID-LTL. Our approach begins by pre-training a detection model on the large-scale auxiliary dataset to learn universal representations of network traffic characteristics. Then, an automatic pruning strategy is crafted to prune the pre-trained model, which uses a kernel based nonlinear traffic feature selection algorithm to filter out the key information most relevant to the original traffic. Finally, the layer-wise knowledge distillation method is combined to transfer the useful knowledge learned by the pre-trained model to a lightweight student model. This method can not only quickly adapt to novel few-shot NID tasks, but also further compress the model size, reduce computational and storage overhead. Experimental results demonstrate that the proposed NID-LTL method has excellent classification performance with small model sizes, low parameter counts, and low floating point operations (FLOPs). Especially, in the 1-shot scenario, the NID-LTL method achieves 89.38% classification accuracy with only 1.41% of the parameters in the original model.