  Network Intrusion Detection (NID) works as a kernel technology for the
security network environment, obtaining extensive research and application.
Despite enormous efforts by researchers, NID still faces challenges in
deploying on resource-constrained devices. To improve detection accuracy while
reducing computational costs and model storage simultaneously, we propose a
lightweight intrusion detection approach based on self-knowledge distillation,
namely LNet-SKD, which achieves the trade-off between accuracy and efficiency.
Specifically, we carefully design the DeepMax block to extract compact
representation efficiently and construct the LNet by stacking DeepMax blocks.
Furthermore, considering compensating for performance degradation caused by the
lightweight network, we adopt batch-wise self-knowledge distillation to provide
the regularization of training consistency. Experiments on benchmark datasets
demonstrate the effectiveness of our proposed LNet-SKD, which outperforms
existing state-of-the-art techniques with fewer parameters and lower
computation loads.
