云南民族大学学报（自然科学版）2026，Vol.35Issue(2)：224-233,10.DOI:10.3969/j.issn.1672-8513.2026.02.009
基于卷积循环的知识蒸馏入侵检测方法
Knowledge distillation intrusion detection method based on convolution recurrent network
摘要
Abstract
Aiming at the problems that existing intrusion detection methods based on deep learning have insufficient ability to extract mixed features of traffic data and low efficiency of model deployment,this paper proposed an intrusion detection model combining convolutional cyclic network and knowledge distillation.This model uses the convolution cycle structure to extract the sequential mixture features of traffic data,and proposes a two-stage knowledge distillation method for the convolution cycle structure.The attention mechanism is introduced in the process of feature distilling students network structure to optimize the initial parameters,and then introduce relevance in the distillation loss to transfer the middle layer relationship knowledge of the teacher network and fine-tune the global parameters.The experimental results show that the proposed model has better deployment efficiency than the traditional intrusion model,and the proposed method improves the performance of the original model by 5.49％.关键词
入侵检测/深度学习/卷积循环神经网络/知识蒸馏Key words
intrusion detection/deep learning/CRNN/knowledge distillation分类
信息技术与安全科学引用本文复制引用
董国芳,刘兵,鲁烨堃..基于卷积循环的知识蒸馏入侵检测方法[J].云南民族大学学报（自然科学版）,2026,35(2):224-233,10.基金项目
国家自然科学基金(61662089). （61662089）
