计算机技术与发展2025，Vol.35Issue(7)：55-62,8.DOI:10.20165/j.cnki.ISSN1673-629X.2025.0087
基于联邦全局知识蒸馏的异常网络入侵检测方法
Anomaly Network Intrusion Detection Method Based on Federated Learning with Global Knowledge Distillation
摘要
Abstract
In the field of network intrusion detection,Federated Learning(FL)has gained significant attention as a distributed processing method that protects data privacy.However,due to data heterogeneity among participating nodes,traditional FL methods often fail to achieve high performance during joint training.To address this issue,we propose an improved federated learning with global knowledge distillation(FLGKD-ANIDS).It sets up a buffer in the central server to cache multiple rounds of model parameters uploaded by clients.Furthermore,these cached parameters are used to generate teacher model parameters containing multi-round global knowledge,which guide the knowledge distillation process in the client side.This mechanism allows clients to train the local data by injecting global knowledge feature.We conducted experiments on two public available datasets,UNSW-NB15 and CIC-IDS2017.The results show that FLGKD-ANIDS significantly improves model performance across various data heterogeneity scenarios compared to existing federated learning methods,and its performance approaches that of centrally trained models.关键词
入侵检测系统/联邦学习/数据隐私/知识蒸馏/数据异构Key words
intrusion detection system/federated learning/data privacy/knowledge distillation/data heterogeneity分类
信息技术与安全科学引用本文复制引用
石迎澳,李润知,姬怡..基于联邦全局知识蒸馏的异常网络入侵检测方法[J].计算机技术与发展,2025,35(7):55-62,8.基金项目
河南省高等学校重点科研项目(25B520009) （25B520009）
