信息安全研究2026，Vol.12Issue(6)：526-532,7.DOI:10.12379/j.issn.2096-1057.2026.06.05
基于联邦学习的网络协同入侵检测方法研究综述
Research Review on Collaborative Intrusion Detection Based on Federated Learning
摘要
Abstract
The increasing complexity of cyber attacks challenges traditional centralized intrusion detection systems.Federated learning-based collaborative intrusion detection enables collaborative modeling and knowledge sharing among multiple nodes without sharing raw data,thereby effectively improving the detection capability for cross-domain and unknown attacks.This paper systematically reviews the research progress of federated learning-based collaborative intrusion detection.Existing methods are classified and analyzed from multiple perspectives,including architecture-aware,model adaptation and evolution-driven,as well as privacy and security enhanced approaches.Commonly used datasets and evaluation metrics are summarized.Finally,the major challenges and future research directions are discussed,providing references for subsequent research in this field.关键词
联邦学习/协同入侵检测/联邦入侵检测/网络安全/隐私增强Key words
federated learning/collaborative intrusion detection/federated intrusion detection/network security/privacy enhancement分类
信息技术与安全科学引用本文复制引用
陈良臣,傅德印,刘宝旭,卢志刚,姜政伟,高曙..基于联邦学习的网络协同入侵检测方法研究综述[J].信息安全研究,2026,12(6):526-532,7.基金项目
中国劳动关系学院教改项目(JG26041) （JG26041）
国家重点研发计划项目(2023YFB2603800) （2023YFB2603800）
国家统计局全国统计科学研究项目(2022LY005) （2022LY005）
中国科学院网络测评技术重点实验室课题(KFKT2022-003) （KFKT2022-003）
中国劳动关系学院科研重点项目(26XYZD002) （26XYZD002）
中国劳动关系学院研究生教改项目(YJG2506) （YJG2506）
中国劳动关系学院教师学术团队项目(24JSTD016) （24JSTD016）
