基于联邦学习的网络协同入侵检测方法研究综述OA

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.