联邦学习驱动的网络入侵检测研究综述

doi:10.3778/j.issn.1002-8331.2506-0010

摘要/Abstract

摘要： 随着网络流量的快速增长和攻击手段的不断演化，传统入侵检测方法在数据孤岛、隐私保护和异构环境适配方面暴露出明显不足。联邦学习因具备“数据不出本地”的特性，为跨域协作建模和隐私保护提供了新思路，但针对其在入侵检测领域的研究尚缺乏系统化梳理与方法演进分析。围绕联邦学习驱动的网络入侵检测研究，从聚合策略、隐私保护机制和检测技术三大核心维度出发，系统总结近年来的研究进展，分析各类方法的优劣与适用场景，并归纳当前面临的挑战与发展趋势。为该领域相关研究人员提供研究思路，也为实际系统的设计与部署提供理论支撑。

关键词: 联邦学习（FL）, 入侵检测, 聚合策略, 隐私保护, 检测技术

Abstract: With the rapid growth of network traffic and the continuous evolution of attack methods, traditional intrusion detection methods have exposed obvious deficiencies in data island, privacy protection and heterogeneous environment adaptation. Federated learning provides a new idea for cross-domain collaborative modeling and privacy protection because of its characteristic of “data not being local”. However, there is still a lack of systematic combing and method evolution analysis for its research in the field of intrusion detection. This paper focuses on the research of network intrusion detection driven by federated learning. Starting from the three core dimensions of aggregation strategy, privacy protection mechanism and detection technology, the paper systematically summarizes the research progress in recent years, analyzes the advantages and disadvantages of various methods and applicable scenarios, and summarizes the current challenges and development trends. This paper provides research ideas for relevant researchers in this field, and also provides theoretical support for the design and deployment of actual systems.

Key words: federal learning (FL), intrusion detection, aggregation strategy, privacy protection, detection technology

张舒琦, 王海凤, 王再平, 赵鹏, 刘英华, 池志宏, 赵昕晟. 联邦学习驱动的网络入侵检测研究综述[J]. 计算机工程与应用, 2026, 62(8): 48-63.

ZHANG Shuqi, WANG Haifeng, WANG Zaiping, ZHAO Peng, LIU Yinghua, CHI Zhihong, ZHAO Xinsheng. Review of Network Intrusion Detection Driven by Federated Learning[J]. Computer Engineering and Applications, 2026, 62(8): 48-63.

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