Cross-domain federated intrusion detection model under heterogeneous data distribution

Zhang Shangzhe

doi:10.11959/j.issn.1000-0801.2026043

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Cross-domain federated intrusion detection model under heterogeneous data distribution

Engineering and Application | 更新时间：2026-03-05

- Cross-domain federated intrusion detection model under heterogeneous data distribution
- Telecommunications Science Vol. 42, Issue 2, Pages: 195-203(2026)
- 作者机构：
  
  中国联合网络通信有限公司哈尔滨市分公司，黑龙江哈尔滨 150001
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.2026043
  CLC： TN915.08;TP393
- Received：01 September 2025，
  
  Revised：2025-10-21，
  
  Accepted：11 November 2025，
  
  Published：20 February 2026
- 稿件说明：
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张尚哲.异构数据分布下的跨域联邦入侵检测模型[J].电信科学,2026,42(02):195-203.

Zhang Shangzhe.Cross-domain federated intrusion detection model under heterogeneous data distribution[J].Telecommunications Science,2026,42(02):195-203.
张尚哲.异构数据分布下的跨域联邦入侵检测模型[J].电信科学,2026,42(02):195-203. DOI： 10.11959/j.issn.1000-0801.2026043.

Zhang Shangzhe.Cross-domain federated intrusion detection model under heterogeneous data distribution[J].Telecommunications Science,2026,42(02):195-203. DOI： 10.11959/j.issn.1000-0801.2026043.

摘要

5G/6G时代的高速移动通信网络和物联网广泛互联，使网络环境更加开放复杂，各类网络入侵威胁也日益严峻。传统入侵检测系统大多采用集中式模型，需要将各域数据汇集到中心进行分析，存在数据孤岛和隐私泄露隐患，且难以适应跨运营域、跨网络场景的多样化威胁。为此，提出了一种基于联邦学习（federated learning，FL）的跨域入侵检测系统（intrusion detection system，IDS）模型框架Cross-FL-IDS，通过在各网络域本地训练入侵检测模型、全球聚合更新参数，实现不同域协同检测新兴威胁。在保护各域数据隐私的前提下，Cross-FL-IDS引入跨域特征共享与个性化融合机制，提升了模型对异构流量模式的泛化能力。

Abstract

In the 5G/6G era

owing to the widespread interconnection of high‑speed mobile communication networks and the Internet of things

network environments are rendered more open and complex

and the severity of diverse network intrusion threats is heightened. Traditional intrusion detection systems are predominantly based on centralized architectures

under which data from each domain are required to be aggregated at a central site for analysis; as a result

risks of data silos and privacy leakage are introduced

and adaptation to diverse threats across operator domains and networks is hindered. To address these issues

a federated learning based cross‑domain intrusion detection system model framework (Cross‑FL‑IDS) was proposed

in which intrusion detection models were trained locally within each network domain and model parameters were globally aggregated and updated

by which collaborative detection of emerging threats across domains was achieved. Under the premise that the privacy of each domain’s data was preserved

cross‑domain feature‑sharing and personalized fusion mechanisms were introduced in Cross‑FL‑IDS

through which the model’s generalization to heterogeneous traffic patterns was improved.

关键词

Keywords

references

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