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面向工业CPS联邦入侵检测的协作保障框架

梁俊威 陈剑勇 林秋镇 杨耿 江凯

深圳大学学报(理工版)2026,Vol.43Issue(2):162-170,9.
深圳大学学报(理工版)2026,Vol.43Issue(2):162-170,9.DOI:10.3724/SP.J.1249.2026.02162

面向工业CPS联邦入侵检测的协作保障框架

A collaborative assurance framework for federated intrusion detection in industrial cyber-physical system

梁俊威 1陈剑勇 2林秋镇 2杨耿 3江凯3

作者信息

  • 1. 深圳信息职业技术大学计算机与软件学院,广东 深圳 518172||深圳大学计算机与软件学院,广东 深圳 518060
  • 2. 深圳大学计算机与软件学院,广东 深圳 518060
  • 3. 深圳信息职业技术大学计算机与软件学院,广东 深圳 518172
  • 折叠

摘要

Abstract

To address the challenges posed by non-independent identically distributed(Non-IID)data and adversarial attacks in federated intrusion detection for industrial cyber-physical systems(CPS),a collaborative assurance framework based on quality of service(QoS)and hyperledger Fabric(HLF)consortium blockchain technology is proposed,termed as QoS-HLF.Specifically,a QoS-awareness/evaluation(QoS-A/E)collaborative model selection mechanism is developed to quantitatively assess the collaborative service quality of intrusion detection models deployed in different regional subnetworks of industrial CPS.By selecting QoS-consistent and functionally homogeneous third party models for federated training,the proposed approach mitigates convergence degradation caused by heterogeneous data distributions.In addition,by integrating the permissioned distributed architecture of HLF with the collaborative modeling protocol,a decentralized cross-domain federated modeling platform is constructed to ensure that federated learning participants can securely initiate and engage in collaborations without relying on a trusted central authority.A QoS-A/E-driven federated collaboration chaincode is further designed to enable automatic and reliable execution of agreed protocols.The mechanism supports timely detection of participants whose model performance suddenly deteriorates,fails,or exhibits adversarial behavior,and dynamically replaces them with qualified alternatives to maintain collaboration stability.Experimental results and theoretical analysis on real industrial CPS datasets demonstrate that the proposed QoS-HLF framework achieves an F1-score of 98.29%and reduces the false positive rate to 1.28%.Meanwhile,blockchain-based evidence storage and smart contract mechanisms significantly enhance the security,trustworthiness,and automation capability of the collaborative process.The proposed framework offers an efficient and reliable solution for secure collaborative detection in industrial CPS.

关键词

信息物理系统/入侵检测系统/服务质量/超级记账本Fabric/联盟链/协作建模协议

Key words

cyber-physical systems/intrusion detection system/quality of service/hyperledger Fabric/consortium blockchain/collaborative modeling protocol

分类

信息技术与安全科学

引用本文复制引用

梁俊威,陈剑勇,林秋镇,杨耿,江凯..面向工业CPS联邦入侵检测的协作保障框架[J].深圳大学学报(理工版),2026,43(2):162-170,9.

基金项目

Basic and Applied Basic Research of Guangdong Province(2023A1515110070,2022A1515110667) 广东省基础与应用基础研究基金资助项目(2023A151511 0070,2022A1515110667) (2023A1515110070,2022A1515110667)

深圳大学学报(理工版)

1000-2618

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