SecuFL-IoT:一个适应性保护隐私的联合学习框架,用于智能工业网络中异常检测
1College Computing and Information Technology, University of Bisha, Bisha, Saudi Arabia. aqzaz@ub.edu.sa.
Scientific reports
|January 29, 2026
概括
SecuFL-IoT通过一种新的联合学习框架来增强工业物联网 (IIoT) 网络安全. 它实现了卓越的异常检测和效率,同时确保了IIoT设备的数据隐私.
科学领域:
- 工业物联网 (IIoT) 中的网络安全和隐私
- 联合学习框架的联合学习框架.
- 异常检测系统 异常检测系统
背景情况:
- 工业物联网设备的扩散带来了重大的网络安全和隐私风险,特别是在异常检测和安全数据共享方面.
- 现有的联合学习方法经常在资源有限的IIoT环境中与通信效率和强大的安全性作斗争.
研究的目的:
- 引入SecuFL-IoT,一个安全和有效的通信联合学习框架,专门为IIoT环境量身定制.
- 为了提高异常检测准确性,数据隐私和IIoT系统的运营效率.
主要方法:
- 集成自适应异常检测,基于格子的同型加密和差异隐私.
- 利用强化学习进行自适应值调整以优化安全性和效率.
- 在X-IIoTID数据集上对最先进的联合学习模型 (FedAvg,FedProx,SCAFFOLD) 的评估.
主要成果:
- 在异常检测方面,SecuFL-IoT获得了F1得分88.5%和2.7%的假阳性率,超过了基线模型的表现.
- 通信开支减少了53%,融合速度快了23%,能源消耗降低了35%.
- 提高对抗性强度,降低数据中毒成功率至9%以下,同时确保强有力的隐私保证.
结论:
- SecuFL-IoT为IIoT提供了一个可扩展,保护隐私和符合行业标准的联合学习解决方案.
- 该框架符合ISA/IEC 62,443网络安全标准,适用于智能工厂和电网等关键基础设施.
- 为未来的研究解决了诸如加密延迟和静态网络拓假设等局限性.
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