在6G网络中使用同态加密和图形神经网络的IIoT的隐私保护入侵检测框架
1School of Future Information Technology, Shijiazhuang University, Shijiazhuang, 050035, Hebei, China. hbj.king.1974@163.com.
Scientific reports
|December 10, 2025
概括
6G网络中的工业物联网 (IIoT) 的新入侵检测系统 (IDS) 使用图形神经网络 (GNN) 和同型加密 (HE) 来增强网络安全并保护数据隐私.
科学领域:
- 网络安全 网络安全
- 网络工程 网络工程
- 数据 隐私 数据 隐私 数据
背景情况:
- 工业物联网 (IIoT) 与6G网络的整合提供了先进的连接,但增加了网络安全风险.
- 在6G-IIoT环境中,资源有限的设备和动态的网络结构加剧了漏洞.
- 现有的解决方案往往在隐私保护和现代网络威胁的复杂性方面扎.
研究的目的:
- 为6G-IIoT环境量身定制的新型,保护隐私的入侵检测系统 (IDS) 开发.
- 利用图形神经网络 (GNN) 和同态加密 (HE) 来实现强大的威胁识别和数据安全.
- 为了应对检测APT,DDoS和尸网络等复杂攻击的挑战,同时保持严格的隐私标准.
主要方法:
- 利用图形神经网络 (GNN) 来建模复杂的设备间通信和流量模式.
- 实现同型加密 (HE) 进行安全,分布式的训练和对加密数据的推断,确保隐私.
- 设计了系统,以便在资源有限的IIoT设备上有效运行,而不会集中敏感信息.
主要成果:
- 对各种网络威胁实现了超过98%的检测准确度,包括APT,DDoS和Mirai尸网络攻击.
- 证明了低虚假阳性率和最小的计算开销,适用于IIoT的边缘计算.
- 在准确性,可扩展性和隐私保护方面,GNN+HE模型超过了最先进的方法.
结论:
- 拟议的GNN+HE系统为6G-IIoT网络安全提供了一个安全,可扩展和符合隐私的解决方案.
- 它有效地减轻了与数据聚合相关的隐私风险,并增强了威胁检测能力.
- 未来的工作重点是对抗性强度,实时部署和联合学习集成,以提高弹性.
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