相关实验视频
安全的区块链集成深度学习框架,用于联合的风险适应性和保护隐私的物联网边缘智能集
K Swathi1, Putta Durga2, K Venkata Prasad3
1Department of CSE, Koneru Lakshmaiah Education Foundation, Vaddeswaram, India.
Scientific reports
|November 20, 2025
概括
本研究介绍了一种新的区块链集成深度学习框架,用于安全的物联网 (IoT) 边缘计算. 它通过将区块链透明度与边缘设备的深度学习灵活性相结合,提高了安全性和可信度.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 网络安全 网络安全
背景情况:
- 物联网 (IoT) 设备的扩散需要安全,可扩展和智能边缘计算框架.
- 在脆弱环境中物理部署的边缘节点面临资源操纵和网络威胁的风险.
- 现有的解决方案往往无法平衡安全与资源限制,影响物联网系统的稳定性.
研究的目的:
- 提出一个新的框架,整合区块链和深度学习,以实现安全的物联网边缘计算.
- 通过提高可信度和适应性来解决当前边缘计算安全策略的局限性.
- 开发一种混合架构,利用区块链的透明度和深度学习的灵活性.
主要方法:
- 区块链编排的联合课程学习 (BOFCL) 用于使用区块链威胁指数进行风险优先培训.
- 零知识证明启用安全推理引擎 (ZK-SIE) 以保护隐私和可验证的模型推理.
- 区块链索引对抗攻击模拟器 (BI-AAS) 用于对模拟攻击进行模型测试和再培训.
- 能源意识轻量级共识与自适应同步 (ELCAS) 以实现高效的全球模型同步.
- 信任指数模型来源和部署分类账 (TIMPDL) 用于透明的模型谱系跟踪和部署.
主要成果:
- 该框架结合了数据完整性,对抗性稳定性和对边缘情报的信任意识部署.
- 在训练延迟,同步能耗和隐私泄露方面实现了减少.
- 通过适应性训练序列,提高对高风险边缘场景的反应能力.
- 确保可验证的隐私保护推断和模型完整性,而无需数据暴露.
结论:
- 拟议的区块链集成深度学习框架为物联网中安全的分散边缘智能提供了基础性的进步.
- 混合架构成功地平衡了安全要求与边缘环境的资源限制.
- 这种方法显著提高了物联网边缘计算解决方案的稳定性和可靠性.
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