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一个可扩展和安全的联合学习认证方案用于物联网.

Premkumar Chithaluru1,2, B Veera Jyothi3, Fahd S Alharithi4

  • 1Symbiosis Institute of Technology, Hyderabad Campus, Symbiosis International (Deemed University), Pune, India.

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本研究介绍了ScLBS,这是物联网 (IoT) 网络的新型身份验证方案. ScLBS使用联合学习 (FL) 来增强安全性和可扩展性,同时降低物联网设备的资源消耗.

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科学领域:

  • 计算机科学 计算机科学
  • 网络安全 网络安全
  • 密码学 密码学 密码学 密码学

背景情况:

  • 物联网 (IoT) 网络面临着由于资源限制和动态拓学的重大身份验证挑战.
  • 传统的身份验证方法,如基于密码和基于证书的方案,由于高开销,不适合大规模的物联网部署.
  • 缺乏集中式信任基础设施使分布式物联网环境中的安全通信复杂化.

研究的目的:

  • 为分布式和可持续的物联网环境提供基于联合学习 (FL) 的自我认证认证方案Sclbs.
  • 为了实现分散的公钥衍生,并加强信任适应,而不依赖第三方证书颁发机构.
  • 为了提高对各种攻击的抵抗力,如假冒身份,Sybil和在物联网网络中重复攻击.

主要方法:

  • 自认证的公钥加密与FL驱动的信任调整的整合.
  • 实施零知识机制和位置认证认证.
  • 使用与[公式:参见文本]树的等级密钥管理,以实现高效的组回购和保密.
  • 在Dolev-Yao对手模型下使用ProVerif进行正式的安全验证.
  • 通过NS-3模拟和废弃分析进行性能评估.

主要成果:

  • 与现有的物联网身份验证方案相比,ScLBS表现出较低的身份验证延迟和减少的消息开销.
  • 在模拟中观察到网络利用率的提高和能源消耗的降低.
  • 该计划保持有界的联合学习开销,同时确保安全.
  • 正式验证证实了私钥和会话密钥的保密性以及认证的正确性.

结论:

  • 在受限制的物联网网络中,ScLBS提供了安全性,可扩展性和资源效率之间的平衡权衡.
  • 拟议的方案有效地解决了物联网中安全和可扩展的身份验证的基本挑战.
  • 联合学习集成对于物联网环境中的信任适应和去中心化密钥管理是有益的.