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The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
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机器学习增强的基于属性的身份验证,用于安全的物联网访问控制.

Jibran Saleem1, Umar Raza1, Mohammad Hammoudeh2

  • 1Department of Engineering, Faculty of Science and Engineering, Manchester Metropolitan University, Manchester M1 5GD, UK.

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|May 14, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了用于安全物联网 (IoT) 认证的SmartIoT混合机器学习 (ML) 模型. 它通过使用基于属性的方法和ML异常检测来提高工业4.0环境中的安全性和效率.

关键词:
工业4.0 工业4.0 工业4.0 工业4.0 工业4.0 是什么?这就是为什么物联网物联网物联网.基于属性的身份验证.混合动力ML混合动力ML机器学习是机器学习.随机的森林随机的森林安全的安全的安全的安全的安全.

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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
  • 机器学习 机器学习

背景情况:

  • 物联网 (IoT) 设备的快速增长需要先进的身份验证.
  • 传统系统在像工业4.0这样的资源有限的环境中,在平衡安全,隐私和效率方面面临挑战.
  • 现有的解决方案往往难以处理计算开销和保护敏感信息.

研究的目的:

  • 介绍SmartIoT混合机器学习 (ML) 模型,用于增强物联网身份验证.
  • 为了提高安全性,并最大限度地减少认证机制中的计算开销.
  • 为低功耗物联网设备和工业4.0应用提供适合的解决方案.

主要方法:

  • 整合基于属性的身份验证与轻量级机器学习算法.
  • 利用随机森林分类器,根据用户属性,登录模式和行为分析实时检测异常.
  • 整合保护隐私的基于属性的凭证和基于属性的签名.

主要成果:

  • 实现了86%的认证准确度,88%的精度和96%的回忆.
  • 平均响应时间为112ms,适合低功耗物联网设备.
  • 在实验评估中显著超过现有解决方案.

结论:

  • 智能物联网混合ML模型为物联网身份验证提供了增强的安全性,隐私性和计算效率.
  • 该模型表现出强大的安全弹性,效率和适应现实应用的适应性.
  • 它为保护关键部门和工业4.0环境提供了可行的解决方案.