一个基于机器学习的身份验证和入侵检测方案,用于物联网用户隐私保护在雾环境中的匿名性
Khondokar Oliullah1,2, Md Whaiduzzaman2,3,4, Md Julkar Nayeen Mahi2,3
1Department of Information and Communication Technology, Comilla University, Cumilla, Bangladesh.
PloS one
|June 16, 2025
概括
本研究介绍了雾计算的两步身份验证,通过使用圆曲线加密 (ECC) 和集体学习来提高物联网安全性,用于入侵检测. 它确保了隐私和强大的安全性,防止未经授权的访问.
科学领域:
- 计算机科学 计算机科学
- 网络安全 网络安全
- 网络安全 网络安全
背景情况:
- 雾计算安全面临着物联网用户身份验证的挑战,通常需要披露敏感数据.
- 现有的方法在快速增长的物联网网络中努力平衡低计算开销和强大的安全性.
- 传统的入侵检测方法对物联网设备的准确性较低.
研究的目的:
- 为雾计算环境提出一种新,安全和高效的两步身份验证方案.
- 通过尽量减少在身份验证期间共享的敏感信息来增强用户隐私.
- 为了提高雾网络中的物联网设备的入侵检测准确度.
主要方法:
- 实施了两步验证:使用圆曲线加密 (ECC) 和秘密ID进行匿名验证,然后进行入侵检测.
- 用户使用加密凭证在云服务提供商 (CSP) 注册.
- 参与组合学习方法用于在可疑用户活动中检测异常.
主要成果:
- 拟议的方案在入侵检测方面实现了高准确性 (99.86%),精度 (99.89%),回忆 (99.96%) 和F1得分 (99.91%).
- 确保使用哈希函数和对称加密/解密的安全端到端通信.
- 在雾环境中,成功地平衡了低计算开销与对物联网用户的强大安全性.
结论:
- 新的两步验证方案显著提高了雾计算安全性和用户隐私.
- 股权集团学习方法为物联网设备提供高度准确的入侵检测.
- 拟议的方法为大型物联网网络中的身份验证提供了安全有效的解决方案.
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