通过神经元关键性分析和轻量级加密来增强虚拟物理无法克隆的功能安全性
Raviha Khan1, Hani Saleh2, Brahim Mefgouda3
1Center for Cyber-Physical Systems - System on Chip Lab and Computer and Information Engineering Department, Khalifa University, Abu Dhabi, UAE.
Scientific reports
|October 17, 2025
概括
这项研究通过使用一种新的加密框架来增强虚拟物理无法克隆功能的 (VPUF) 安全性. 它实现了高附加安全性与最小的延迟,保护物联网设备免受逆向工程.
科学领域:
- 计算机科学 计算机科学
- 网络安全 网络安全
- 硬件安全 硬件安全
背景情况:
- 物理非克隆功能 (PUF) 提供基于硬件的设备认证,使用制造变量.
- 传统的PUF面临着硬件开销,老化和易受攻击等局限性.
- 虚拟PUF (VPUF) 作为一种基于软件的替代方案,用于物联网环境中使用神经网络.
研究的目的:
- 加强虚拟物理不可克隆函数 (VPUFs) 对物理提取和逆向工程的安全性.
- 为VPUF引入一个轻量级,神经元关键性意识的加密框架.
- 为了保持VPUF的性能和准确性,同时提高安全性.
主要方法:
- 开发了一种轻量级的加密框架,专注于通过剥离分析识别的关键神经元.
- 应用了基于XOR的选择性加密来最大限度地降低计算开销.
- 集成了一个使用雷利衰减 (杰克模型) 的动态密钥生成机制.
主要成果:
- 在VPUF模型提取和逆向工程方面实现了高达99.4%的附加安全性.
- 证明了微秒级延迟,保持了认证准确性.
- 成功识别并利用关键神经元进行高效的加密.
结论:
- 拟议的神经元关键性意识加密框架显著提高了VPUF的安全性.
- 这种方法为保护物联网设备提供了可扩展和高效的解决方案.
- 该方法平衡了强大的安全性与最小的性能影响.
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