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Updated: Jul 19, 2025

Optimized Fabrication Procedure for High-Quality Graphene-based Moiré Superlattice Devices

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机器学习通过混合组装层插入石墨烯物理不可克隆的功能来抵抗攻击的安全性.

Subin Lee¹, Byung Chul Jang^2,3, Minseo Kim¹

¹Department of Electronic Engineering Gachon University, 1342 Seongnam-daero, Seongnam, 13120, Republic of Korea.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)

|August 17, 2023

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此摘要是机器生成的。

这项研究开发了一种基于石墨烯的新型安全密钥,使用混合的西兰层来抵抗机器学习 (ML) 攻击. 与现有方法相比,新密钥显示出优越的安全性能和明显较低的ML攻击精度.

科学领域:

材料科学材料科学材料科学
纳米技术纳米技术
网络安全网络安全

背景情况:

石墨烯独特的电子特性为高级安全应用提供了潜力.
现有的基于石墨烯的安全密钥容易受到复杂的机器学习 (ML) 攻击.
开发强大的,不可伪造的安全密钥对于数据保护至关重要.

研究的目的:

设计基于石墨烯的安全密钥,增强对ML攻击的抵抗力.
调查混合八甲基三西兰 (ODTS) 和 perfluorooctyltriethoxysilane (FOTS) 层用于无序的兴奋剂的使用.
评估生成的安全密钥的安全性能和独特性.

主要方法:

用混合ODTS和FOTS层制造石墨烯设备.
在100mV时生成12x12数组基于电流的安全密钥.
使用拉曼光谱进行表征,以确认独特性和载体度.
评估安全密钥的统一性和关间距离 (HD).
对 ML 攻击精度与开发的安全系统进行评估.

主要成果:

混合的西兰层诱导了石墨烯中无序的兴奋剂状态.

关键词:

石墨烯是一种石墨烯.机器学习攻击机器学习攻击.物理上的不可克隆的功能.雷曼光谱法雷曼光谱法自组装的单层单层.

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安全钥匙具有很高的统一性 (50.0 ± 12.3%) 和关间距离 (45.5 ± 16.7%).

拉曼光谱通过分析G峰值转移证实了1万个数据点的独特性.

开发的防御系统实现了10.33%的低ML攻击精度.

仅使用FOTS的石墨烯装置显示了显著更高的ML攻击精度 (44.81%).

结论:

在石墨烯上混合ODTS/FOTS层创建了一个强大的安全密钥,可以抵抗ML攻击.
与以前的基于石墨烯的密钥相比,无序的兴奋剂状态和独特的密钥生成提供了更高的安全性.
这种方法为开发下一代安全硬件提供了一个有希望的途径.