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基于混合组装层插入石墨烯物理不可克隆功能的抗机器学习攻击安全性

Machine Learning Attacks-Resistant Security by Mixed-Assembled Layers-Inserted Graphene Physically Unclonable Function.

作者信息

Lee Subin, Jang Byung Chul, Kim Minseo, Lim Si Heon, Ko Eunbee, Kim Hyun Ho, Yoo Hocheon

机构信息

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

School of Electronics Engineering, Kyungpook National University, 80 Daehakro, Bukgu, Daegu, 41566, Republic of Korea.

出版信息

Adv Sci (Weinh). 2023 Oct;10(30):e2302604. doi: 10.1002/advs.202302604. Epub 2023 Aug 16.

Abstract

Mixed layers of octadecyltrichlorosilane (ODTS) and 1H,1H,2H,2H-perfluorooctyltriethoxysilane (FOTS) on an active layer of graphene are used to induce a disordered doping state and form a robust defense system against machine-learning attacks (ML attacks). The resulting security key is formed from a 12 × 12 array of currents produced at a low voltage of 100 mV. The uniformity and inter-Hamming distance (HD) of the security key are 50.0 ± 12.3% and 45.5 ± 16.7%, respectively, indicating higher security performance than other graphene-based security keys. Raman spectroscopy confirmed the uniqueness of the 10,000 points, with the degree of shift of the G peak distinguishing the number of carriers. The resulting defense system has a 10.33% ML attack accuracy, while a FOTS-inserted graphene device is easily predictable with a 44.81% ML attack accuracy.

摘要

在石墨烯活性层上混合十八烷基三氯硅烷(ODTS)和1H,1H,2H,2H-全氟辛基三乙氧基硅烷(FOTS)层,用于诱导无序掺杂状态,并形成针对机器学习攻击(ML攻击)的强大防御系统。生成的安全密钥由在100 mV低电压下产生的12×12电流阵列形成。安全密钥的均匀性和汉明间距(HD)分别为50.0±12.3%和45.5±16.7%,表明其安全性能高于其他基于石墨烯的安全密钥。拉曼光谱证实了这10000个点的独特性,G峰的位移程度区分了载流子数量。生成的防御系统的ML攻击准确率为10.33%,而插入FOTS的石墨烯器件很容易被预测,ML攻击准确率为44.81%。

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