Lee Subin, Jang Byung Chul, Kim Minseo, Lim Si Heon, Ko Eunbee, Kim Hyun Ho, Yoo Hocheon
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.
Adv Sci (Weinh). 2023 Oct;10(30):e2302604. doi: 10.1002/advs.202302604. Epub 2023 Aug 16.
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.
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%。