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

被引:7
作者
Lee, Subin [1 ]
Jang, Byung Chul [2 ,3 ]
Kim, Minseo [1 ]
Lim, Si Heon [4 ,5 ]
Ko, Eunbee [4 ,5 ]
Kim, Hyun Ho [4 ,5 ]
Yoo, Hocheon [1 ]
机构
[1] Gachon Univ, Dept Elect Engn, 1342 Seongnam Daero, Seongnam 13120, South Korea
[2] Kyungpook Natl Univ, Sch Elect Engn, 80 Daehakro, Daegu 41566, South Korea
[3] Kyungpook Natl Univ, Sch Elect & Elect Engn, 80 Daehakro, Daegu 41566, South Korea
[4] Kumoh Natl Inst Technol, Dept Energy Engn Convergence, 61 Daehakro, Gumi Si 39177, Gumi, South Korea
[5] Kumoh Natl Inst Technol, Sch Mat Sci & Engn, 61 Daehakro, Gumi Si 39177, Gumi, South Korea
基金
新加坡国家研究基金会;
关键词
graphene; machine learning attack; physical unclonable function; raman spectroscopy; self-assembled monolayer; FIELD-EFFECT TRANSISTORS; ELECTRONIC-STRUCTURE; CHARGE-TRANSFER; RAMAN; PERFORMANCE; STRAIN; PUF;
D O I
10.1002/advs.202302604
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
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 x 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 & PLUSMN; 12.3% and 45.5 & PLUSMN; 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.
引用
收藏
页数:11
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