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

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18

초록

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.

키워드

graphene; machine learning attack; physical unclonable function; raman spectroscopy; self-assembled monolayer; FIELD-EFFECT TRANSISTORS; ELECTRONIC-STRUCTURE; CHARGE-TRANSFER; RAMAN; PERFORMANCE; STRAIN; PUF
제목
Machine Learning Attacks-Resistant Security by Mixed-Assembled Layers-Inserted Graphene Physically Unclonable Function
저자
Lee, Subin; Jang, Byung Chul; Kim, Minseo; Lim, Si Heon; Ko, Eunbee; Kim, Hyun Ho; Yoo, Hocheon
DOI
10.1002/advs.202302604
발행일
2023-10
유형
Article
저널명
Advanced Science
권
10
호
30