Machine Learning Attacks-Resistant Security by Mixed-Assembled Layers-Inserted Graphene Physically Unclonable Functionopen access
- Authors
- Lee, Subin; Jang, Byung Chul; Kim, Minseo; Lim, Si Heon; Ko, Eunbee; Kim, Hyun Ho; Yoo, Hocheon
- Issue Date
- Oct-2023
- Publisher
- WILEY
- Keywords
- graphene; machine learning attack; physical unclonable function; raman spectroscopy; self-assembled monolayer
- Citation
- ADVANCED SCIENCE, v.10, no.30
- Journal Title
- ADVANCED SCIENCE
- Volume
- 10
- Number
- 30
- URI
- https://scholarworks.bwise.kr/kumoh/handle/2020.sw.kumoh/26378
- DOI
- 10.1002/advs.202302604
- ISSN
- 2198-3844
2198-3844
- 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 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.
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