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Machine Learning Attacks-Resistant Security by Mixed-Assembled Layers-Inserted Graphene Physically Unclonable Function
- Lee, Subin;
- Jang, Byung Chul;
- Kim, Minseo;
- Lim, Si Heon;
- Ko, Eunbee;
- 외 2명
WEB OF SCIENCE
13SCOPUS
18초록
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
- 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
- 발행일
- 2023-10
- 유형
- Article
- 저널명
- Advanced Science
- 권
- 10
- 호
- 30
- 언어
- ENG
- 출판사
- WILEY
- 발행국가
- 미국
- ISSN
- E 2198-3844