التفاصيل البيبلوغرافية
| العنوان: |
Machine Learning Attacks‐Resistant Security by Mixed‐Assembled Layers‐Inserted Graphene Physically Unclonable Function. |
| المؤلفون: |
Lee, Subin, Jang, Byung Chul, Kim, Minseo, Lim, Si Heon, Ko, Eunbee, Kim, Hyun Ho, Yoo, Hocheon |
| المصدر: |
Advanced Science; 10/26/2023, Vol. 10 Issue 30, p1-11, 11p |
| مصطلحات موضوعية: |
MACHINE learning, GRAPHENE, RAMAN spectroscopy, MIXING height (Atmospheric chemistry), LOW voltage systems |
| مستخلص: |
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. [ABSTRACT FROM AUTHOR] |
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