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Machine Learning Attacks-Resistant Security by Mixed-Assembled Layers-Inserted Graphene Physically Unclonable Function
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DOI：10.1002/advs.202302604.png)
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Abstract
En 中文
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.
Keywords:
graphene
machine learning attack
physical unclonable function
raman spectroscopy
self-assembled monolayer
Journal
IF:
14.1
Papers:
1.8W
Citations:
11.5W
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