Machine Learning Attacks‐Resistant Security by Mixed‐Assembled Layers‐Inserted Graphene Physically Unclonable Function (Adv. Sci. 30/2023)

Machine Learning Attacks‐Resistant Security In article number 2302604, Hocheon Yoo and co‐workers present an efficient method for extracting a security key using graphene at just 100 mV voltage. By introducing diverse functional groups via mixed‐assembled monolayers into the graphene device, they cr...

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Detalles Bibliográficos
Autores principales: Lee, Subin, Jang, Byung Chul, Kim, Minseo, Lim, Si Heon, Ko, Eunbee, Kim, Hyun Ho, Yoo, Hocheon
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2023
Materias:
Frontispiece
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10602504/
http://dx.doi.org/10.1002/advs.202370204
Descripción
Sumario:Machine Learning Attacks‐Resistant Security In article number 2302604, Hocheon Yoo and co‐workers present an efficient method for extracting a security key using graphene at just 100 mV voltage. By introducing diverse functional groups via mixed‐assembled monolayers into the graphene device, they created an unconventional dipole distribution, yielding distinct characteristics and abundant randomness. This approach achieves significant results: 50% uniformity, 45.5% inter‐Hamming distance, and a strong 10.33% defense rate against machine learning attacks. [Image: see text]

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