Login / Signup

Machine Learning Attacks-Resistant Security by Mixed-Assembled Layers-Inserted Graphene Physically Unclonable Function.

Subin LeeByung Chul JangMinseo KimSi Heon LimEunbee KoHyun Ho KimHocheon Yoo
Published in: Advanced science (Weinheim, Baden-Wurttemberg, Germany) (2023)
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.
Keyphrases
  • global health
  • machine learning
  • raman spectroscopy
  • room temperature
  • carbon nanotubes
  • walled carbon nanotubes
  • public health
  • artificial intelligence
  • high resolution
  • high density