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

Abstract

Abstract 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.

Keywords

• graphene
• machine learning attack
• physical unclonable function
• raman spectroscopy
• self‐assembled monolayer