Machine Learning Attacks‐Resistant Security by Mixed‐Assembled Layers‐Inserted Graphene Physically Unclonable Function - Subin Lee; Byung Chul Jang; Minseo Kim; Si Heon Lim; Eunbee Ko; Hyun Ho Kim; Hocheon Yoo (Advanced Science, 2023)
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.