US 12,474,905 B2
Detecting vulnerabilities to fault injection in computer code using machine learning
Alexander Matrosov, Hillsboro, OR (US); and Christopher Schneider, Fife (GB)
Assigned to NVIDIA Corporation, Santa Clara, CA (US)
Filed by NVIDIA Corporation, Santa Clara, CA (US)
Filed on Oct. 31, 2018, as Appl. No. 16/177,311.
Claims priority of provisional application 62/678,202, filed on May 30, 2018.
Prior Publication US 2019/0370473 A1, Dec. 5, 2019
Int. Cl. H04L 29/06 (2006.01); G06F 8/41 (2018.01); G06F 8/75 (2018.01); G06F 18/214 (2023.01); G06F 21/54 (2013.01); G06F 21/57 (2013.01); G06N 20/00 (2019.01)
CPC G06F 8/433 (2013.01) [G06F 8/75 (2013.01); G06F 18/214 (2023.01); G06F 21/54 (2013.01); G06F 21/577 (2013.01); G06N 20/00 (2019.01)] 26 Claims
OG exemplary drawing
 
1. One or more processors comprising:
circuitry to use one or more neural networks to identify, based, at least in part on a graph topology indicating one or more conditional branches that are dependent on one or more cryptologic primitives of a software program, one or more vulnerabilities to a fault injection attack at a physical layer of a computer system to perform the software program, wherein the fault injection attack is to cause the one or more cryptologic primitives to be corrupted or one or more security procedures to be bypassed.