US 12,001,530 B2
Machine-learning for password guess risk determination
Rocio Cabrera Lozoya, Antibes (FR); Slim Trabelsi, Biot (FR); and Carlos Rafael Ocanto Davila, Neuilly sur Seine (FR)
Assigned to SAP SE, Walldorf (DE)
Filed by SAP SE, Walldorf (DE)
Filed on Feb. 9, 2022, as Appl. No. 17/668,208.
Prior Publication US 2023/0252114 A1, Aug. 10, 2023
Int. Cl. G06F 21/00 (2013.01); G06F 21/31 (2013.01); G06F 40/253 (2020.01); G06F 40/263 (2020.01); G06F 40/40 (2020.01)
CPC G06F 21/31 (2013.01) [G06F 40/253 (2020.01); G06F 40/263 (2020.01); G06F 40/40 (2020.01)] 20 Claims
OG exemplary drawing
 
1. A system comprising:
at least one hardware processor; and
a non-transitory computer-readable medium storing instructions that, when executed by the at least one hardware processor, cause the at least one hardware processor to perform operations comprising:
accessing information about a computing resource;
accessing information about a user attempting to access the computing resource;
using the information about the computing resource to retrieve a computing domain-specific text corpus for a domain encompassing the computing resource;
using the information about the user to retrieve a user information-specific text corpus for a piece of user information in the accessed information about the user;
passing the domain-specific text corpus to a first natural language processing (NLP) machine-learned model trained specifically for the domain, a first NLP machine-learned model outputting domain-specific word embeddings;
passing the user information-specific text corpus to a second NLP machine-learned model trained specifically for the piece of user information, the second NLP machine-learned model outputting user information-specific word embeddings; and
inputting the domain-specific word embeddings and the user information-specific word embeddings into a probabilistic context free grammar (PCFG) machine-learned model, the PCFG machine-learned model updating itself based on the input and outputting a set of password guesses.