US 12,088,613 B2
Machine learning powered authentication challenges
Neil Shah, Los Angeles, CA (US); Mingyi Zhao, Los Angeles, CA (US); and Yu-Hsin Chen, Los Angeles, CA (US)
Assigned to Snap Inc., Santa Monica, CA (US)
Filed by Snap Inc., Santa Monica, CA (US)
Filed on Apr. 20, 2023, as Appl. No. 18/303,807.
Application 18/303,807 is a continuation of application No. 16/450,463, filed on Jun. 24, 2019, granted, now 11,641,368.
Prior Publication US 2023/0262082 A1, Aug. 17, 2023
This patent is subject to a terminal disclaimer.
Int. Cl. H04L 9/40 (2022.01); G06N 20/00 (2019.01)
CPC H04L 63/1425 (2013.01) [G06N 20/00 (2019.01); H04L 63/083 (2013.01); H04L 63/1433 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method comprising:
receiving, by one or more processors, a plurality of user login attempts, each user login attempt associated with a user of a client device and a login source;
randomly sampling a portion of the plurality of user login attempts for each of the randomly sampled user login attempts:
generating a login feature vector associated with the user login attempt, the login feature vector comprising a plurality of user-context features that represent a probability of malicious software attacks;
associating the plurality of user-context features with a plurality of respective risk values;
determining a risk score associated with the user login attempt based on the plurality of respective risk values using a trained machine learning model;
determining that the risk score exceeds a predetermined threshold value;
in response to the determination that the risk score exceeds the predetermined threshold value, issuing an authentication challenge to the user; and
associating a challenge response label with the login feature vector based on a user response to the authentication challenge.