CPC H04L 63/1425 (2013.01) [G06F 16/22 (2019.01); G06F 16/2474 (2019.01); G06F 21/554 (2013.01); G06N 5/01 (2023.01); G06N 5/04 (2013.01); G06N 20/00 (2019.01); G06N 20/20 (2019.01); G06Q 20/4016 (2013.01); H04L 41/0604 (2013.01); H04L 43/16 (2013.01); H04L 63/0428 (2013.01); G06F 3/0484 (2013.01); G06F 21/6245 (2013.01); G06F 2221/034 (2013.01); H04L 63/1416 (2013.01)] | 19 Claims |
1. A method comprising:
receiving an input dataset;
using a machine learning model to determine a model score for each data record of at least a portion of the input dataset;
determining monitoring values including by summing individual divergence contributions for each bin of a group of bins, wherein each monitoring value of at least a portion of the determined monitoring values is associated with a measure of similarity comparing (i) a histogram of model scores for those data records of the input dataset within a corresponding moving reference window and (ii) a histogram of model scores for those data records of the input dataset within a corresponding moving target window; and
outputting the determined monitoring values.
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