US 12,341,793 B2
Computer based systems configured to adjust frequency scanning of the dark web in search of information and method of use thereof
Asher Smith-Rose, Midlothian, VA (US); Joshua Edwards, Philadelphia, PA (US); Lin Ni Lisa Cheng, Great Neck, NY (US); Shabnam Kousha, Washington, DC (US); and Tyler Maiman, Melville, NY (US)
Assigned to Capital One Services, LLC, McLean, VA (US)
Filed by Capital One Services, LLC, McLean, VA (US)
Filed on Sep. 28, 2022, as Appl. No. 17/954,807.
Prior Publication US 2024/0106843 A1, Mar. 28, 2024
Int. Cl. H04L 9/40 (2022.01); G06N 3/08 (2023.01)
CPC H04L 63/1425 (2013.01) [G06N 3/08 (2013.01); H04L 63/102 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method comprising:
obtaining, by one or more processors, a trained spam upsurge detection machine learning model that is trained to output a determination when a current frequency associated with spam communications received by a current user exceeds a baseline frequency associated with the current user;
receiving, by the one or more processors, from a computing device of a user, a permission indicator identifying a permission by the user to detect communications being received by the computing device;
receiving, by the one or more processors, from the computing device, an indication of at least one communication being received;
identifying, by the one or more processors, using a spam detection machine learning model, that the at least one communication is a particular spam communication;
updating, by the one or more processors, a frequency at which spam communications have been received by the user based at least in part on the particular spam communication;
utilizing, by the one or more processors, the trained spam upsurge detection machine learning model to determine that the frequency exceeds a baseline frequency associated with the user; and
initiating, by the one or more processors, and in response to the determination, a scan of one or more dark web resources.