US 12,010,142 B2
Phishing mail generator with adaptive complexity using generative adversarial network
Alok Singh, Ghaziabad (IN); Nitish Kumar, Jamshedpur (IN); and Kanishka Kayathwal, Kota (IN)
Assigned to MASTERCARD INTERNATIONAL INCORPORATED, Purchase, NY (US)
Filed by MASTERCARD INTERNATIONAL INCORPORATED, Purchase, NY (US)
Filed on Sep. 8, 2021, as Appl. No. 17/469,009.
Prior Publication US 2023/0075964 A1, Mar. 9, 2023
Int. Cl. H04L 9/40 (2022.01); G06N 3/02 (2006.01); G06N 3/044 (2023.01); G06N 3/045 (2023.01); G06N 3/088 (2023.01)
CPC H04L 63/1483 (2013.01) [G06N 3/044 (2023.01); G06N 3/045 (2023.01); G06N 3/088 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method for generating phishing electronic mail (email) with adaptive complexity, comprising:
obtaining a plurality of phishing emails from a trained generative adversarial neural network, the trained generative adversarial neural network including a generator neural network and a discriminator neural network;
selecting a subset of phishing emails, from the plurality of phishing emails, using a reinforcement learning system trained on user-specific behavior based on likelihood that a particular user will take action on one or more of the subset of phishing emails;
sending one or more of the subset of phishing emails to a user email account associated with the particular user; and
adjusting the reinforcement learning system to select one or more phishing emails that are likely to cause the particular user to take action, based on user action feedback to the one or more of the subset of phishing emails.