Systems and methods for AIDA campaign controller intelligent records

Patent metadata

Field	Value
Publication number	US-11334673-B2
Application number	US-202117361185-A
Country	US
Kind code	B2
Filing date	Jun 28, 2021
Priority date	Dec 1, 2017
Publication date	May 17, 2022
Grant date	May 17, 2022

Abstract

Systems and methods, disclosed herein, of a campaign controller that stores information to a database about execution of multiple simulated phishing campaigns for multiple users, where each of the simulated phishing campaigns use one or more models for communicating simulated phishing communications. Based on this information, the campaign controller may determine a rate of success of the model, in causing a user to interact with a link in one of the simulated phishing campaigns, and may display the model's rate of success via a user interface.

First claim

What is claimed is:

1. A method comprising:
identifying, by one or more processors, an artificial intelligence model trained with results from one or more simulated phishing communications and one or more attributes of one or more users, wherein the artificial intelligence model is configured to take as input one or more attributes of a user and provide as output information for taking an action for a simulated phishing communication to cause a user to interact with a link of the simulated phishing communication;
determining, by the one or more processors based at least on one or more metrics, a rate of success of the artificial intelligence model to cause one or more users of the plurality of users with the one or more attributes to interact with the link of the simulated phishing communication; and
initiating, by the one or more processors, retraining of the artificial intelligence model using the rate of success.

2. The method of claim 1 , wherein the one or more metrics comprise a number of interactions with the link.

3. The method of claim 1 , wherein the one or more metrics comprise one or more of the following: a number of simulated phishing communications communicated, a timing of simulated phishing communications communicated or a type of the number of simulated phishing communications communicated.

4. The method of claim 1 , further comprising initiating, by the one or more processors, retraining of the artificial intelligence model responsive to receipt of new information about the artificial intelligence model.

5. The method of claim 1 , further comprising initiating, by the one or more processors, retraining of the artificial intelligence model responsive to receipt of recipient feedback to the artificial intelligence model.

6. The method of claim 1 , further comprising initiating, by the one or more processors, retraining of the artificial intelligence model responsive to the artificial intelligence model being used a number of times.

7. The method of claim 1 , further comprising initiating, by the one or more processors, retraining of the artificial intelligence model periodically.

8. The method of claim 1 , further comprising initiating, by the one or more processors, retraining of the artificial intelligence model based at least on a history of how effective the model has been.

9. The method of claim 1 , further comprising using, by the one or more processors, AB testing to determine whether the retrained artificial intelligence model or the artificial intelligence model is more effective.

10. The method of claim 1 , further comprising using, by the one or more processors, the retrained artificial intelligence model to create the simulated phishing communication to cause the user to interact with the link of the simulated phishing communication.

11. A system comprising:
one or more processors, coupled to memory, to:
identify an artificial intelligence model trained with results from one or more simulated phishing communications and one or more attributes of one or more users, wherein the artificial intelligence model is configured to take as input one or more attributes of a user and provide as output information for taking an action for a simulated phishing communication to cause a user to interact with a link of the simulated phishing communication;
determine, based at least on one or more metrics, a rate of success of the artificial intelligence model to cause one or more users of the plurality of users with the one or more attributes to interact with the link of the simulated phishing communication; and
initiate retraining of the artificial intelligence model using the rate of success.

12. The system of claim 11 , wherein the one or more metrics comprise a number of interactions with the link.

13. The system of claim 11 , wherein the one or more metrics comprise one or more of the following: a number of simulated phishing communications communicated, a timing of simulated phishing communications communicated or a type of the number of simulated phishing communications communicated.

14. The system of claim 11 , wherein the one or more processors initiate retraining of the artificial intelligence model responsive to receipt of new information about the artificial intelligence model.

15. The system of claim 11 , wherein the one or more processors initiate retraining of the artificial intelligence model responsive to receipt of recipient feedback to the artificial intelligence model.

16. The system of claim 11 , wherein the one or more processors initiate retraining of the artificial intelligence model responsive to the artificial intelligence model being used a number of times.

17. The system of claim 11 , wherein the one or more processors initiate retraining of the artificial intelligence model periodically.

18. The system of claim 11 , wherein the one or more processors initiate retraining of the artificial intelligence model based at least on a history of how effective the model has been.

19. The system of claim 11 , wherein the one or more processors use AB testing to determine whether the retrained artificial intelligence model or the artificial intelligence model is more effective.

20. The system of claim 11 , wherein the one or more processors use the retrained artificial intelligence model to create the simulated phishing communication to cause the user to interact with the link of the simulated phishing communication.

Assignees

• Knowbe4 Inc

Inventors

• Sjouwerman Stu
• Sites Eric

Classifications

• G06N3/0499

  Feedforward networks · CPC title

• G06N3/09

  Supervised learning · CPC title

• H04L63/1433 Primary

  Vulnerability analysis · CPC title

• G06N3/08

  Learning methods · CPC title

• H04L67/10

  in which an application is distributed across nodes in the network (software deployment G06F8/60; multiprogramming arrangements G06F9/46) · CPC title