Systems and methods for data breach detection using virtual card numbers

Patent metadata

Field	Value
Publication number	US-11694205-B2
Application number	US-202117161732-A
Country	US
Kind code	B2
Filing date	Jan 29, 2021
Priority date	Jan 29, 2021
Publication date	Jul 4, 2023
Grant date	Jul 4, 2023

Abstract

Disclosed are systems and methods for data breach identification. The method may include: generating virtual card number (VCN) data sets; storing the VCN data sets on a first database; receiving one or more compromised VCN data sets stored on a second database and obtained from a scan of unindexed websites; comparing the compromised VCN data sets with the VCN data set stored on the first database to determine whether the VCN data sets have been compromised; for each compromised VCN data set, training the recurrent neural network (RNN) to associate the compromised VCN data sets with one or more sequential patterns found within the compromised VCN data sets to generate a trained RNN; receiving a first VCN data set from the first database; determining whether the first VCN data set matches a compromised VCN data set; and transmitting a message indicating the determination to a user or provider device.

First claim

What is claimed is:

1. A computer-implemented method for training and using a recurrent neural network for data breach identification, the method comprising:
generating, by one or more processors, a plurality of virtual card numbers, wherein each one of the plurality of virtual card numbers is associated with a user device, a provider device, and security data to generate a virtual card number data set;
storing, by the one or more processors, one or more of the virtual card number data sets on a first database;
receiving, by the one or more processors, one or more compromised virtual card number data sets, wherein the one or more compromised virtual card number data sets is parsed from compromised data stored on a second database isolated from communication with the first database, and wherein the compromised data is obtained from a scan of unindexed websites on a network;
comparing, by the one or more processors, the one or more compromised virtual card number data sets with the one or more virtual card number data sets stored on the first database;
determining, by the one or more processors, whether one of the one or more of the virtual card number data sets has been compromised based on the comparison;
for each of the one or more compromised virtual card number data sets, training, by the one or more processors, the recurrent neural network to associate the compromised virtual card number data set with one or more sequential patterns found within the compromised virtual card number data set, to generate a trained recurrent neural network;
receiving a first virtual card number data set from the first database;
determining, using the trained recurrent neural network, by the one or more processors, whether the first virtual card number data set matches a compromised virtual card number data set by detecting at least one of the one or more sequential patterns found within the compromised virtual card number data set;
upon determining the first virtual card number data set matches a compromised virtual card number data set, automatically regenerating a virtual card number for the first virtual card number data set; and
transmitting, by the one or more processors, the regenerated virtual card number and a message to the user device or the provider device associated with the first virtual card number data set indicating the first virtual card number data set is compromised.

2. The method of claim 1 , further comprising:
receiving, by the one or more processors, a request to authenticate a transaction; and
declining to authenticate the transaction upon determining, by the one or more processors, that the transaction involves a compromised virtual card number data set.

3. The method of claim 1 , wherein the security data comprises one or more of a card verification value, a card verification code, a physical address number, or a personal identification number associated with a user device.

4. The method of claim 3 , wherein the determining the first virtual card number data set matches a compromised virtual card number data set stored on the second database further comprises determining whether the virtual card number and one or more of the user device, provider device, or security data of the first virtual card number data set is similar to the virtual card number and one or more of the user device, provider device, or security data of the compromised virtual card number data sets stored on the second database.

5. The method of claim 4 , wherein the determining the first virtual card number data set matches a compromised virtual card number data set stored on the second database further comprises determining, by the one or more processors, whether a pre-determined threshold is satisfied.

6. The method of claim 1 , further comprising:
upon parsing one or more virtual card number data sets from the compromised data on the second database, storing, by the one or more processors, the parsed virtual card number data sets on a third database separate from the second database, wherein comparing the one or more compromised virtual card number data sets with the one or more virtual card number data sets stored on the first database comprises comparing the one or more compromised virtual card number data sets stored on the third database with the virtual card number data sets stored on the first database.

7. The method of claim 6 , wherein the third database is located at a physical location remote from the first and second databases.

8. The method of claim 1 , wherein the second database is located at a physical location remote from the first database.

9. The method of claim 1 , wherein transmitting the message further includes causing presentation of the message via a user interface of a user or provider device.

10. The method of claim 9 , wherein causing presentation of the message via the user interface includes presentation by at least one of a voice notification, application notification, tactile notification, or graphic notification.

11. A system for training and using a recurrent neural network for data breach identification, the system comprising:
at least one memory device having processor-readable instructions stored therein; and
at least one central processing unit including at least one processor configured to access the memory device and execute the processor-readable instructions, which when executed by the processor configures the processor to perform a plurality of functions, including functions for:
generating a plurality of virtual card numbers, wherein each one of the plurality of virtual card numbers is associated with a user device, a provider device, and security data to generate a virtual card number data set;
storing one or more of the virtual card number data sets on a first database;
receiving, by the one or more processors, one or more compromised virtual card number data sets, wherein the one or more compromised virtual card number data sets is parsed from compromised data stored on a second database isolated from communication with the first database, and wherein the compromised data is obtained from a scan of unindexed websites on a network;
comparing the one or more compromised virtual card number data sets with the one or more virtual card number data sets stored on the first database;
determining whether one of the one or more of the virtual card number data sets has been compromised based on the comparison;
training the recurrent neural network to associate the compromised virtual card number data set with one or more sequential patterns found within the compromised virtual card number data set, to generate a trained recurrent neural network;
receiving a first virtual card number data set from the first database;
determining, using the trained recurrent neural network, whether the first virtual card number data set matches a compromised virtual card number data set by detecting at least one of the one or more sequential patterns found within the compromised virtual card number data set;
upon determining the first virtual card number data set matches a compromised virtual card number data set, automatically regenerating a virtual card number for the first virtual card number data set; and
transmitting the regenerated virtual card number and a message to a user or provider device associated with the first virtual card number data set indicating the first virtual card number data set is compromised.

12. The system of claim 11 , wherein the processor is further configured to perform functions for:
receiving a request to authenticate a transaction; and
declining to authenticate the transaction upon determining that the transaction involv

Assignees

• Capital One Services Llc

Inventors

• Learned Jacob
• Saia Michael
• Miracolo Max
• Gibilterra Kaylyn

Classifications

• G06N3/09

  Supervised learning · CPC title

• G06N3/0442

  characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU] · CPC title

• G06N3/08

  Learning methods · CPC title

• G06Q20/351

  Virtual cards · CPC title

• G06Q20/4016 Primary

  involving fraud or risk level assessment in transaction processing · CPC title