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1. WO2022165510 - SYSTEMS AND METHODS FOR DATA BREACH DETECTION USING VIRTUAL CARD NUMBERS

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Publication Number WO/2022/165510
Publication Date 04.08.2022
International Application No. PCT/US2022/070406
International Filing Date 28.01.2022
IPC
G06Q 20/34 2012.1
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
20Payment architectures, schemes or protocols
30characterised by the use of specific devices
34using cards, e.g. integrated circuit cards or magnetic cards
G06Q 20/40 2012.1
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
20Payment architectures, schemes or protocols
38Payment protocols; Details thereof
40Authorisation, e.g. identification of payer or payee, verification of customer or shop credentials; Review and approval of payers, e.g. check of credit lines or negative lists
CPC
G06N 3/08
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computer systems based on biological models
02using neural network models
08Learning methods
G06Q 20/351
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
20Payment architectures, schemes or protocols
30characterised by the use of specific devices ; or networks
34using cards, e.g. integrated circuit [IC] cards or magnetic cards
351Virtual cards
G06Q 20/40
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
20Payment architectures, schemes or protocols
38Payment protocols; Details thereof
40Authorisation, e.g. identification of payer or payee, verification of customer or shop credentials; Review and approval of payers, e.g. check credit lines or negative lists
G06Q 20/4016
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
20Payment architectures, schemes or protocols
38Payment protocols; Details thereof
40Authorisation, e.g. identification of payer or payee, verification of customer or shop credentials; Review and approval of payers, e.g. check credit lines or negative lists
401Transaction verification
4016involving fraud or risk level assessment in transaction processing
H04L 63/1416
HELECTRICITY
04ELECTRIC COMMUNICATION TECHNIQUE
LTRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
63Network architectures or network communication protocols for network security
14for detecting or protecting against malicious traffic
1408by monitoring network traffic
1416Event detection, e.g. attack signature detection
H04L 63/1425
HELECTRICITY
04ELECTRIC COMMUNICATION TECHNIQUE
LTRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
63Network architectures or network communication protocols for network security
14for detecting or protecting against malicious traffic
1408by monitoring network traffic
1425Traffic logging, e.g. anomaly detection
Applicants
  • CAPITAL ONE SERVICES, LLC [US]/[US]
Inventors
  • LEARNED, Jacob
  • SAIA, Michael
  • MIRACOLO, Max
  • GIBILTERRA, Kaylyn
Agents
  • SUTTON, Andrew
  • WINCHESTER, Jessica
Priority Data
17/161,73229.01.2021US
Publication Language English (en)
Filing Language English (EN)
Designated States
Title
(EN) SYSTEMS AND METHODS FOR DATA BREACH DETECTION USING VIRTUAL CARD NUMBERS
(FR) SYSTÈMES ET PROCÉDÉS PERMETTANT UNE DÉTECTION DE VIOLATION DE DONNÉES À L'AIDE DE NUMÉROS DE CARTE VIRTUELLE
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Abstract
(EN) 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.
(FR) L'invention divulgue des systèmes et des procédés permettant une identification de violation de données. Le procédé peut consister : à générer des ensembles de données de numéro de carte virtuelle (VCN pour Virtual Card Number) ; à stocker les ensembles de données de numéro VCN sur une première base de données ; à recevoir un ou plusieurs ensembles de données de numéro VCN compromis stockés sur une seconde base de données et obtenus à partir d'un balayage de sites web non indexés ; à comparer des ensembles de données de numéro VCN compromis avec l'ensemble de données de numéro VCN stocké sur la première base de données pour déterminer si les ensembles de données de numéro VCN ont été compromis ; pour chaque ensemble de données de numéro VCN compromis, à former le réseau neuronal récurrent (RNN pour Recurrent Neural Network) pour associer les ensembles de données de numéro VCN compromis avec un ou plusieurs motifs séquentiels trouvés dans les ensembles de données de numéro VCN compromis pour générer un réseau RNN formé ; à recevoir un premier ensemble de données de numéro VCN en provenance de la première base de données ; à déterminer si le premier ensemble de données de numéro VCN correspond à un ensemble de données de numéro VCN compromis ; et à transmettre un message indiquant la détermination à un dispositif d'utilisateur ou de fournisseur.
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