Goods & Services

Providing temporary use of online non-downloadable software for use in credit card and identity fraud detection and prevention, and post-fraud detection, investigation, resolution, and support, namely, for use in automating credit card transaction screening, credit card transaction filtering and prioritization, credit card transaction review, credit card transaction resolution, credit card chargeback processing, and credit card fraud analyst monitoring; application service provider, namely, providing, hosting, managing, developing, and maintaining applications, software, websites, and databases in the fields of credit card and identity fraud detection and prevention; Electronic monitoring of credit card activity to detect fraud via the internet; Electronic monitoring of credit card use for online purchasing to detect fraud via the internet; Electronic monitoring of personally identifying information to detect identity theft via the internet; Electronic monitoring of banking activity to detect fraud via the internet; providing advice and consultation related to the electronic monitoring of credit card activity to detect fraud via the internet; Providing user authentication services using biometric hardware, trusted path and device recognition software technology, tranformer neural networks and artificial intelligence for e-commerce transactions Personal background verification services

Goods & Services

Providing temporary use of online non-downloadable software for use in credit card and identity fraud detection and prevention, and post-fraud detection, investigation, resolution, and support, namely, for use in automating credit card transaction screening, credit card transaction filtering and prioritization, credit card transaction review, credit card transaction resolution, credit card chargeback processing, and credit card fraud analyst monitoring; application service provider, namely, providing, hosting, managing, developing, and maintaining applications, software, websites, and databases in the fields of credit card and identity fraud detection and prevention; Electronic monitoring of credit card activity to detect fraud via the internet; Electronic monitoring of credit card use for online purchasing to detect fraud via the internet; Electronic monitoring of personally identifying information to detect identity theft via the internet; Electronic monitoring of banking activity to detect fraud via the internet; providing advice and consultation related to the electronic monitoring of credit card activity to detect fraud via the internet; Providing user authentication services using biometric hardware, trusted path and device recognition software technology, tranformer neural networks and artificial intelligence for e-commerce transactions Personal background verification services

Abstract

A system for detecting whether a device seeking communication with a server is a returning device that previously communicated with the server includes a database that stores groups of device attributes based on observable device characteristics and unique identifiers. The database is generally not accessible to the devices. Each attribute group and the associated device identifier (DID) can uniquely identify a particular device, and the associated DID is generally not derivable from the attributes. The database may satisfy a uniqueness property so that each attribute value in the database may also uniquely identify a device.

IPC Classes  ?

• H04L 67/50 - Network services
• H04L 9/40 - Network security protocols
• H04L 67/303 - Terminal profiles

Abstract

A system for establishing a trusted path for secure communication between client devices and server devices, such as between an account holder and a financial institution, can provide the core security attributes of confidentiality (of the parties), integrity (of the information), anti-replay (protection against replay fraud) and/or anti-tampering (protection against unauthorized changes to information being exchanged and/or modules that generate and communicate such information). A messaging layer implementation in favor of a transport layer implementation can provide a trusted path. This infrastructure features secure cryptographic key storage, and implementation of a trusted path built using the cryptographic infrastructure. The trusted path protects against unauthorized information disclosure, modification, or replays. These services can effectively protect against Man-in-the-Middle, Man-in-the-Application, and other attacks.

IPC Classes  ?

• H04L 9/32 - Arrangements for secret or secure communicationsNetwork security protocols including means for verifying the identity or authority of a user of the system
• G06F 21/53 - Monitoring users, programs or devices to maintain the integrity of platforms, e.g. of processors, firmware or operating systems during program execution, e.g. stack integrity, buffer overflow or preventing unwanted data erasure by executing in a restricted environment, e.g. sandbox or secure virtual machine
• G06F 21/56 - Computer malware detection or handling, e.g. anti-virus arrangements
• G06F 21/57 - Certifying or maintaining trusted computer platforms, e.g. secure boots or power-downs, version controls, system software checks, secure updates or assessing vulnerabilities
• G06F 21/60 - Protecting data
• G06F 21/64 - Protecting data integrity, e.g. using checksums, certificates or signatures
• G06Q 20/38 - Payment protocolsDetails thereof
• H04L 9/40 - Network security protocols
• H04W 12/02 - Protecting privacy or anonymity, e.g. protecting personally identifiable information [PII]
• H04W 12/03 - Protecting confidentiality, e.g. by encryption
• H04W 12/10 - Integrity
• H04W 12/12 - Detection or prevention of fraud
• H04W 12/128 - Anti-malware arrangements, e.g. protection against SMS fraud or mobile malware

Abstract

A system for detecting whether a device seeking communication with a server is a returning device that previously communicated with the server includes a database that stores groups of device attributes based on observable device characteristics and unique identifiers. The database is generally not accessible to the devices. Each attribute group and the associated device identifier (DID) can uniquely identify a particular device, and the associated DID is generally not derivable from the attributes. The database may satisfy a uniqueness property so that each attribute value in the database may also uniquely identify a device.

IPC Classes  ?

• H04L 67/50 - Network services
• H04L 9/40 - Network security protocols
• H04L 67/303 - Terminal profiles

Abstract

A system for establishing a trusted path for secure communication between client devices and server devices, such as between an account holder and a financial institution, can provide the core security attributes of confidentiality (of the parties), integrity (of the information), anti-replay (protection against replay fraud) and/or anti-tampering (protection against unauthorized changes to information being exchanged and/or modules that generate and communicate such information). A messaging layer implementation in favor of a transport layer implementation can provide a trusted path. This infrastructure features secure cryptographic key storage, and implementation of a trusted path built using the cryptographic infrastructure. The trusted path protects against unauthorized information disclosure, modification, or replays. These services can effectively protect against Man-in-the-Middle, Man-in-the-Application, and other attacks.

IPC Classes  ?

• H04L 29/06 - Communication control; Communication processing characterised by a protocol
• G06F 21/53 - Monitoring users, programs or devices to maintain the integrity of platforms, e.g. of processors, firmware or operating systems during program execution, e.g. stack integrity, buffer overflow or preventing unwanted data erasure by executing in a restricted environment, e.g. sandbox or secure virtual machine
• G06F 21/56 - Computer malware detection or handling, e.g. anti-virus arrangements
• G06F 21/57 - Certifying or maintaining trusted computer platforms, e.g. secure boots or power-downs, version controls, system software checks, secure updates or assessing vulnerabilities
• G06F 21/60 - Protecting data
• G06F 21/64 - Protecting data integrity, e.g. using checksums, certificates or signatures
• G06Q 20/38 - Payment protocolsDetails thereof
• H04L 9/32 - Arrangements for secret or secure communicationsNetwork security protocols including means for verifying the identity or authority of a user of the system
• H04L 9/40 - Network security protocols
• H04W 12/03 - Protecting confidentiality, e.g. by encryption
• H04W 12/10 - Integrity
• H04W 12/12 - Detection or prevention of fraud
• H04W 12/128 - Anti-malware arrangements, e.g. protection against SMS fraud or mobile malware
• H04W 12/02 - Protecting privacy or anonymity, e.g. protecting personally identifiable information [PII]

Abstract

A system for establishing a trusted path for secure communication between client devices and server devices, such as between an account holder and a financial institution, can provide the core security attributes of confidentiality (of the parties), integrity (of the information), anti-replay (protection against replay fraud) and/or anti-tampering (protection against unauthorized changes to information being exchanged and/or modules that generate and communicate such information). A messaging layer implementation in favor of a transport layer implementation can provide a trusted path. This infrastructure features secure cryptographic key storage, and implementation of a trusted path built using the cryptographic infrastructure. The trusted path protects against unauthorized information disclosure, modification, or replays. These services can effectively protect against Man-in-the-Middle, Man-in-the-Application, and other attacks.

IPC Classes  ?

• H04L 29/06 - Communication control; Communication processing characterised by a protocol
• H04L 9/32 - Arrangements for secret or secure communicationsNetwork security protocols including means for verifying the identity or authority of a user of the system
• G06Q 20/38 - Payment protocolsDetails thereof
• G06F 21/57 - Certifying or maintaining trusted computer platforms, e.g. secure boots or power-downs, version controls, system software checks, secure updates or assessing vulnerabilities
• G06F 21/60 - Protecting data
• G06F 21/64 - Protecting data integrity, e.g. using checksums, certificates or signatures
• H04L 9/40 - Network security protocols
• H04W 12/10 - Integrity
• H04W 12/03 - Protecting confidentiality, e.g. by encryption
• H04W 12/128 - Anti-malware arrangements, e.g. protection against SMS fraud or mobile malware
• G06F 21/53 - Monitoring users, programs or devices to maintain the integrity of platforms, e.g. of processors, firmware or operating systems during program execution, e.g. stack integrity, buffer overflow or preventing unwanted data erasure by executing in a restricted environment, e.g. sandbox or secure virtual machine
• G06F 21/56 - Computer malware detection or handling, e.g. anti-virus arrangements
• H04W 12/12 - Detection or prevention of fraud
• H04W 12/02 - Protecting privacy or anonymity, e.g. protecting personally identifiable information [PII]

Abstract

A system for detecting whether a device seeking communication with a server is a returning device that previously communicated with the server includes a database that stores groups of device attributes based on observable device characteristics and unique identifiers. The database is generally not accessible to the devices. Each attribute group and the associated device identifier (DID) can uniquely identify a particular device, and the associated DID is generally not derivable from the attributes. The database may satisfy a uniqueness property so that each attribute value in the database may also uniquely identify a device.

IPC Classes  ?

• H04L 9/40 - Network security protocols
• H04L 67/50 - Network services
• H04L 67/303 - Terminal profiles

Abstract

A system of classifying devices and/or app instances a new or returning divides attributes generated from observations received from an uncharacterized device/software app into base-fingerprint attributes and predictor attributes, where the two kinds of attributes have different longevities. Predictor attribute tuples from attribute tuples having the same base fingerprint as the base fingerprint corresponding to the uncharacterized device/app, and the predictor attribute tuple corresponding to the uncharacterized device/app are analyzed using a machine learned predictor function to obtain a final fingerprint. Machine learning techniques such as logistic regression, support vector machine, and artificial neural network can provide a predictor function that can decrease the conflict rate of the final fingerprint and, hence, the utility thereof, without significantly affecting the accuracy of classification.

IPC Classes  ?

• G06N 20/10 - Machine learning using kernel methods, e.g. support vector machines [SVM]
• G06N 20/00 - Machine learning

Abstract

A system for establishing a trusted path for secure communication between client devices and server devices, such as between an account holder and a financial institution, can provide the core security attributes of confidentiality (of the parties), integrity (of the information), anti-replay (protection against replay fraud) and/or anti-tampering (protection against unauthorized changes to information being exchanged and/or modules that generate and communicate such information). A messaging layer implementation in favor of a transport layer implementation can provide a trusted path. This infrastructure features secure cryptographic key storage, and implementation of a trusted path built using the cryptographic infrastructure. The trusted path protects against unauthorized information disclosure, modification, or replays. These services can effectively protect against Man-in-the-Middle, Man-in-the-Application, and other attacks.

IPC Classes  ?

• H04L 29/06 - Communication control; Communication processing characterised by a protocol
• H04L 9/32 - Arrangements for secret or secure communicationsNetwork security protocols including means for verifying the identity or authority of a user of the system
• G06Q 20/38 - Payment protocolsDetails thereof
• G06F 21/57 - Certifying or maintaining trusted computer platforms, e.g. secure boots or power-downs, version controls, system software checks, secure updates or assessing vulnerabilities
• G06F 21/60 - Protecting data
• G06F 21/64 - Protecting data integrity, e.g. using checksums, certificates or signatures
• H04W 12/10 - Integrity
• H04W 12/03 - Protecting confidentiality, e.g. by encryption
• H04W 12/128 - Anti-malware arrangements, e.g. protection against SMS fraud or mobile malware
• G06F 21/53 - Monitoring users, programs or devices to maintain the integrity of platforms, e.g. of processors, firmware or operating systems during program execution, e.g. stack integrity, buffer overflow or preventing unwanted data erasure by executing in a restricted environment, e.g. sandbox or secure virtual machine
• G06F 21/56 - Computer malware detection or handling, e.g. anti-virus arrangements
• H04W 12/12 - Detection or prevention of fraud
• H04W 12/02 - Protecting privacy or anonymity, e.g. protecting personally identifiable information [PII]

Abstract

A system for detecting whether a device seeking communication with a server is a returning device that previously communicated with the server includes a database that stores groups of device attributes based on observable device characteristics and unique identifiers. The database is generally not accessible to the devices. Each attribute group and the associated device identifier (DID) can uniquely identify a particular device, and the associated DID is generally not derivable from the attributes. The database may satisfy a uniqueness property so that each attribute value in the database may also uniquely identify a device.

IPC Classes  ?

• H04L 29/08 - Transmission control procedure, e.g. data link level control procedure
• H04L 29/06 - Communication control; Communication processing characterised by a protocol

Abstract

A system for establishing a trusted path for secure communication between client devices and server devices, such as between an account holder and a financial institution, can provide the core security attributes of confidentiality (of the parties), integrity (of the information), anti-replay (protection against replay fraud) and/or anti-tampering (protection against unauthorized changes to information being exchanged and/or modules that generate and communicate such information). A messaging layer implementation in favor of a transport layer implementation can provide a trusted path. This infrastructure features secure cryptographic key storage, and implementation of a trusted path built using the cryptographic infrastructure. The trusted path protects against unauthorized information disclosure, modification, or replays. These services can effectively protect against Man-in-the-Middle, Man-in-the-Application, and other attacks.

IPC Classes  ?

• H04L 29/06 - Communication control; Communication processing characterised by a protocol
• H04L 9/32 - Arrangements for secret or secure communicationsNetwork security protocols including means for verifying the identity or authority of a user of the system
• G06Q 20/38 - Payment protocolsDetails thereof
• G06F 21/57 - Certifying or maintaining trusted computer platforms, e.g. secure boots or power-downs, version controls, system software checks, secure updates or assessing vulnerabilities
• G06F 21/60 - Protecting data
• G06F 21/64 - Protecting data integrity, e.g. using checksums, certificates or signatures
• H04W 12/10 - Integrity
• H04W 12/12 - Detection or prevention of fraud
• H04W 12/00 - Security arrangementsAuthenticationProtecting privacy or anonymity
• G06F 21/53 - Monitoring users, programs or devices to maintain the integrity of platforms, e.g. of processors, firmware or operating systems during program execution, e.g. stack integrity, buffer overflow or preventing unwanted data erasure by executing in a restricted environment, e.g. sandbox or secure virtual machine
• G06F 21/56 - Computer malware detection or handling, e.g. anti-virus arrangements
• H04W 12/02 - Protecting privacy or anonymity, e.g. protecting personally identifiable information [PII]

Abstract

A system of classifying devices and/or app instances a new or returning divides attributes generated from observations received from an uncharacterized device/software app into base-fingerprint attributes and predictor attributes, where the two kinds of attributes have different longevities. Predictor attribute tuples from attribute tuples having the same base fingerprint as the base fingerprint corresponding to the uncharacterized device/app, and the predictor attribute tuple corresponding to the uncharacterized device/app are analyzed using a machine learned predictor function to obtain a final fingerprint. Machine learning techniques such as logistic regression, support vector machine, and artificial neural network can provide a predictor function that can decrease the conflict rate of the final fingerprint and, hence, the utility thereof, without significantly affecting the accuracy of classification.

IPC Classes  ?

• G06N 20/00 - Machine learning
• G06N 20/10 - Machine learning using kernel methods, e.g. support vector machines [SVM]
• H04W 12/79 - Radio fingerprint
• H04L 29/06 - Communication control; Communication processing characterised by a protocol
• H04W 12/03 - Protecting confidentiality, e.g. by encryption
• H04W 12/065 - Continuous authentication
• H04W 12/084 - Access security using delegated authorisation, e.g. open authorisation [OAuth] protocol
• H04W 12/122 - Counter-measures against attacksProtection against rogue devices

Abstract

A system of classifying devices and/or app instances a new or returning divides attributes generated from observations received from an uncharacterized device/software app into base-fingerprint attributes and predictor attributes, where the two kinds of attributes have different longevities. Predictor attribute tuples from attribute tuples having the same base fingerprint as the base fingerprint corresponding to the uncharacterized device/app, and the predictor attribute tuple corresponding to the uncharacterized device/app are analyzed using a machine learned predictor function to obtain a final fingerprint. Machine learning techniques such as logistic regression, support vector machine, and artificial neural network can provide a predictor function that can decrease the conflict rate of the final fingerprint and, hence, the utility thereof, without significantly affecting the accuracy of classification.

IPC Classes  ?

• G06N 20/10 - Machine learning using kernel methods, e.g. support vector machines [SVM]
• G06N 20/00 - Machine learning

Abstract

A system for detecting whether a device seeking communication with a server is a returning device that previously communicated with the server includes a database that stores groups of device attributes based on observable device characteristics and unique identifiers. The database is generally not accessible to the devices. Each attribute group and the associated device identifier (DID) can uniquely identify a particular device, and the associated DID is generally not derivable from the attributes. The database may satisfy a uniqueness property so that each attribute value in the database may also uniquely identify a device.

IPC Classes  ?

• H04L 29/06 - Communication control; Communication processing characterised by a protocol
• H04L 29/08 - Transmission control procedure, e.g. data link level control procedure

Abstract

A system facilitates secure communication between an authorized user device and two or more servers via two or more channels that are associated with the respective servers. For each communication channel, the system receives a device identifier for the authorized user device and links the device identifiers together via another identifier, thereby allowing the system to recognize that the different device identifiers identify the same authorized user device. The system can identify an unauthorized device masquerading as the authorized user device by determining that a communication from the unauthorized device does not include another identifier linking the two or more device identifiers and/or by determining that a device identifier computed during the registration process is different from a linked identifier.

IPC Classes  ?

• H04L 29/06 - Communication control; Communication processing characterised by a protocol
• H04W 12/08 - Access security
• H04W 12/06 - Authentication
• H04W 12/12 - Detection or prevention of fraud
• H04L 29/08 - Transmission control procedure, e.g. data link level control procedure
• H04W 88/02 - Terminal devices

Abstract

A system for establishing a trusted path for secure communication between client devices and server devices, such as between an account holder and a financial institution, can provide the core security attributes of confidentiality (of the parties), integrity (of the information), anti-replay (protection against replay fraud) and/or anti-tampering (protection against unauthorized changes to information being exchanged and/or modules that generate and communicate such information). A messaging layer implementation in favor of a transport layer implementation can provide a trusted path. This infrastructure features secure cryptographic key storage, and implementation of a trusted path built using the cryptographic infrastructure. The trusted path protects against unauthorized information disclosure, modification, or replays. These services can effectively protect against Man-in-the-Middle, Man-in-the-Application, and other attacks.

IPC Classes  ?

• H04L 29/06 - Communication control; Communication processing characterised by a protocol
• H04L 9/32 - Arrangements for secret or secure communicationsNetwork security protocols including means for verifying the identity or authority of a user of the system
• G06F 21/53 - Monitoring users, programs or devices to maintain the integrity of platforms, e.g. of processors, firmware or operating systems during program execution, e.g. stack integrity, buffer overflow or preventing unwanted data erasure by executing in a restricted environment, e.g. sandbox or secure virtual machine
• G06F 21/56 - Computer malware detection or handling, e.g. anti-virus arrangements
• H04W 12/12 - Detection or prevention of fraud
• G06Q 20/38 - Payment protocolsDetails thereof
• G06F 21/57 - Certifying or maintaining trusted computer platforms, e.g. secure boots or power-downs, version controls, system software checks, secure updates or assessing vulnerabilities
• G06F 21/60 - Protecting data
• G06F 21/64 - Protecting data integrity, e.g. using checksums, certificates or signatures
• H04W 12/10 - Integrity
• H04W 12/02 - Protecting privacy or anonymity, e.g. protecting personally identifiable information [PII]

Goods & Services

Providing temporary use of online non-downloadable software for use in credit card and identity fraud detection and prevention, and post-fraud detection, investigation, resolution, and support, namely, for use in automating credit card transaction screening, credit card transaction filtering and prioritization, credit card transaction review, credit card transaction resolution, credit card chargeback processing, and credit card fraud analyst monitoring; application service provider, namely, providing, hosting, managing, developing, and maintaining applications, software, websites, and databases in the fields of credit card and identity fraud detection and prevention Credit card and identity fraud detection services, namely, fraud detection services in the field of credit cards for on-line purchasing, telephone orders, and card-not present (CNP) transactions; credit card and identify fraud prevention services, namely, providing authentication of personal identification information; post-fraud detection services in the field of credit cards for on-line purchasing, telephone orders, and card-not present (CNP) transactions; providing advice and consultation in the field of credit card and identity fraud detection; identification verification services, namely, providing authentication of personal identification information

Goods & Services

Providing temporary use of online non-downloadable software for use in credit card and identity fraud detection and prevention, and post-fraud detection, investigation, resolution, and support, namely, for use in automating credit card transaction screening, credit card transaction filtering and prioritization, credit card transaction review, credit card transaction resolution, credit card chargeback processing, and credit card fraud analyst monitoring; application service provider, namely, providing, hosting, managing, developing, and maintaining applications, software, websites, and databases in the fields of credit card and identity fraud detection and prevention Credit card and identity fraud detection services, namely, fraud detection services in the field of credit cards for on-line purchasing, telephone orders, and card-not present (CNP) transactions; credit card and identify fraud prevention services, namely, providing authentication of personal identification information; post-fraud detection services in the field of credit cards for on-line purchasing, telephone orders, and card-not present (CNP) transactions; providing advice and consultation in the field of credit card and identity fraud detection; identification verification services, namely, providing authentication of personal identification information