Visa International Service Association

Ajit Gaddam

Selim Aissi

Gyan Prakash

Pushkar Joglekar

Taeho Kgil

Ara Jermakyan

Christian Aabye

Glenn Leon Powell

Sekhar Nagasundaram

Rasta A Mansour

Heng Tang

Vlad Spichek

Methods and systems for inducing model shift in a malicious computer's machine learning model is disclosed. A data processor can determine that a malicious computer uses a machine learning model with a boundary function to determine outcomes. The data processor can then generate transition data intended to shift the boundary function and then provide the transition data to the malicious computer. The data processor can repeat generating and providing the transition data, thereby causing the boundary function to shift over time.

Proactive defense of untrustworthy machine learning system

Methods and systems for analyzing request to access resources and determining a resource access policy are provided. The resource access system can train, store, evaluate, and deploy machine learning models that learn to output a trust score associated with a resource access request, the trust score relating to the request's legitimacy. A resource access system can receive a request for a resource from a requesting entity. The resource access system can determine an entity profile using request data in the request. The resource access system can request data from the request to determine whether the request is normal or anomalous. Using a policy engine, the resource access system can determine a resource access policy, such as allowing or denying access to the resource.

Real-time entity anomaly detection

Disclosed are a method and system for generating a behavior profile for a new entity. The method is performed in response to and immediately after a new entity is added to a fraud detection system. The method includes generating a doppelganger behavior profile for the new entity based on feature data of the new entity and pre-existing entities. Generating the profile includes determining a similarity score for each pre-existing entity, ranking the plurality of pre-existing entities based on the score, selecting pre-existing entities based on the ranking, and combining behavior profiles of the preexisting entities to produce the profile. The method also includes assigning the profile to the new entity and monitoring the new entity. The method further includes detecting fraud, an anomaly, and/or malicious intent based on the profile, and automatically suspending an aspect of the new entity in response to the detection.

Systems and methods for generating behavior profiles for new entities

Described herein is a platform for providing a resource catalog local to an appliance device, such that the appliance device is able to generate a transaction related to one or more resources within the resource catalog. In some embodiments, the appliance device may receive resource information within streaming data. For example, the appliance device may receive an indication that a portion of streaming media data is related to a resource. Upon determining that a portion of streaming data is related to a resource, the appliance device may be configured to store the portion of data in relation to the identified resource. A user may retrieve and/or execute the portion of streaming data at a later date. In some embodiments, the user may request that a transaction be conducted with respect to the resource and a corresponding transaction request may be generated by the appliance device.

Platform for appliance based resource catalog

Methods and systems for evaluating microservice system level activities including system calls and commands, and generating security policies for microservices are disclosed. A microservice agent, operating on a microservice host, can collect system level activity data corresponding to a plurality of microservices operating on the microservice host. The microservice agent can transmit the system level activity data to a microservice evaluator that can use the system level activity data to train machine learning models to identify normal and abnormal microservice system level activities. The normal and abnormal system level activities can be used to generate security policies that can be applied to the microservices. Microservices that perform abnormal system level activities or system level activities that violate security policies can be paused or terminated.

Microservice adaptive security hardening

A method and system for protecting access to remote systems, such as resource databases containing sensitive resources, such as cryptographic keys or personally identifying information, is disclosed. A server can receive a plurality of access requests from a requesting entity. The server can identify an entity profile corresponding to the access requests, as well as a machine learning model corresponding to the entity profile. The access requests can be used to form access sequences, which can be evaluated by the machine learning model. The machine learning model returns an anomaly score that can be compared to a threshold. If the anomaly score exceeds the threshold, the server can prevent further access to the remote system, for example, by revoking a credential associated with the requesting entity.

Systems and methods to secure API platforms

Described herein is a platform and method for determining a confidence level associated with a transaction that utilizes dynamic data. In some embodiments, the confidence level is determined based on location data received in relation to the transaction. For example, some embodiments are directed to storing first location information collected from a mobile device provided in a request for the dynamic data, receiving second location information related to a transaction conducted using the dynamic data, and comparing the two with respect to the amount of time that has elapsed between collection of each to determine a confidence level associated with a likelihood that the transaction is authentic.

Location verification during dynamic data transactions

A method and system for generating a behavior profile for a new entity to be added to a behavior-monitored system include receiving feature data of a new entity and feature data of a plurality of pre-existing entities from at least one data source. The plurality of pre-existing entities are associated with a plurality of pre-existing behavior profiles. A doppelganger behavior profile is determined for the new entity based at least partially on the feature data of the new entity and the feature data of the plurality of pre-existing entities, and the doppelganger behavior profile is assigned to the new entity. A target action is implemented with respect to the new entity based at least partially on the doppelganger behavior profile assigned to the new entity.

Embodiments of the invention provision multiple payment tokens on a communication device. The communication device may be provisioned with multiple limited use keys (LUK), each LUK being associated with a specific type of transaction. When the communication device is used for a transaction, the communication device automatically determines a type of the transaction and selects an appropriate LUK based on the determined transaction type. The selected LUK may be used to create a cryptogram, which can be used to verify the transaction.

Transaction risk based token

Techniques described herein include a platform and process for provisioning user information onto a machine-to-machine device in order to enable the machine-to-machine device to conduct transactions utilizing the user information. In some embodiments, a user device is used to relay information between a machine-to-machine device and a provisioning service provider computer. In some embodiments, a machine-to-machine device is connected to the provisioning service provider computer via a network connection. Upon receiving a request to provision the machine-to-machine device, the service provider computer may identify the device from a device identifier. The service provider computer may generate an access credential or token for the machine-to-machine device. The access credential, token, and/or one or more policies may be provisioned onto the machine-to-machine device.

Provisioning platform for machine-to-machine devices

Disclosed are methods for authorizing a transaction, including receiving a policy message, the policy message including a policy ruleset for determining whether a transaction is authorized and biometric parameters for a machine learning algorithm for authenticating an identity of a user involved in a transaction, receiving biometric measurement data associated with a biometric measurement of a user involved in the transaction, calculating an authentication score based on the biometric measurement data using the machine learning algorithm, wherein the authentication score includes an indication of whether an identity of the user is authenticated based on the biometric measurement data, determining whether the transaction satisfies the policy ruleset for determining authorization of the transaction, and transmitting a decision message, wherein the decision message includes an indication of whether the transaction satisfies the policy ruleset. Systems and computer program products are also disclosed.

Systems, methods, and computer program products for authorizing a transaction

Methods and systems for evaluating metrics (e.g., quality of service metrics) corresponding to a monitored computer, detecting metric anomalies, and issuing alerts, are disclosed. A metrics collecting agent, operating on a monitored computer, collects metrics corresponding to the monitored computer and/or one or more monitored services. These metrics are transmitted to a monitoring server that dynamically determines metric thresholds corresponding to normal metrics and anomalous metrics. Using these metric thresholds, along with a machine learning model, the monitoring server can determine whether one or more metrics are anomalous, automatically issue alerts to security and operations teams, and/or transmit a control instruction to the monitored computer in order to fix the issue causing the anomalous metrics.

Growing community of inventors

Sunnyvale, CA, United States of America

Ajit Gaddam