Abstract: Disclosed herein are systems and methods for monitoring network traffic to identify cyberattacks. An example method includes obtaining a first plurality of data packets transmitted over a network during at least one period of time; determining a plurality of feature values based on the plurality of data packets, each feature corresponding to execution of a protocol by the first system; generating an embedding based on the plurality of feature values and an encoder, the encoder configured to generate embeddings based on pluralities of feature values, the embeddings corresponding to a period of time; and determining that at least one data packet of the plurality of data packets is involved in a cyberattack based on the embedding. The method can include causing an execution of a remedial protocol in association with the first system.

Abstract: Some embodiments involve systems and methods for facilitating authentication for an entity system to use a partner system using pooled identifiers (e.g., without requiring the entity system to first register with the partner system). In some embodiments, based on (i) a request, from a first entity system, indicating a first partner system to process at least a part of an operation and (ii) an indication that the first entity system is not registered for use of the first partner system, a platform system may link a first authentication identifier of the first identifier set with the first entity system. The platform system may process the operation using the first authentication identifier linked by the platform system with the first entity system, and the platform system may use platform authentication data established with the first partner system to authenticate the platform system to the first partner system for performing the operation.

Abstract: A server computer system for detecting a fraudulent electronic transaction may be configured to receive a transaction associated with a merchant. The system processes the transaction with a first machine learning model that is trained specific to the merchant, to obtain a first fraud detection, and processes the transaction with a second machine learning model that is trained based on a plurality of merchants, to obtain a second fraud detection. The system detects fraud associated with the transaction based on the first fraud detection and the second fraud detection. In response to detecting the fraud that is associated with the transaction, the system blocks the transaction.

Abstract: The subject technology generates training data based at least in part on reporting data from a card network. The subject technology trains a machine learning model based on the training data. The subject technology determines, using the machine learning model, a fee description for a transaction. The subject technology determines a variable rate and a fixed amount based at least in part on the fee description. The subject technology determines at least one cost based at least in part the variable rate and the fixed amount for each transaction from a set of transactions. The subject technology evaluates at least one result against second reporting data. The subject technology provides, for display, the at least one cost and the at least one result on a display of a client device.

Abstract: Presented herein are systems and methods of controlling execution of operations via network operation protocols to delegate across services distributed in networked environments. A first service may receive, from a second service, a request to delegate including a digital address of a first digital key manager of a user to which tokens on a distributed ledger is assigned. The first service may construct, responsive to the request to delegate, a network operation protocol comprising: (i) a first component to control transfer of an assignment of tokens from the first digital key manager to a second digital key manager of the first service on behalf of a computing system, (ii) a second component to set constraints on execution of ledger operations on the distributed ledger for transferring the assignment, and (iii) a third component to execute the ledger operations to transfer the assignment.

Abstract: Embodiments may include receiving a request including transaction data to evaluate a transaction as part of a charge path that processes at least a portion of the transaction; processing, by a set of blocking machine learning models, a portion of the transaction data to generate a set of blocking scores for the transaction; retrieving, from a memory, a set of card testing attack (CTA) scores associated with the transaction, the set of CTA scores indicative of an occurrence of a card testing attack, and the set of CTA scores retrieved based on the transaction data; adjusting a set of default blocking score thresholds based on the set of CTA scores to generate a set of adjusted blocking score thresholds; and determining to allow the transaction based on comparison of the set of blocking scores with the set of adjusted blocking score thresholds.

Abstract: Disclosed herein are systems and methods for identifying and addressing malicious network traffic based on network traffic lane activity. An example method includes receiving data associated with a plurality of messages transmitted via a network, determining a first network traffic lane associated with a first set of messages of the plurality of messages and a second network traffic lane associated with a second set of messages of the plurality of messages, and determining that the first set of messages is associated with an increased probability of being involved in a distributed denial of service (DDoS) attack. In examples, the method includes causing at least one remedial action to be performed for at least a portion of messages associated with the first network traffic lane. Non-transitory machine-readable mediums are also disclosed.

Abstract: Aspects of the present disclosure relate to systems and methods for managing access to data in a cloud-based software platform. A first cloud-based software application generates first data associated with a user account on the cloud-based software platform. A second cloud-based software application may also be connected to the user account and request access to the first data generated by the first application. The user account sets a collection of access permissions on the first data, where the second cloud-based software application is granted access to the first data subject to the collection of access permissions.

Abstract: In some embodiments, a computer system is described. In some embodiments, the computer system generates a suggested collection of applications based at least in part on a first application selected by a client device, receives a second selection of a second application from the suggested collection of applications, integrates the first application and second application with a client application on the host platform, receives a request to perform an operation on the client application, selects at least one of the first application or the second application of the client application to perform the operation based on a determination that the at least one of the first application or the second application have access to the database, sends a request to perform the operation to the selected first or second application, and receives a set of results based on performance of the operation.

Abstract: Methods and systems for disambiguating a user from a plurality of users within a computing infrastructure when the plurality of users utilize a common identifier are described. Disambiguation may occur through the user of subsequent verification of the identity of the user which generates an authentication token to be passed to the computing infrastructure during authentication requests using the common identifier.

Abstract: This disclosure provides techniques for providing a context-aware tool that, in some examples, is globally available throughout a platform.

Abstract: A method and apparatus for efficient and progressive fraud detection are described. Transactions are received by a server computer system and include attributes and transaction data. To determine whether a transaction is fraudulent, and thus should be rejected, a progressive fraud determination process is performed by the server computer system. The progressive fraud determination process includes performing a first set of one or more transaction fraud determinations based on a first set of features determined for the transaction. When the first set of one or more transaction fraud determinations identifies the transaction as fraudulent or legitimate, the progressive fraud determination process is terminated based on this decision, and before performing additional successive fraud determination processes. By exiting the progressive fraud determination process early, substantial processing, memory and bandwidth savings can be realized without sacrificing fraud detection accuracy.

Abstract: Disclosed herein are systems and methods for efficiently analyzing data using intelligent methods and systems. In an embodiment, a server identifies an aggregated data associated with an electronic payment system comprising data corresponding to a set of attributes for a set of transactions associated with the electronic payment system, each attribute having one or more corresponding values; generates a set of data slices from the aggregated data, the set of data slices including a first data slice containing data corresponding to a first value of a plurality of values associated with a first attribute of the set of attributes; executes a computer model that receives an input of the aggregated data and an input of at least the first data slice and outputs an identification of an anomaly associated with the electronic payment system.

Abstract: A method and apparatus for fraud detection during transactions using identity graphs are described. A method includes receiving, at a commerce platform system, a transaction from a user having initial transaction attributes and transaction data. The method also includes determining, by the commerce platform system, an identity associated with the user associated with additional transaction attributes not received with the transaction. Furthermore, the method includes accessing a feature set associated with the initial transaction attributes and the additional transaction attributes that includes machine learning (ML) model features for detecting transaction fraud.

Abstract: Presented herein are systems and methods of evaluating network operations associated with computing systems. A server may receive, from a computing system, an electronic request to execute a first network operation using a plurality of attributes provided by an end user device to the computing system. The first network operation may be initiated via the end user device. The server may retrieve (i) a digital fingerprint associated with an identity of the computing system and (ii) a plurality of network operation metrics associated with the computing system. The server may execute, using the digital fingerprint and the plurality of network operation metrics, a machine learning (ML) model to generate a likelihood of fraud caused by the computing system. The server may, in response to the likelihood of fraud satisfying a threshold, execute a second network operation using the plurality of attributes, instead of executing the first network operation.

Abstract: A method for de-duplicating data records may include generating a nodal data structure comprising a set of nodes, in response to receiving a first set of transaction data corresponding to aggregated data associated with a first set of transactions, associating a first subset of nodes with the first set of transaction data, responsive to receiving a second set of transaction data corresponding to aggregated data associated with a second set of transactions and at least a subset of the first set of transactions, associating a second subset of nodes to the first set of transaction data, combining the first subset and the second subset, executing an analytical protocol to identify data associated with the transaction, disassociating the transaction with the first subset, and associating the data associated with the transaction identified via the analytical protocol with the first subset and the second subset.

Abstract: A machine learning framework and method for using the same are described.

Abstract: A method and apparatus for machine learning model (MLM) based fraud detection of transactions are described. The method may include determining a first block rate of a first MLM that scores transactions, wherein the fraud detection system blocks a transaction when a score generated for the transaction by the first MLM does not satisfy a first threshold value. The method may also include accessing a second MLM to be used by the fraud detection system for blocking future transactions. Furthermore, the method may include determining a second threshold value using a set of prior transactions input into the second MLM that results in a second block rate of the second MLM within a predetermined margin of the first threshold. The method may also include deploying the second MLM using the second threshold value for performing fraud detection of at least one new transaction.

Abstract: A method and system for performing a movement of a service within a distributed system. In one embodiment, the method comprises determining one or more locations where the service is to be moved. The method further comprises for each of the one or more locations, computing a metric including a cross-cell cost (C-cost) or a data locality-cost (DL-cost) of the software service if a movement of the software service to a location was to occur, prior to performing the movement of the software service; and modelling a latency and availability of the system due to the movement of the software service to the location based on the metric. The method further comprises performing the movement of the service based on the modelled latency and availability of the system.

Abstract: A method comprising: creating, on a server, an object corresponding to the transaction; tracking state of the object as the object transitions between a plurality of states, the object being in one state while waiting for a transfer for the transaction; and automatically associating with the object by matching a reference code received with the transfer.