US 12,764,151 B2Granted

Systems and methods for generating embeddings of network event data to detect fraudulent behavior in networked environments

AssigneeStripe, Inc.

InventorsYashu Lingaraju, Stathis Vafeias, Chiranth Hegde

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Abstract

Presented herein are systems and methods of generating embeddings for network events to detect fraudulent activities in networked environments. A service may receive a request to execute a first network operation in a network environment. The service may identify an event dataset associated with the first network operation to be executed. The service may apply the first event dataset to a machine learning (ML) model comprising a plurality of weights. The ML model may be established using training data comprising (i) a first sample event dataset associated with a second network operation and (ii) a second sample event dataset corresponding to a modification of a portion of the first sample event dataset. The service may generate, based on applying the event dataset of the first network operation to the ML model, a plurality of embeddings indicative of fraudulence of the first network operation.

Claims20

Dependent claims

A method of generating embeddings for network events to detect fraudulent activities in networked environments, comprising:

receiving, by one or more processors, a request to execute a first network operation in a network environment;
identifying, by the one or more processors, an event dataset associated with the first network operation to be executed;
applying, by the one or more processors, the first event dataset to a first machine learning (ML) model comprising a plurality of weights, wherein the first ML model is established by:
identifying training data comprising (i) a first sample event dataset associated with a second network operation and (ii) a second sample event dataset corresponding to a modification of a portion of the first sample event dataset;
generating, by applying to the first ML model, (i) a first plurality of embeddings using the first sample event dataset and (ii) a second plurality of embeddings using the second sample event dataset;
comparing the first plurality of embeddings with the second plurality of embeddings to generate a similarity metric;
and
updating at least one of the plurality of weights of the first ML model based on the similarity metric;
generating, by the one or more processors, based on applying the event dataset of the first network operation to the ML model, a plurality of embeddings indicative of fraudulence of the first network operation;
and
sending, by the one or more processors, the plurality of embeddings to a second ML model to determine a likelihood of fraudulence in the first network operation.

8 more elements

A method of training machine learning (ML) models to generate embeddings for network events to detect fraudulent activities in networked environments, comprising:

identifying, by one or more processors, training data comprising (i) a first sample event dataset associated with a second network operation and (ii) a second sample event dataset corresponding to a modification of a portion of the first sample event dataset;
generating, by the one or more processors, based on applying to a first ML model comprising a plurality of weights, (i) a first plurality of embeddings using the first sample event dataset and (ii) a second plurality of embeddings using the second sample event dataset;
comparing, by the one or more processors, the first plurality of embeddings with the second plurality of embeddings to generate a similarity metric;
and
updating, by the one or more processors, at least one of the plurality of weights of the first ML model based on the similarity metric;
and
storing, by the one or more processors, the plurality of weights of the first ML model to generate a second plurality of embeddings for a second network operation to be provided to a second ML model to determine a likelihood of fraudulence in the second network operation.

4 more elements

A system for generating embeddings for network events to detect fraudulent activities in networked environments, comprising:

one or more processors coupled with memory, configured to:
receive a request to execute a first network operation in a network environment;
identify an event dataset associated with the first network operation to be executed;
apply the first event dataset to a first machine learning (ML) model comprising a plurality of weights, wherein the first ML model is established by:
identifying training data comprising (i) a first sample event dataset associated with a second network operation and (ii) a second sample event dataset corresponding to a modification of a portion of the first sample event dataset;
generating, by applying to the first ML model, (i) a first plurality of embeddings using the first sample event dataset and (ii) a second plurality of embeddings using the second sample event dataset;
comparing the first plurality of embeddings with the second plurality of embeddings to generate a similarity metric;
and
updating at least one of the plurality of weights of the first ML model based on the similarity metric;
generate, based on applying the event dataset of the first network operation to the ML model, a plurality of embeddings indicative of fraudulence of the first network operation;
and
send the plurality of embeddings to a second ML model to determine a likelihood of fraudulence in the first network operation.

9 more elements

Dependencies are inferred from the claim text.