Intuit, Inc.

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Itsik Yizhak Mantin

Yaron Sheffer

Laetitia Kahn

A computer-implemented method includes receiving data comprising a plurality of application programming interface (API) requests from a plurality of client devices. The method includes generating a plurality of API sessions based on the data, wherein each of the API sessions is associated with a corresponding client device of the plurality of client devices and includes a sequence of API requests originating from the corresponding client device. The method includes comparing each of the plurality of API sessions to one or more of a plurality of different patterns indicative of permissible API sessions determined based on training data. The method includes determining one or more API sessions of the plurality of API sessions generated based on the data are abnormal based, at least in part, on the comparing. Finally, the method includes performing one or more actions based on determining the one or more API sessions are abnormal.

Detection of abnormal application programming interface (API) sessions including a sequence of API requests

A computer-implemented method includes receiving training data that includes a plurality of API requests from a plurality of client devices. The method includes generating a plurality of permissible API sessions based on the training data. Each of the permissible API sessions is associated with a corresponding client device of the plurality of client devices and includes a sequence of API requests originating from the corresponding client device. The method includes applying a sequence embedding technique to the plurality of permissible API sessions to generate a plurality of embeddings and applying a dimensionality reduction technique to the plurality of embedding to generate a plurality of compact embeddings. The method includes storing each of the compact embeddings in a space partitioning data structure at storage locations within the space partitioning data structure that are determined based on similarities between the compact embeddings.

Detection of abnormal application programming interface (API) sessions including a sequence of API requests using space partitioning data structures

A computer-implemented method includes receiving training data including a plurality of API requests from a plurality of client devices. The method includes generating a plurality of permissible API sessions based on the training data. The method includes applying a sequence embedding technique to the plurality of permissible API sessions to generate a plurality of embeddings. The method includes applying a dimensionality reduction technique to the plurality of embeddings to generate a plurality of compact embeddings. The method includes applying a clustering technique to the plurality of compact embeddings to determine a plurality of different clusters of the compact embeddings. The method includes generating a plurality of patterns based on the plurality of different clusters. Each of the plurality of patterns is descriptive of permissible API sessions associated with a corresponding cluster of the plurality of different clusters.

The present disclosure provides techniques for recommending vendors using machine learning models. One example method includes receiving transaction data indicative of a transaction, identifying, based on the transaction data, an associated merchant of one or more merchants and an associated time window of one or more time windows, receiving historical transaction data indicative of one or more historical transactions, wherein the one or more historical transactions are associated with the associated merchant and the associated time window, generating features based on the transaction data and the historical transaction data, and determining, using a classification model, that the transaction is fraudulent, based on the generated features.

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Hod Hasharon, Israel

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