The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Dec. 27, 2022

Filed:

Jun. 25, 2020
Applicant:

Amazon Technologies, Inc., Seattle, WA (US);

Inventors:

Sergul Aydore, New York, NY (US);

Baris Coskun, Glen Rock, NJ (US);

Luca Melis, New York, NY (US);

Assignee:

Amazon Technologies, Inc., Seattle, WA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 16/245 (2019.01); G06N 3/08 (2006.01); G06N 3/04 (2006.01);
U.S. Cl.
CPC ...
G06N 3/088 (2013.01); G06F 16/245 (2019.01); G06N 3/0454 (2013.01);
Abstract

Systems, devices, and methods are provided for detecting anomalous events from categorical data using autoencoders. A system may receive a data set associated with actions requested within the computing environment, wherein the data set includes first categorical data indicative of anomalous activity in the computing environment. The system may train an autoencoder to reconstruct approximations of requests associated with the computing environment based on the received data set, wherein training the autoencoder includes using a beta divergence and a maximum mean discrepancy divergence. The trained system may receive a request to invoke an action within the computing environment, may generate a reconstruction of the request to invoke the action using the trained autoencoder, may determine a normalcy score based on a probability that the reconstruction of the request exists in the training data set, and, based on the calculated normalcy score, may determine whether requests indicate anomalous data.


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