Sift Science, Inc.

Volha Leusha

Wei Liu

Kostyantyn Gurnov

Yanqing Bao

Nicholas Benavides

Irving Chen

Andy Cai

Logan Davis

Louie Zhang

A method for machine learning-based detection of an automated fraud or abuse attack includes: identifying, via a computer network, a digital event associated with a suspected automated fraud or abuse attack; composing, via one or more computers, a digital activity signature of the suspected automated fraud or abuse attack based on digital activity associated with the suspected automated fraud or abuse attack; computing, via a machine learning model, an encoded representation of the digital activity signature; searching, via the one or more computers, an automated fraud or abuse signature registry based on the encoded representation of the digital activity signature; determining a likely origin of the digital event based on the searching of the automated fraud or abuse signature registry; and selectively implementing one or more automated threat mitigation actions based on the likely origin of the digital event.

Systems and methods for machine learning-based detection of an automated fraud attack or an automated abuse attack

Growing community of inventors

San Francisco, CA, United States of America

Volha Leusha