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

US12047401B2 · kind B2 · utility

Assignee

• Sift Science, Inc. · US

Inventors

• Kostyantyn Gurnov · San Francisco, US
• Wei Liu · Schenectady, US
• Nicholas Benavides · Washington, US
• Volha Leusha · San Francisco, US
• Yanqing Bao · San Francisco, US
• Louie Zhang · San Francisco, US
• Irving Chen · Sha Tin, CN
• Logan Davis · San Francisco, US
• Andy Cai · San Francisco, US

Key dates

• Filing date: Sep 12, 2023
• Grant date: Jul 23, 2024
• Expiry date: Sep 12, 2043

Classification

• Technology area (CPC G): Physics
• CPC primary: G06N20/00
• WIPO field: Computer technology
• WIPO sector: Electrical engineering

Abstract

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.