Retrospective learning of communication patterns by machine learning models for discovering abnormal behavior

US11050793B2 · kind B2 · utility

Assignee

• Abnormal Security Corporation · US

Inventors

• Sanjay Jeyakumar · Berkeley, US
• Jeshua Alexis Bratman · San Francisco, US
• Dmitry Chechik · Redwood City, US
• Abhijit Bagri · San Francisco, US
• Evan James Reiser · San Francisco, US
• Sanny Xiao Lang Liao · San Francisco, US
• Yu Zhou Lee · San Francisco, US
• Carlos Daniel Gasperi · New York, US
• Kevin Lau · Brown Center, US
• Kai Jing Jiang · San Francisco, US
• Su Li Debbie Tan · San Mateo, US
• Jeremy Kao · San Francisco, US
• Cheng-Lin Yeh · San Francisco, US

Key dates

• Filing date: Jul 13, 2020
• Grant date: Jun 29, 2021
• Expiry date: Jul 13, 2040

Classification

• Technology area (CPC G): Physics
• CPC primary: G06N7/01
• WIPO field: Computer technology
• WIPO sector: Electrical engineering

Abstract

Conventional email filtering services are not suitable for recognizing sophisticated malicious emails, and therefore may allow sophisticated malicious emails to reach inboxes by mistake. Introduced here are threat detection platforms designed to take an integrative approach to detecting security threats. For example, after receiving input indicative of an approval from an individual to access past email received by employees of an enterprise, a threat detection platform can download past emails to build a machine learning (ML) model that understands the norms of communication with internal contacts (e.g., other employees) and/or external contacts (e.g., vendors). By applying the ML model to incoming email, the threat detection platform can identify security threats in real time in a targeted manner.