Invention Application
- Patent Title: RETROSPECTIVE LEARNING OF COMMUNICATION PATTERNS BY MACHINE LEARNING MODELS FOR DISCOVERING ABNORMAL BEHAVIOR
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Application No.: US17361106Application Date: 2021-06-28
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Publication No.: US20210329035A1Publication Date: 2021-10-21
- Inventor: Sanjay Jeyakumar , Jeshua Alexis Bratman , Dmitry Chechik , Abhijit Bagri , Evan James Reiser , Sanny Xiao Yang Liao , Yu Zhou Lee , Carlos Daniel Gasperi , Kevin Lau , Kai Jing Jiang , Su Li Debbie Tan , Jeremy Kao , Cheng-Lin Yeh
- Applicant: Abnormal Security Corporation
- Applicant Address: US CA San Francisco
- Assignee: Abnormal Security Corporation
- Current Assignee: Abnormal Security Corporation
- Current Assignee Address: US CA San Francisco
- Main IPC: H04L29/06
- IPC: H04L29/06 ; G06N20/00

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.
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