Abnormal Security Corporation

Evan James Reiser

Kai Jing Jiang

Cheng-Lin Yeh

Jeremy Kao

Abhijit Bagri

Su Li Debbie Tan

Jeshua Alexis Bratman

Sanjay Jeyakumar

Yu Zhou Lee

Dmitry Chechik

Kevin Lau

Carlos Daniel Gasperi

Sanny Xiao Yang Liao

Yea So Jung

Sanny Xiao Lang Liao

Fang Shuo Deng

Venkatram Krishnamoorthi

Venkatram Kishnamoorthi

Feng Shuo Deng

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.

Programmatic discovery, retrieval, and analysis of communications to identify abnormal communication activity

Multistage analysis of emails to identify security threats

Introduced here are computer programs and computer-implemented techniques for discovering malicious emails and then remediating the threat posed by those malicious emails in an automated manner. A threat detection platform may monitor a mailbox to which employees of an enterprise are able to forward emails deemed to be suspicious for analysis. This mailbox may be referred to as an 'abuse mailbox' or “phishing mailbox.” The threat detection platform can examine emails contained in the abuse mailbox and then determine whether any of those emails represent threats to the security of the enterprise. For example, the threat detection platform may classify each email contained in the abuse mailbox as being malicious or non-malicious. Thereafter, the threat detection platform may determine what remediation actions, if any, are appropriate for addressing the threat posed by those emails determined to be malicious.

Abuse mailbox for facilitating discovery, investigation, and analysis of email-based threats

Threat detection platforms for detecting, characterizing, and remediating email-based threats in real time

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

Introduced here are computer programs and computer-implemented techniques for discovering malicious emails and then remediating the threat posed by those malicious emails in an automated manner. A threat detection platform may monitor a mailbox to which employees of an enterprise are able to forward emails deemed to be suspicious for analysis. This mailbox may be referred to as an 'abuse mailbox' or 'phishing mailbox.' The threat detection platform can examine emails contained in the abuse mailbox and then determine whether any of those emails represent threats to the security of the enterprise. For example, the threat detection platform may classify each email contained in the abuse mailbox as being malicious or non-malicious. Thereafter, the threat detection platform may determine what remediation actions, if any, are appropriate for addressing the threat posed by those emails determined to be malicious.

Growing community of inventors

San Francisco, CA, United States of America

Evan James Reiser