Abnormal Security Corporation

Cheng-Lin Yeh

Jeshua Alexis Bratman

Sanjay Jeyakumar

Kai Jing Jiang

Su Li Debbie Tan

Dmitry Chechik

Yu Zhou Lee

Abhijit Bagri

Jeremy Kao

Evan James Reiser

Kevin Lau

Carlos Daniel Gasperi

Sanny Xiao Lang Liao

Sanny Xiao Yang Liao

Umut Gultepe

Raphael Kargon

Erin Elisabeth Edkins Ludert

Yea So Jung

Lawrence Stockton Moore

Hariank Sagar Muthakana

Geng Sng

Joey Esteban Cerquera

Nishant Bhalchandra Karandikar

Fang Shuo Deng

Venkatram Krishnamoorthi

Micah J Zirn

Michael Douglas Kralka

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

Introduced here are threat detection platforms designed to discover possible instances of email account compromise in order to identify threats to an enterprise. In particular, a threat detection platform can examine the digital activities performed with the email accounts associated with employees of the enterprise to determine whether any email accounts are exhibiting abnormal behavior. Examples of digital activities include the reception of an incoming email, transmission of an outgoing email, creation of a mail filter, and occurrence of a sign-in event (also referred to as a 'login event'). Thus, the threat detection platform can monitor the digital activities performed with a given email account to determine the likelihood that the given email account has been compromised.

Discovering email account compromise through assessments of digital activities

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

A plurality of features associated with a message are determined. At least one feature included in the plurality of features is associated with a payload of the message. A determination is made that supplemental analysis should be performed on the message. The determination is based at least in part on performing behavioral analysis using at least some of the features included in the plurality of features. Supplemental analysis is performed.

Multi-tiered approach to payload detection for incoming communications

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

Deriving and surfacing insights regarding security threats is disclosed. A plurality of features associated with a message is determined. A plurality of facet models is used to analyze the determined features. Based at least in part on the analysis, it is determined that the message poses a security threat. A prioritized set of information is determined to be provided as output that is representative of why the message was determined to pose a security threat. At least a portion of the prioritized set of information is provided as output.

Deriving and surfacing insights regarding 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.

Growing community of inventors

Menlo Park, CA, United States of America

Cheng-Lin Yeh