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US Patent 11477222 Cyber threat defense system protecting email networks with machine learning models using a range of metadata from observed email communications

Patent 11477222 was granted and assigned to Darktrace on October, 2022 by the United States Patent and Trademark Office.

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Contents

Is a
Patent
Patent
0

Patent attributes

Patent Applicant
Darktrace
Darktrace
0
Current Assignee
Darktrace
Darktrace
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Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
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Patent Number
114772220
Patent Inventor Names
Stephen Pickman0
Matthew Dunn0
Matthew Ferguson0
Date of Patent
October 18, 2022
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Patent Application Number
167326440
Date Filed
January 2, 2020
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Patent Citations
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US Patent 10735445 Detecting behavioral anomaly in machine learned rule sets
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US Patent 11243941 Techniques for generating pre-emptive expectation messages
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US Patent 11252171 Methods and systems for detecting abnormal user activity
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US Patent 10237298 Session management
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US Patent 10268821 Cyber security
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US Patent 10419466 Cyber security using a model of normal behavior for a group of entities
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US Patent 10516693 Cyber security
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US Patent 10701093 Anomaly alert system for cyber threat detection
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Patent Citations Received
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US Patent 11757907 Cybersecurity threat intelligence and remediation system
Patent Primary Examiner
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Michael W Chao
0

A cyber-threat defense system for a network including its email domain protects this network from cyber threats. Modules utilize machine learning models as well communicate with a cyber threat module. Modules analyze the wide range of metadata from the observed email communications. The cyber threat module analyzes with the machine learning models trained on a normal behavior of email activity and user activity associated with the network and in its email domain in order to determine when a deviation from the normal behavior of email activity and user activity is occurring. A mass email association detector determines a similarity between highly similar emails being i) sent from or ii) received by a collection of two or more individual users in the email domain in a substantially simultaneous time frame. Mathematical models can be used to determine similarity weighing in order to derive a similarity score between compared emails.

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