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US Patent 9591006 Lateral movement detection

Patent 9591006 was granted and assigned to Microsoft on March, 2017 by the United States Patent and Trademark Office.

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Contents

Is a
Patent
Patent

Patent attributes

Patent Applicant
Microsoft
Microsoft
Current Assignee
Microsoft
Microsoft
Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
Patent Number
9591006
Date of Patent
March 7, 2017
Patent Application Number
14490594
Date Filed
September 18, 2014
Patent Citations Received
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US Patent 12081575 Anomalous connection detection using combination of online machine learning models
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US Patent 11973781 Methods, systems, and devices for dynamically modeling and grouping endpoints for edge networking
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US Patent 11997139 Deceiving attackers accessing network data
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US Patent 12026257 Method of malware detection and system thereof
0
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US Patent 11689558 Attack path detection method, attack path detection system and non-transitory computer-readable medium
0
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US Patent 11695800 Deceiving attackers accessing network data
0
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US Patent 11716341 Methods, systems, and devices for dynamically modeling and grouping endpoints for edge networking
0
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US Patent 11716342 Methods, systems, and devices for dynamically modeling and grouping endpoints for edge networking
0
...
Patent Primary Examiner
‌
Don Zhao
Patent abstract

Lateral movement detection may be performed by employing different detection models to score logon sessions. The different detection models may be implemented by and/or utilize counts computed from historical security event data. The different detection models may include probabilistic intrusion detection models for detecting compromised behavior based on logon behavior, a sequence of security events observed during a logon session, inter-event time between security events observed during a logon session, and/or an attempt to logon using explicit credentials. Scores for each logon session that are output by the different detection models may be combined to generate a ranking score for each logon session. A list of ranked alerts may be generated based on the ranking score for each logon session to identify compromised authorized accounts and/or compromised machines. An attack graph may be automatically generated based on compromised account-machine pairs to visually display probable paths of an attacker.

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