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US Patent 8359652 Detecting anomalies in access control lists

Patent 8359652 was granted and assigned to Microsoft on January, 2013 by the United States Patent and Trademark Office.

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Contents

Is a
Patent
Patent

Patent attributes

Current Assignee
Microsoft
Microsoft
Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
Patent Number
8359652
Patent Inventor Names
Prasad G. Naldurg0
Ranjita Bhagwan0
Tathagata Das0
Date of Patent
January 22, 2013
Patent Application Number
12610309
Date Filed
October 31, 2009
Patent Citations Received
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US Patent 11693688 Recommendation generation based on selection of selectable elements of visual representation
0
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US Patent 11991187 Security threat detection based on network flow analysis
0
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US Patent 12015591 Reuse of groups in security policy
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US Patent 11792151 Detection of threats based on responses to name resolution requests
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US Patent 11921610 Correlation key used to correlate flow and context data
0
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US Patent 11785032 Security threat detection based on network flow analysis
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US Patent 11997120 Detecting threats to datacenter based on analysis of anomalous events
0
Patent Primary Examiner
‌
Minh Dinh
Patent abstract

An access control anomaly detection system and method to detect potential anomalies in access control permissions and report those potential anomalies in real time to an administrator for possible action. Embodiments of the system and method input access control lists and semantic groups (or any dataset having binary matrices) to perform automated anomaly detection. This input is processed in three broad phases. First, policy statements are extracted from the access control lists. Next, object-level anomaly detection is performed using thresholds by categorizing outliers in the policies discovered in the first phase as potential anomalies. This object-level anomaly detection can yield object-level security anomalies and object-level accessibility anomalies. Group-level anomaly detection is performed in the third phase by using semantic groups and user sets extracted in first phase to find maximal overlaps using group mapping. This group-level anomaly detection can yield group-level security anomalies and group-level accessibility anomalies.

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