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US Patent 10063434 Classifying applications or activities based on network behavior

Patent 10063434 was granted and assigned to ExtraHop Networks on August, 2018 by the United States Patent and Trademark Office.

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Contents

Is a
Patent
Patent
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Patent attributes

Patent Applicant
ExtraHop Networks
ExtraHop Networks
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Current Assignee
ExtraHop Networks
ExtraHop Networks
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Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
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Patent Number
100634340
Patent Inventor Names
Bhushan Prasad Khanal0
Casey Alvin Marks0
Eric Jacob Ball0
Xue Jun Wu0
Date of Patent
August 28, 2018
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Patent Application Number
156901350
Date Filed
August 29, 2017
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Patent Citations Received
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US Patent 12107888 Automated preemptive polymorphic deception
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US Patent 11463466 Monitoring encrypted network traffic
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US Patent 11463465 Automatic determination of user roles and asset types based on network monitoring
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US Patent 11461673 Shared machine learning model for application discovery
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US Patent 11496378 Correlating causes and effects associated with network activity
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US Patent 11494693 Machine learning model re-training based on distributed feedback
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US Patent 11431744 Detection of denial of service attacks
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US Patent 11438247 Correlating network traffic that crosses opaque endpoints
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Patent Primary Examiner
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Kashif Siddiqui
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Patent abstract

Embodiments are directed to monitoring network traffic in a network. A network monitoring engine may be employed to monitor the network to provide metric profiles based on a plurality of characteristics associated with one or more network flows. The network monitoring engine may provide profile objects based on the metric profiles. The network monitoring engine may provide the profile objects to a classifier engine. The classifier engine provide trained activity models selected from a plurality of trained activity models that may be based on a ranked ordering of characteristics of the trained activity models and the profile objects. The classifier engine may provide classification results for the profile objects based on the trained activity models. And, the network monitoring engine may execute policies based on the classification results associated with the profile objects.

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