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公开(公告)号：US10063434B1
公开(公告)日：2018-08-28
申请号：US15690135
申请日：2017-08-29
摘要： 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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公开(公告)号：US11012329B2
公开(公告)日：2021-05-18
申请号：US16565109
申请日：2019-09-09
发明人： Eric Jacob Ball , Eric Joseph Hammerle , Benjamin Thomas Higgins , Bhushan Prasad Khanal , Michael Kerber Krause Montague , Xue Jun Wu
IPC分类号： H04L12/26
摘要： Embodiments are directed to monitoring network traffic using a monitoring engine that monitors network traffic in networks to provide metrics. An inference engine may provide activity profiles based on portions of the network traffic where each activity profile includes features associated with the portions of network traffic. The inference engine may determine other activity profiles correlated with the activity profiles based on correlation models such that the determination of the other activity profiles occurs prior to monitoring an occurrence of other portions of the network traffic. The inference engine may modify monitoring actions of the monitoring engine based on the other activity profiles. The inference engine may provide reports based on the portions of the network traffic, the activity profiles, the other portions of the network traffic, or the other activity profiles.
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公开(公告)号：US20220070073A1
公开(公告)日：2022-03-03
申请号：US17318423
申请日：2021-05-12
发明人： Eric Jacob Ball , Eric Joseph Hammerle , Benjamin Thomas Higgins , Bhushan Prasad Khanal , Michael Kerber Krause Montague , Xue Jun Wu
IPC分类号： H04L12/26
摘要： Embodiments are directed to monitoring network traffic using a monitoring engine that monitors network traffic in networks to provide metrics. An inference engine may provide activity profiles based on portions of the network traffic where each activity profile includes features associated with the portions of network traffic. The inference engine may determine other activity profiles correlated with the activity profiles based on correlation models such that the determination of the other activity profiles occurs prior to monitoring an occurrence of other portions of the network traffic. The inference engine may modify monitoring actions of the monitoring engine based on the other activity profiles. The inference engine may provide reports based on the portions of the network traffic, the activity profiles, the other portions of the network traffic, or the other activity profiles.
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公开(公告)号：US10411978B1
公开(公告)日：2019-09-10
申请号：US16100116
申请日：2018-08-09
发明人： Eric Jacob Ball , Eric Joseph Hammerle , Benjamin Thomas Higgins , Bhushan Prasad Khanal , Michael Kerber Krause Montague , Xue Jun Wu
IPC分类号： H04L12/26
摘要： Embodiments are directed to monitoring network traffic using a monitoring engine that monitors network traffic in networks to provide metrics. An inference engine may provide activity profiles based on portions of the network traffic where each activity profile includes features associated with the portions of network traffic. The inference engine may determine other activity profiles correlated with the activity profiles based on correlation models such that the determination of the other activity profiles occurs prior to monitoring an occurrence of other portions of the network traffic. The inference engine may modify monitoring actions of the monitoring engine based on the other activity profiles. The inference engine may provide reports based on the portions of the network traffic, the activity profiles, the other portions of the network traffic, or the other activity profiles.
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公开(公告)号：US11496378B2
公开(公告)日：2022-11-08
申请号：US17318423
申请日：2021-05-12
发明人： Eric Jacob Ball , Eric Joseph Hammerle , Benjamin Thomas Higgins , Bhushan Prasad Khanal , Michael Kerber Krause Montague , Xue Jun Wu
IPC分类号： H04L43/062 , H04L43/04 , H04L43/08 , H04L43/12
摘要： Embodiments are directed to monitoring network traffic using a monitoring engine that monitors network traffic in networks to provide metrics. An inference engine may provide activity profiles based on portions of the network traffic where each activity profile includes features associated with the portions of network traffic. The inference engine may determine other activity profiles correlated with the activity profiles based on correlation models such that the determination of the other activity profiles occurs prior to monitoring an occurrence of other portions of the network traffic. The inference engine may modify monitoring actions of the monitoring engine based on the other activity profiles. The inference engine may provide reports based on the portions of the network traffic, the activity profiles, the other portions of the network traffic, or the other activity profiles.
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公开(公告)号：US20200052985A1
公开(公告)日：2020-02-13
申请号：US16565109
申请日：2019-09-09
发明人： Eric Jacob Ball , Eric Joseph Hammerle , Benjamin Thomas Higgins , Bhushan Prasad Khanal , Michael Kerber Krause Montague , Xue Jun Wu
IPC分类号： H04L12/26
摘要： Embodiments are directed to monitoring network traffic using a monitoring engine that monitors network traffic in networks to provide metrics. An inference engine may provide activity profiles based on portions of the network traffic where each activity profile includes features associated with the portions of network traffic. The inference engine may determine other activity profiles correlated with the activity profiles based on correlation models such that the determination of the other activity profiles occurs prior to monitoring an occurrence of other portions of the network traffic. The inference engine may modify monitoring actions of the monitoring engine based on the other activity profiles. The inference engine may provide reports based on the portions of the network traffic, the activity profiles, the other portions of the network traffic, or the other activity profiles.
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公开(公告)号：US10382296B2
公开(公告)日：2019-08-13
申请号：US16113442
申请日：2018-08-27
摘要： 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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公开(公告)号：US20190068465A1
公开(公告)日：2019-02-28
申请号：US16113442
申请日：2018-08-27
摘要： 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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