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公开(公告)号：US10075360B2
公开(公告)日：2018-09-11
申请号：US14165450
申请日：2014-01-27
Applicant: Cisco Technology, Inc.
Inventor： Jean-Philippe Vasseur , Grégory Mermoud , Jonathan W. Hui , Sukrit Dasgupta
CPC classification number: H04L43/12 , H04L41/16 , H04L43/08 , Y04S40/168
Abstract: In one embodiment, a learning machine may be used to select observer nodes in a LLN such that the liveness of one or more nodes of interest may be monitored indirectly. In particular, a management device may receive network data on one or more network traffic parameters of a computer network. The management device may then determine, based on the network data, a candidate list of potential observer nodes to monitor activity or inactivity of one or more subject nodes. The management device may then dynamically select, using a machine learning model, a set of optimized observer nodes from the candidate list of potential observer nodes.
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公开(公告)号：US10062036B2
公开(公告)日：2018-08-28
申请号：US14280082
申请日：2014-05-16
Applicant: Cisco Technology, Inc.
Inventor： Grégory Mermoud , Jean-Philippe Vasseur , Sukrit Dasgupta
Abstract: In one embodiment, a network device receives metrics regarding a path in the network. A predictive model is generated using the received metrics and is operable to predict available bandwidth along the path for a particular type of traffic. A determination is made as to whether a confidence score for the predictive model is below a confidence threshold associated with the particular type of traffic. The device obtains additional data regarding the path based on a determination that the confidence score is below the confidence threshold. The predictive model is updated using the additional data regarding the path.
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公开(公告)号：US09836696B2
公开(公告)日：2017-12-05
申请号：US14339347
申请日：2014-07-23
Applicant: Cisco Technology, Inc.
Inventor： Jean-Philippe Vasseur , Grégory Mermoud , Sukrit Dasgupta
CPC classification number: G06N5/048 , G06N99/005 , H04L12/1827
Abstract: In one embodiment, a management system determines respective capability information of machine learning systems, the capability information including at least an action the respective machine learning system is configured to perform. The management system receives, for each of the machine learning systems, respective performance scoring information associated with the respective action, and computes a degree of freedom for each machine learning system to perform the respective action based on the performance scoring information. Accordingly, the management system then specifies the respective degree of freedom to the machine learning systems. In one embodiment, the management system comprises a management device that computes a respective trust level for the machine learning systems based on receiving the respective performance scoring feedback, and a policy engine that computes the degree of freedom based on receiving the trust level. In further embodiments, the machine learning system performs the action based on the degree of freedom.
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公开(公告)号：US09813432B2
公开(公告)日：2017-11-07
申请号：US14604175
申请日：2015-01-23
Applicant: Cisco Technology, Inc.
Inventor： Sukrit Dasgupta , Jean-Philippe Vasseur
CPC classification number: H04L63/1416 , G06F11/3409 , G06F11/3466 , G06F2201/86
Abstract: In one embodiment, a device in a network monitors one or more metrics regarding network traffic associated with a particular application. The device detects an application-centric anomaly based on the monitored one or more metrics. The device causes an anomaly mitigation action to be performed in the network, in response to detecting the application-centric anomaly.
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公开(公告)号：US20170310695A1
公开(公告)日：2017-10-26
申请号：US15632993
申请日：2017-06-26
Applicant: Cisco Technology, Inc.
Inventor： Jean-Philippe Vasseur , Sukrit Dasgupta , Thomas Reuther
CPC classification number: H04L63/1425 , H04L43/022 , H04L43/04 , H04L43/062 , H04L43/12 , H04L63/00 , H04L63/1416
Abstract: In one embodiment, a first device in a network receives traffic flow data from a plurality of devices in the network. The traffic flow data from at least one of the plurality of devices comprises raw packets of a traffic flow. The first device selects a set of reporting devices from among the plurality of devices based on the received traffic flow data. The first device provides traffic flow reporting instructions to the selected set of reporting devices. The traffic flow reporting instructions cause each reporting device to provide sampled traffic flow data to an anomaly detection device.
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Patent Agency Ranking
公开(公告)号：US09794145B2
公开(公告)日：2017-10-17
申请号：US14591079
申请日：2015-01-07
Applicant: Cisco Technology, Inc.
Inventor： Jean-Philippe Vasseur , Sukrit Dasgupta , Grégory Mermoud
CPC classification number: H04L43/08 , H04L41/145 , H04L41/147 , H04L41/16
Abstract: In one embodiment, a device in a network monitors performance data for a first predictive model. The first predictive model is used to make proactive decisions in the network. The device maintains a supervisory model based on the monitored performance data for the first predictive model. The device identifies a time period during which the supervisory model predicts that the first predictive model will perform poorly. The device causes a switchover from the first predictive model to a second predictive model at a point in time associated with the time period, in response to identifying the time period.
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公开(公告)号：US20170279835A1
公开(公告)日：2017-09-28
申请号：US15211145
申请日：2016-07-15
Applicant: Cisco Technology, Inc.
Inventor： Andrea Di Pietro , Jean-Philippe Vasseur , Sukrit Dasgupta
CPC classification number: H04L63/1425 , G06N3/006 , G06N20/00 , H04L41/147 , H04L43/024 , H04L43/062 , H04L43/14 , H04L63/02 , H04L63/145 , H04L63/1458 , H04L2463/144
Abstract: In one embodiment, a node in a network detects an anomaly in the network based on a result of a machine learning-based anomaly detector analyzing network traffic. The node determines a packet capture policy for the anomaly by applying a machine learning-based classifier to the result of the anomaly detector. The node selects a set of packets from the analyzed traffic based on the packet capture policy. The node stores the selected set of packets for the detected anomaly.
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公开(公告)号：US09722906B2
公开(公告)日：2017-08-01
申请号：US14604570
申请日：2015-01-23
Applicant: Cisco Technology, Inc.
Inventor： Jean-Philippe Vasseur , Sukrit Dasgupta , Thomas Reuther
CPC classification number: H04L63/1425 , H04L43/022 , H04L43/04 , H04L43/062 , H04L43/12 , H04L63/00 , H04L63/1416
Abstract: In one embodiment, a first device in a network receives traffic flow data from a plurality of devices in the network. The traffic flow data from at least one of the plurality of devices comprises raw packets of a traffic flow. The first device selects a set of reporting devices from among the plurality of devices based on the received traffic flow data. The first device provides traffic flow reporting instructions to the selected set of reporting devices. The traffic flow reporting instructions cause each reporting device to provide sampled traffic flow data to an anomaly detection device.
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公开(公告)号：US09654361B2
公开(公告)日：2017-05-16
申请号：US14276563
申请日：2014-05-13
Applicant: Cisco Technology, Inc.
Inventor： Jean-Philippe Vasseur , Sukrit Dasgupta
CPC classification number: H04L43/062 , H04L12/4633 , H04L12/4641 , H04L41/142 , H04L41/147 , H04L41/16 , H04L41/5009 , H04L43/02 , H04L43/026 , H04L43/028 , H04L43/04 , H04L43/0817 , H04L43/0829 , H04L43/087 , H04L43/0876 , H04L43/0882 , H04L43/16 , H04L63/1408
Abstract: In one embodiment, data is received at a device regarding a network-monitoring process in which one or more nodes in a network export network metrics to one or more collector nodes. A change to the network-monitoring process is determined based on the received data. The device also adjusts the network-monitoring process to implement the determined change.
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110.发明授权公开(公告)号：US09626628B2
公开(公告)日：2017-04-18
申请号：US14165462
申请日：2014-01-27
Applicant: Cisco Technology, Inc.
Inventor： Sukrit Dasgupta , Jean-Philippe Vasseur , Grégory Mermoud
IPC: G06N99/00
CPC classification number: G06N99/005
Abstract: In one embodiment, techniques are shown and described relating to a point-to-multipoint communication infrastructure for expert-based knowledge feed-back using learning machines. A learning machine may communicate an expert discovery request into a network to discover one or more experts, and then receive from the one or more experts, one or more expert discovery responses. Based on the one or more received expert discovery responses, the learning machine may then build a dynamic multicast tree of experts to assist the learning machine in a computer network.
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