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公开(公告)号：US20190089599A1
公开(公告)日：2019-03-21
申请号：US15705462
申请日：2017-09-15
Applicant: Cisco Technology, Inc.
Inventor： Pierre-André Savalle , Grégory Mermoud , Jean-Philippe Vasseur
CPC classification number: H04L41/22 , H04L41/142 , H04L41/16 , H04L41/5025 , H04L63/0421 , H04L67/303 , H04L67/34 , H04L67/36 , H04W12/02
Abstract: In one embodiment, a service identifies a performance issue exhibited by a first device in a first network. The service forms a set of one or more time series of one or more characteristics of the first device associated with the identified performance issue. The service generates a mapping between the set of one or more time series of one or more characteristics of the first device to one or more time series of one or more characteristics of a second device in a second network. The mapping comprises a relevancy score that quantifies a degree of similarity between the characteristics of the first and second devices. The service determines a likelihood of the second device exhibiting the performance issue based on the generated mapping and on the relevancy score. The service provides an indication of the determined likelihood to a user interface associated with the second network.
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公开(公告)号：US10220167B2
公开(公告)日：2019-03-05
申请号：US15180675
申请日：2016-06-13
Applicant: Cisco Technology, Inc.
Inventor： Grégory Mermoud , Jean-Philippe Vasseur , Pierre-André Savalle
IPC: H04L29/06 , A61M15/00 , H04L29/08 , A61M11/00 , A61M16/00 , A61K9/00 , A61K31/58 , A61M16/14 , G06F19/00
Abstract: In one embodiment, a device in a network detects an anomaly in the network by analyzing a set of sample data regarding one or more conditions of the network using a behavioral analytics model. The device receives feedback regarding the detected anomaly. The device determines that the anomaly was a true positive based on the received feedback. The device excludes the set of sample data from a training set for the behavioral analytics model, in response to determining that the anomaly was a true positive.
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123.发明授权公开(公告)号：US10193912B2
公开(公告)日：2019-01-29
申请号：US15052257
申请日：2016-02-24
Applicant: Cisco Technology, Inc.
Inventor： Grégory Mermoud , Jean-Philippe Vasseur , Pierre-André Savalle
Abstract: In one embodiment, a device in a network loads an anomaly detection model for warm-start. The device filters input data for the model during a warm-start grace period after warm-start of the anomaly detection model. The model is not updated during the warm-start grace period based on the filtering. The device determines an end to the warm-start grace period. The device updates the anomaly detection model using unfiltered input data for the anomaly detection model after the determined end to the warm-start grace period.
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公开(公告)号：US20190028909A1
公开(公告)日：2019-01-24
申请号：US15655153
申请日：2017-07-20
Applicant: Cisco Technology, Inc.
Inventor： Grégory Mermoud , Jean-Philippe Vasseur
IPC: H04W24/08 , H04L12/24 , H04L12/801 , G06N99/00 , H04W16/18
Abstract: In one embodiment, a device receives network metrics regarding networking equipment of a network in a physical location. The device predicts a health status score for the networking equipment in the physical location using the received network metrics as input to a machine learning-based predictive scoring model. The device provides an indication of the predicted health status score in conjunction with a visualization of the physical location for display by an electronic display. The device adjusts the predictive scoring model based on feedback regarding the predicted health status score.
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公开(公告)号：US10182066B2
公开(公告)日：2019-01-15
申请号：US15801807
申请日：2017-11-02
Applicant: Cisco Technology, Inc.
Inventor： Fabien Flacher , Grégory Mermoud , Jean-Philippe Vasseur , Sukrit Dasgupta
IPC: H04L29/06
Abstract: In one embodiment, a device in a network analyzes data indicative of a behavior of a network using a supervised anomaly detection model. The device determines whether the supervised anomaly detection model detected an anomaly in the network from the analyzed data. The device trains an unsupervised anomaly detection model, based on a determination that no anomalies were detected by the supervised anomaly detection model.
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Patent Agency Ranking
公开(公告)号：US10171332B2
公开(公告)日：2019-01-01
申请号：US15630362
申请日：2017-06-22
Applicant: Cisco Technology, Inc.
Inventor： Grégory Mermoud , Jean-Philippe Vasseur , Diane Bouchacourt
IPC: H04L12/26 , H04L12/24 , H04L12/753
Abstract: In one embodiment, network information associated with a plurality of nodes in a network is received at a device in a network. From the plurality of nodes, a node is selected based on a determination that the selected node is an outlier among the plurality of nodes according to the received network information. Then, a probe is sent to the selected node, and in response to the probe, a performance metric is received from the selected node at the device.
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127.发明申请公开(公告)号：US20180359648A1
公开(公告)日：2018-12-13
申请号：US15617444
申请日：2017-06-08
Applicant: Cisco Technology, Inc.
Inventor： Pierre-André Savalle , Grégory Mermoud , Jean-Philippe Vasseur , Javier Cruz Mota
CPC classification number: H04W24/02 , H04W36/00835 , H04W36/32 , H04W64/00 , H04W84/12
Abstract: In one embodiment, a device receives data regarding usage of access points in a network by a plurality of clients in the network. The device maintains an access point graph that represents the access points in the network as vertices of the access point graph. The device generates, for each of the plurality of clients, client trajectories as trajectory subgraphs of the access point graph. A particular client trajectory for a particular client comprises a set of edges between a subset of the vertices of the access point graph and represents transitions between access points in the network performed by the particular client. The device identifies a transition pattern from the client trajectories by deconstructing the trajectory subgraphs. The device uses the identified transition pattern to effect a configuration change in the network.
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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：US20180241762A1
公开(公告)日：2018-08-23
申请号：US15440116
申请日：2017-02-23
Applicant: Cisco Technology, Inc.
Inventor： Pierre-André Savalle , Grégory Mermoud , Laurent Sartran , Jean-Philippe Vasseur
CPC classification number: H04L63/1425 , G06N20/00 , H04L63/1441 , H04L63/1458 , H04L2463/141 , H04L2463/144
Abstract: In one embodiment, a device in a network receives a notification of a particular anomaly detected by a distributed learning agent in the network that executes a machine learning-based anomaly detector to analyze traffic in the network. The device computes one or more distance scores between the particular anomaly and one or more previously detected anomalies. The device also computes one or more relevance scores for the one or more previously detected anomalies. The device determines a reporting score for the particular anomaly based on the one or more distance scores and on the one or more relevance scores. The device reports the particular anomaly to a user interface based on the determined reporting score.
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