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    Removal of environment and local context from network traffic for device classification
    32.
    发明授权
    Removal of environment and local context from network traffic for device classification 有权

    公开(公告)号：US10826772B2

    公开(公告)日：2020-11-03

    申请号：US16188452

    申请日：2018-11-13

    Applicant: Cisco Technology, Inc.

    Inventor： Pierre-André Savalle ,  Jean-Philippe Vasseur ,  Grégory Mermoud

    IPC: H04L12/24 ,  H04L29/08 ,  G06N20/00

    Abstract: In one embodiment, a device classification service assigns a set of endpoint devices to a context group. The device classification service forms a context summary feature vector for the context group that summarizes telemetry feature vectors for the endpoint devices assigned to the context group. Each telemetry feature vector is indicative of a plurality of traffic features observed for the endpoint devices. The device classification service normalizes a telemetry feature vector for a particular endpoint device using the context summary feature vector. The device classification service classifies, using the normalized telemetry feature vector for the particular endpoint device as input to a device type classifier, the particular endpoint device as being of a particular device type.

    ADAPTIVE THRESHOLD SELECTION FOR SD-WAN TUNNEL FAILURE PREDICTION
    33.
    发明申请
    ADAPTIVE THRESHOLD SELECTION FOR SD-WAN TUNNEL FAILURE PREDICTION 审中-公开

    公开(公告)号：US20200342346A1

    公开(公告)日：2020-10-29

    申请号：US16392825

    申请日：2019-04-24

    Applicant: Cisco Technology, Inc.

    Inventor： Sharon Shoshana Wulff ,  Grégory Mermoud ,  Jean-Philippe Vasseur

    IPC: G06N20/00 ,  H04L12/46 ,  H04L12/24

    Abstract: In one embodiment, a supervisory service for a software-defined wide area network (SD-WAN) uses a plurality of different decision thresholds for a machine learning-based classifier, to predict tunnel failures of a tunnel in the SD-WAN. The supervisory service captures performance data indicative of performance of the classifier when using the different decision thresholds. The supervisory service selects, based on the captured performance data, a particular decision threshold for the classifier, in an attempt to optimize the performance of the classifier. The supervisory service uses the selected decision threshold for the classifier, to predict a tunnel failure of the tunnel.

    HIERARCHICAL MODELS USING SELF ORGANIZING LEARNING TOPOLOGIES
    34.
    发明申请
    HIERARCHICAL MODELS USING SELF ORGANIZING LEARNING TOPOLOGIES 审中-公开

    公开(公告)号：US20200304530A1

    公开(公告)日：2020-09-24

    申请号：US16894332

    申请日：2020-06-05

    Applicant: Cisco Technology, Inc.

    Inventor： Pierre-André Savalle ,  Grégory Mermoud ,  Laurent Sartran ,  Jean-Philippe Vasseur

    IPC: H04L29/06 ,  H04L12/24

    Abstract: In one embodiment, a device obtains characteristics of a first anomaly detection model executed by a first distributed learning agent in a network. The device receives a query from a second distributed learning agent in the network that requests identification of a similar anomaly detection to that of a second anomaly detection model executed by the second distributed learning agent. The device identifies, after receiving the query from the second distributed learning agent, the first anomaly detection model as being similar to that of the second anomaly detection model, based on the characteristics of the first anomaly detection model. The device causes the first anomaly detection model to be sent to the second distributed learning agent for execution.

    Using machine learning based on cross-signal correlation for root cause analysis in a network assurance service
    35.
    发明授权
    Using machine learning based on cross-signal correlation for root cause analysis in a network assurance service 有权

    公开(公告)号：US10785090B2

    公开(公告)日：2020-09-22

    申请号：US15983437

    申请日：2018-05-18

    Applicant: Cisco Technology, Inc.

    Inventor： Jean-Philippe Vasseur ,  Grégory Mermoud ,  Santosh Ghanshyam Pandey ,  Vikram Kumaran

    IPC: H04L12/24 ,  H04L12/26 ,  G06N20/00 ,  H04L29/12

    Abstract: In one embodiment, a network assurance service associates a target key performance indicator (tKPI) measured from a network with a plurality of causation key performance indicators (cKPIs) measured from the network that may indicate a root cause of a tKPI anomaly. The network assurance service applies a machine learning-based anomaly detector to the tKPI over time, to generate tKPI anomaly scores. The network assurance service calculates, for each of cKPIs, a mean and standard deviation of that cKPI using a plurality of different time windows associated with the tKPI anomaly scores. The network assurance service uses the calculated means and standard deviations of the cKPIs in the different time windows to calculate cross-correlation scores between the tKPI anomaly scores and the cKPIs. The network assurance service selects one or more of the cKPIs as the root cause of the tKPI anomaly based on their calculated cross-correlation scores.

    Using random forests to generate rules for causation analysis of network anomalies
    36.
    发明授权
    Using random forests to generate rules for causation analysis of network anomalies 有权

    公开(公告)号：US10771313B2

    公开(公告)日：2020-09-08

    申请号：US15881909

    申请日：2018-01-29

    Applicant: Cisco Technology, Inc.

    Inventor： David Tedaldi ,  Grégory Mermoud ,  Jean-Philippe Vasseur

    IPC: H04L12/24

    Abstract: In one embodiment, a network assurance service receives one or more sets of network characteristics of a network, each network characteristic forming a different feature dimension in a multi-dimensional feature space. The network assurance service applies machine learning-based anomaly detection to the one or more sets of network characteristics, to label each set of network characteristics as anomalous or non-anomalous. The network assurance service identifies, based on the labeled one or more sets of network characteristics, an anomaly pattern as a collection of unidimensional cutoffs in the feature space. The network assurance service initiates a change to the network based on the identified anomaly pattern.

    Hierarchical models using self organizing learning topologies
    37.
    发明授权
    Hierarchical models using self organizing learning topologies 有权

    公开(公告)号：US10701095B2

    公开(公告)日：2020-06-30

    申请号：US16190756

    申请日：2018-11-14

    Applicant: Cisco Technology, Inc.

    Inventor： Pierre-André Savalle ,  Grégory Mermoud ,  Laurent Sartran ,  Jean-Philippe Vasseur

    IPC: H04L29/06 ,  H04L12/24

    Abstract: In one embodiment, a device in a network maintains a plurality of anomaly detection models for different sets of aggregated traffic data regarding traffic in the network. The device determines a measure of confidence in a particular one of the anomaly detection models that evaluates a particular set of aggregated traffic data. The device dynamically replaces the particular anomaly detection model with a second anomaly detection model configured to evaluate the particular set of aggregated traffic data and has a different model capacity than that of the particular anomaly detection model. The device provides an anomaly event notification to a supervisory controller based on a combined output of the second anomaly detection model and of one or more of the anomaly detection models in the plurality of anomaly detection models.

    Dynamic selection of models for hybrid network assurance architectures
    38.
    发明授权
    Dynamic selection of models for hybrid network assurance architectures 审中-公开

    公开(公告)号：US10673728B2

    公开(公告)日：2020-06-02

    申请号：US15880689

    申请日：2018-01-26

    Applicant: Cisco Technology, Inc.

    Inventor： Andrea Di Pietro ,  Jean-Philippe Vasseur ,  Javier Cruz Mota ,  Grégory Mermoud

    IPC: H04L12/26 ,  H04L12/24

    Abstract: In one embodiment, a local service of a network reports configuration information regarding the network to a cloud-based network assurance service. The local service receives a classifier selected by the cloud-based network assurance service based on the configuration information regarding the network. The local service classifies, using the received classifier, telemetry data collected from the network, to select a modeling strategy for the network. The local service installs, based on the modeling strategy for the network, a machine learning-based model to the local service for monitoring the network.

    ACTIVE LEARNING FOR INTERACTIVE LABELING OF NEW DEVICE TYPES BASED ON LIMITED FEEDBACK
    39.
    发明申请
    ACTIVE LEARNING FOR INTERACTIVE LABELING OF NEW DEVICE TYPES BASED ON LIMITED FEEDBACK 审中-公开

    公开(公告)号：US20200160100A1

    公开(公告)日：2020-05-21

    申请号：US16194442

    申请日：2018-11-19

    Applicant: Cisco Technology, Inc.

    Inventor： Grégory Mermoud ,  Pierre-André Savalle ,  Jean-Philippe Vasseur ,  David Tedaldi

    IPC: G06K9/62 ,  H04L12/26 ,  G06F15/18

    Abstract: In one embodiment, a device clusters traffic feature vectors for a plurality of endpoints in a network into a set of clusters. Each traffic feature vector comprises traffic telemetry data captured for one of the endpoints. The device selects one of the clusters for labeling, based in part on contextual data associated with the clusters that was not used to form the clusters. The device obtains a device type label for the selected cluster by providing data regarding the selected cluster and the contextual data associated with that cluster to a user interface. The device provides the device type label and the traffic feature vectors associated with the selected cluster for training a machine learning-based device type classifier.

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