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    Learning criticality of misclassifications used as input to classification to reduce the probability of critical misclassification
    41.
    发明授权
    Learning criticality of misclassifications used as input to classification to reduce the probability of critical misclassification 有权

    公开(公告)号：US11151476B2

    公开(公告)日：2021-10-19

    申请号：US16186651

    申请日：2018-11-12

    Applicant: Cisco Technology, Inc.

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

    IPC: G06N20/00 ,  G06N7/00 ,  H04L29/08

    Abstract: In one embodiment, a device classification service that uses a machine learning-based device type classifier to classify endpoint devices with device types, identifies a set of device types having similar associated traffic telemetry features. The service obtains, via one or more user interfaces, feedback indicative of whether the device type classifier misclassifying an endpoint device having a particular device type in the set with another device type in the set would be a critical misclassification. The service trains, using the obtained feedback, a prediction model to predict an impact of misclassifying the particular device type as one of the other device types in the set of device types. The service also retrains the machine learning-based device type classifier based on a prediction from the prediction model.

    MODEL INTERPRETABILITY USING PROXY FEATURES
    42.
    发明申请
    MODEL INTERPRETABILITY USING PROXY FEATURES 有权

    公开(公告)号：US20210304061A1

    公开(公告)日：2021-09-30

    申请号：US16832090

    申请日：2020-03-27

    Applicant: Cisco Technology, Inc.

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

    IPC: G06N20/00 ,  G06N5/04

    Abstract: In one embodiment, a service identifies a set of attributes associated with a first machine learning model trained to make an inference about a computer network. The service obtains labels for each of the set of attributes, each label indicating whether its corresponding attribute is a probable cause of the inference. The service maps input features of the first machine learning model to those attributes in the set of attributes that were labeled as probable causes of the inference. The service generates a second machine learning model in part by using the mapped attributes to form a set of input features for the second machine learning model, whereby the input features of the first machine learning model and the input features of the second machine learning model differ.

    CASCADE-BASED CLASSIFICATION OF NETWORK DEVICES USING MULTI-SCALE BAGS OF NETWORK WORDS
    45.
    发明申请
    CASCADE-BASED CLASSIFICATION OF NETWORK DEVICES USING MULTI-SCALE BAGS OF NETWORK WORDS 有权

    公开(公告)号：US20210288876A1

    公开(公告)日：2021-09-16

    申请号：US17327124

    申请日：2021-05-21

    Applicant: Cisco Technology, Inc.

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

    IPC: H04L12/24 ,  H04L29/08 ,  G06K9/62

    Abstract: In one embodiment, a device classification service uses feature vectors that represent how frequently one or more traffic features were observed in a network during different time windows to train a cascade of machine learning classifiers to label one or more devices in the network with a device type. The service receives traffic features of traffic associated with a particular device in the network, and then uses the cascade of machine learning classifiers to assign a device type label to the particular device based on the traffic features of the traffic associated with the particular device. The service initiates enforcement of a network policy regarding the device based on its device type based on the device type label assigned to the particular device.

    Adaptive oscillation control for network paths using machine learning
    46.
    发明授权
    Adaptive oscillation control for network paths using machine learning 有权

    公开(公告)号：US11108651B1

    公开(公告)日：2021-08-31

    申请号：US17082215

    申请日：2020-10-28

    Applicant: Cisco Technology, Inc.

    Inventor： Grégory Mermoud ,  Jean-Philippe Vasseur ,  Vinay Kumar Kolar

    IPC: H04L12/24 ,  H04L12/707

    Abstract: In one embodiment, a device generates a model of oscillations between a particular path in a network satisfying a service level agreement template of traffic conveyed via the particular path and the particular path in the network not satisfying the service level agreement template. The device causes the traffic to be rerouted onto the particular path, based on a prediction by the model that the particular path will not oscillate for a period of time. The device determines, using the model, an adjustment to the service level agreement template that would reduce the oscillations. The device provides, to a user interface, an indication of the adjustment to the service level agreement template.

    Forecasting network KPIs
    47.
    发明授权
    Forecasting network KPIs 有权

    公开(公告)号：US11063842B1

    公开(公告)日：2021-07-13

    申请号：US16740051

    申请日：2020-01-10

    Applicant: Cisco Technology, Inc.

    Inventor： Jean-Philippe Vasseur ,  Grégory Mermoud ,  Vinay Kumar Kolar ,  Pierre-Andre Savalle

    IPC: H04L12/24 ,  G06N20/00 ,  H04L12/703 ,  H04L12/46

    Abstract: In one embodiment, a service receives input data from networking entities in a network. The input data comprises synchronous time series data, asynchronous event data, and an entity graph that that indicates relationships between the networking entities in the network. The service clusters the networking entities by type in a plurality of networking entity clusters. The service selects, based on a combination of the received input data, machine learning model data features. The service trains, using the selected machine learning model data features, a machine learning model to forecast a key performance indicator (KPI) for a particular one of the networking entity clusters.

    ANOMALY DETECTION OF MODEL PERFORMANCE IN AN MLOPS PLATFORM
    49.
    发明申请
    ANOMALY DETECTION OF MODEL PERFORMANCE IN AN MLOPS PLATFORM 有权

    公开(公告)号：US20210184958A1

    公开(公告)日：2021-06-17

    申请号：US16710836

    申请日：2019-12-11

    Applicant: Cisco Technology, Inc.

    Inventor： Vinay Kumar Kolar ,  Jean-Philippe Vasseur ,  Grégory Mermoud ,  Pierre-Andre Savalle

    IPC: H04L12/26 ,  G06N20/00 ,  G06K9/62 ,  G06F17/18

    Abstract: In one embodiment, a service tracks performance of a machine learning model over time. The machine learning model is used to monitor one or more computer networks based on data collected from the one or more computer networks. The service also tracks performance metrics associated with training of the machine learning model. The service determines that a degradation of the performance of the machine learning model is anomalous, based on the tracked performance of the machine learning model and performance metrics associated with training of the model. The service initiates a corrective measure for the degradation of the performance, in response to determining that the degradation of the performance is anomalous.

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