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    Detection and resolution of rule conflicts in device classification systems
    11.
    发明授权
    Detection and resolution of rule conflicts in device classification systems 有权

    公开(公告)号：US11290331B2

    公开(公告)日：2022-03-29

    申请号：US16428202

    申请日：2019-05-31

    Applicant: Cisco Technology, Inc.

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

    IPC: H04L12/24 ,  H04L29/06 ,  H04L12/723 ,  H04L41/0873 ,  H04L45/50 ,  H04L41/0893 ,  H04L41/0816

    Abstract: In one embodiment, a service receives a plurality of device type classification rules, each rule comprising a device type label and one or more device attributes used as criteria for application of the label to a device in a network. The service estimates, across a space of the device attributes, device densities of devices having device attributes at different points in that space. The service uses the estimated device densities to identify two or more of the device type classification rules as having overlapping device attributes. The service determines that the two or more device type classification rules are in conflict, based on the two or more rules having different device type labels. The service generates a rule conflict resolution that comprises one of the device type labels from the conflicting two or more device type classification rules.

    Deep learning architecture for collaborative anomaly detection and explanation
    13.
    发明授权
    Deep learning architecture for collaborative anomaly detection and explanation 有权

    公开(公告)号：US10574512B1

    公开(公告)日：2020-02-25

    申请号：US16120529

    申请日：2018-09-04

    Applicant: Cisco Technology, Inc.

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

    IPC: H04L12/24 ,  G06N3/04 ,  G06N3/08

    Abstract: In one embodiment, a network assurance service that monitors a network detects a behavioral anomaly in the network using an anomaly detector that compares an anomaly detection threshold to a target value calculated based on a first set of one or more measurements from the network. The service uses an explanation model to predict when the anomaly detector will detect anomalies. The explanation model takes as input a second set of one or more measurements from the network that differs from the first set. The service determines that the detected anomaly is explainable, based on the explanation model correctly predicting the detection of the anomaly by the anomaly detector. The service provides an anomaly detection alert for the detected anomaly to a user interface, based on the detected anomaly being explainable. The anomaly detection alert indicates at least one measurement from the second set as an explanation for the anomaly.

    DETECTING SPOOFING IN DEVICE CLASSIFICATION SYSTEMS
    17.
    发明申请
    DETECTING SPOOFING IN DEVICE CLASSIFICATION SYSTEMS 有权

    公开(公告)号：US20210329029A1

    公开(公告)日：2021-10-21

    申请号：US16851290

    申请日：2020-04-17

    Applicant: Cisco Technology, Inc.

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

    IPC: H04L29/06 ,  H04L12/26 ,  G06N3/04

    Abstract: In various embodiments, a device classification service obtains device telemetry data indicative of declarative attributes of a device in a network and indicative of behavioral attributes of that device. The device classification service labels the device with a device type, based on the device telemetry data. The device classification service detects device type spoofing exhibited by the device using a model that models a relationship between the declarative attributes and the behavioral attributes. The device classification service initiates, based on the device type spoofing, a mitigation action regarding the device.

    Active learning for interactive labeling of new device types based on limited feedback
    18.
    发明授权
    Active learning for interactive labeling of new device types based on limited feedback 有权

    公开(公告)号：US11100364B2

    公开(公告)日：2021-08-24

    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 ,  G06N20/00 ,  H04L12/26

    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.

    LEARNING STABLE REPRESENTATIONS OF DEVICES FOR CLUSTERING-BASED DEVICE CLASSIFICATION SYSTEMS
    20.
    发明申请
    LEARNING STABLE REPRESENTATIONS OF DEVICES FOR CLUSTERING-BASED DEVICE CLASSIFICATION SYSTEMS 审中-公开

    公开(公告)号：US20200336397A1

    公开(公告)日：2020-10-22

    申请号：US16389013

    申请日：2019-04-19

    Applicant: Cisco Technology, Inc.

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

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

    Abstract: In one embodiment, a device classification service obtains telemetry data for a plurality of devices in a network. The device classification service repeatedly assigns the devices to device clusters by applying clustering to the obtained telemetry data. The device classification service determines a measure of stability loss associated with the cluster assignments. The measure of stability loss is based in part on whether a device is repeatedly assigned to the same device cluster. The device classification service determines, based on the measure of stability loss, that the cluster assignments have stabilized. The device classification service obtains device type labels for the device clusters, after determining that the cluster assignments have stabilized.

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