Flash classification using machine learning for device classification systems

    公开(公告)号:US11349716B2

    公开(公告)日:2022-05-31

    申请号:US16878780

    申请日:2020-05-20

    Abstract: In various embodiments, a device classification service makes a determination that an endpoint device in a network is eligible for expedited device classification based on a policy. The device classification service obtains, after making the determination that the endpoint device in the network is eligible for expedited device classification, telemetry data regarding the endpoint device generated by actively probing the endpoint device. The device classification service determines whether the telemetry data regarding the endpoint device matches any existing device classification rules. The device classification service generates, based on the telemetry data, a device classification rule that assigns a device type to the endpoint device, when the telemetry data does not match any existing device classification rules.

    REVISITING DEVICE CLASSIFICATION RULES UPON OBSERVATION OF NEW ENDPOINT ATTRIBUTES

    公开(公告)号:US20210328986A1

    公开(公告)日:2021-10-21

    申请号:US16854115

    申请日:2020-04-21

    Abstract: In various embodiments, a device classification service uses an initial device classification rule to label each of a set of endpoint devices in a network as being of a particular device type. The device classification service identifies a particular attribute exhibited by at least a portion of the set of endpoint devices and was not previously used to generate the initial device classification rule. The device classification service generates one or more new device classification rules based in part on the particular attribute. The device classification service switches from using the initial device classification rule to label endpoint devices in the network to using the one or more new device classification rules to label endpoint devices in the network.

    COMPRESSED TRANSMISSION OF NETWORK DATA FOR NETWORKING MACHINE LEARNING SYSTEMS

    公开(公告)号:US20210281491A1

    公开(公告)日:2021-09-09

    申请号:US16808896

    申请日:2020-03-04

    Abstract: In one embodiment, a service receives telemetry data indicative of a plurality of performance metrics captured in a network. The service jointly trains, using the received telemetry data, a compression model and an inference model, the compression model being a first machine learning model trained to convert the telemetry data into a compressed representation of the telemetry data and the inference model being a second machine learning model trained to take the compressed representation of the telemetry data as input and apply a classification label to it. The service deploys the compression model to the network. The service receives compressed telemetry data generated by the compression model deployed to the network. The service uses the inference model to classify the compressed telemetry data generated by the compression model deployed to the network.

    Learning packet capture policies to enrich context for device classification systems

    公开(公告)号:US11018943B1

    公开(公告)日:2021-05-25

    申请号:US16878931

    申请日:2020-05-20

    Abstract: In various embodiments, a device classification service receives, from a networking device in a network, an indication that deep packet inspection (DPI) trace data is not available for an endpoint device in the network because the endpoint device does not match any DPI policies of the networking device. The service configures a first DPI policy on the networking device that causes it to capture a DPI trace of traffic associated with the endpoint device. The service receives, via a user interface, an indication that a subset of attributes of the endpoint device in the DPI trace is relevant to labeling the endpoint device with a device type. The service replaces the first DPI policy on the networking device with a second DPI policy that causes it to report only the subset of attributes of endpoint devices to the device classification service for endpoint devices that match the second DPI policy.

    USING RANDOM FORESTS TO GENERATE RULES FOR CAUSATION ANALYSIS OF NETWORK ANOMALIES

    公开(公告)号:US20190238396A1

    公开(公告)日:2019-08-01

    申请号:US15881909

    申请日:2018-01-29

    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.

    MODEL COUNTERFACTUAL SCENARIOS OF SLA VIOLATIONS ALONG NETWORK PATHS

    公开(公告)号:US20220231939A1

    公开(公告)日:2022-07-21

    申请号:US17153561

    申请日:2021-01-20

    Abstract: In one embodiment, a device obtains traffic telemetry data regarding a first path in a network and an alternate path in the network. The device predicts, based on the traffic telemetry data, an amount of traffic for an application that is expected at a particular time. The device makes, based on the traffic telemetry data and on the amount of traffic for the application that is predicted to be expected at the particular time, a counterfactual prediction as to whether the alternate path would violate a service level agreement associated with the traffic, should the traffic be routed via the alternate path at the particular time. The device causes, based on the counterfactual prediction, the traffic for the application to be rerouted from the first path in the network to the alternate path, prior to the particular time.

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