REPAIR WALKER AGENTS IN A NETWORK
    91.
    发明申请

    公开(公告)号:US20190163548A1

    公开(公告)日:2019-05-30

    申请号:US15825248

    申请日:2017-11-29

    Abstract: In one embodiment, a supervisory device in a network receives a help request from a first node in the network indicative of a problem in the network detected by the first node. The supervisory device identifies a second node in the network that is hosting a repair walker agent able to address the detected problem. The supervisory device determines a network path via which the second node is to send repair walker agent to the first node. The supervisory device instructs the second node to send the repair walker agent to the first node via the determined path.

    AUTOMATED SENSOR COVERAGE OPTIMIZATION WITH AN ACTUATING WALKER AGENT

    公开(公告)号:US20190141540A1

    公开(公告)日:2019-05-09

    申请号:US15804064

    申请日:2017-11-06

    Abstract: In one embodiment, a first actuator in a network of sensors and actuators executes a walker agent configured to adjust an actuation setting of the first actuator. The actuation setting controls an area of coverage of the first actuator when actuated. The executing agent on the first actuator receives one or more sensor measurements from one or more of the sensors that are in communication range of the first actuator. The executing agent also controls, based on the received one or more sensor measurements, the area of coverage of the first actuator by adjusting its actuation setting, in an attempt to optimize coverage of the sensors in the network by the areas of coverage of the actuators. The first actuator unloads the executing walker agent after adjusting the actuation setting of the first actuator and propagates the agent to another one of the actuators in the network for execution.

    Proactive roaming handshakes based on mobility graphs

    公开(公告)号:US10285108B1

    公开(公告)日:2019-05-07

    申请号:US15782197

    申请日:2017-10-12

    Abstract: In one embodiment, a service maintains a mobility path graph that represents roaming transitions between wireless access points in a network by one or more client devices in the network. The service identifies, using the mobility path graph, one of the wireless access points in the network to which a particular client device is predicted to roam. The service performs, in advance of the particular client device initiating roaming to the one or more wireless access points, one or more roaming handshakes on behalf of the particular client device and with respect to the wireless access point to which the particular client device is predicted to roam. The service sends handshake data from the performed one or more roaming handshakes to the identified access point to which the particular client device is predicted to roam.

    PROACTIVE ROAMING HANDSHAKES BASED ON MOBILITY GRAPHS

    公开(公告)号:US20190116539A1

    公开(公告)日:2019-04-18

    申请号:US15782197

    申请日:2017-10-12

    Abstract: In one embodiment, a service maintains a mobility path graph that represents roaming transitions between wireless access points in a network by one or more client devices in the network. The service identifies, using the mobility path graph, one of the wireless access points in the network to which a particular client device is predicted to roam. The service performs, in advance of the particular client device initiating roaming to the one or more wireless access points, one or more roaming handshakes on behalf of the particular client device and with respect to the wireless access point to which the particular client device is predicted to roam. The service sends handshake data from the performed one or more roaming handshakes to the identified access point to which the particular client device is predicted to roam.

    Edge-based machine learning for encoding legitimate scanning

    公开(公告)号:US10243980B2

    公开(公告)日:2019-03-26

    申请号:US15205732

    申请日:2016-07-08

    Abstract: In one embodiment, a device in a network receives an indication that a network anomaly detected by an anomaly detector of a first node in the network is associated with scanning activity in the network. The device receives labeled traffic data associated with the detected anomaly that identifies whether the traffic data is associated with legitimate or illegitimate scanning activity. The device trains a machine learning-based classifier using the labeled traffic data to distinguish between legitimate and illegitimate scanning activity in the network. The device deploys the trained classifier to the first node, to distinguish between legitimate and illegitimate scanning activity in the network.

    HIERARCHICAL MODELS USING SELF ORGANIZING LEARNING TOPOLOGIES

    公开(公告)号:US20190081973A1

    公开(公告)日:2019-03-14

    申请号:US16190756

    申请日:2018-11-14

    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.

    Specializing unsupervised anomaly detection systems using genetic programming

    公开(公告)号:US10218729B2

    公开(公告)日:2019-02-26

    申请号:US15205122

    申请日:2016-07-08

    Abstract: In one embodiment, a device in a network receives sets of traffic flow features from an unsupervised machine learning-based anomaly detector. The sets of traffic flow features are associated with anomaly scores determined by the anomaly detector. The device ranks the sets of traffic flow features based in part on their anomaly scores. The device applies a genetic programming approach to the ranked sets of traffic flow features to generate new sets of traffic flow features. The genetic programming approach uses a fitness function that is based in part on the rankings of the sets of traffic flow features. The device specializes the anomaly detector to emphasize a particular type of anomaly using the new sets of traffic flow features.

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