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User assistance coordination in anomaly detection

  • US 10,469,511 B2
  • Filed: 07/15/2016
  • Issued: 11/05/2019
  • Est. Priority Date: 03/28/2016
  • Status: Active Grant
First Claim
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1. A method comprising:

  • receiving, at a device in a network, feedback regarding an anomaly reporting mechanism used by the device to report network anomalies detected by a plurality of distributed learning agents to a user interface, wherein the feedback includes information about how the network anomalies are reported by the anomaly reporting mechanism;

    determining, by the device, an anomaly assessment rate at which a user of the user interface is expected to assess reported anomalies based in part on the feedback;

    receiving, at the device, an anomaly notification regarding a particular anomaly detected by a particular one of the distributed learning agents;

    dynamically adjusting, by the device, a number of anomalies reported to the user interface based on the determined anomaly assessment rate, wherein the determined anomaly assessment rate is an inference made by a machine learning model based on a behavior of a user and the feedback, wherein the anomaly reporting mechanism is adjusted rather than a classifier on the plurality of distributed learning agents; and

    reporting, by the device and via the anomaly reporting mechanism, the particular anomaly to the user interface according to the adjustment.

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