×

Anomaly selection using distance metric-based diversity and relevance

  • US 10,616,251 B2
  • Filed: 02/23/2017
  • Issued: 04/07/2020
  • Est. Priority Date: 02/23/2017
  • Status: Active Grant
First Claim
Patent Images

1. A method comprising:

  • receiving, at a device in a network, a notification of a particular anomaly detected by a distributed learning agent in the network that executes a machine learning-based anomaly detector to analyze traffic in the network;

    computing, by the device, one or more distance scores between the particular anomaly and one or more previously detected anomalies;

    computing, by the device, one or more relevance scores for the one or more previously detected anomalies;

    computing, by the device, a similarity score between the particular anomaly and the one or more previously detected anomalies using a weighting function that discounts the one or more previously detected anomalies based on the one or more distance scores between the particular anomaly and the one or more previously detected anomalies;

    determining, by the device, a reporting score for the particular anomaly using the computed one or more distance scores, the computed similarity score and the computed one or more relevance scores;

    reporting, by the device, the particular anomaly to a user interface based on the determined reporting score;

    ranking, by the device, the distributed learning agent based on similarity scores between anomalies detected by the distributed learning agent; and

    causing, by the device, allocation of network resources to the distributed learning agent for reporting detected anomalies to the device and based on the ranking of the distributed learning agent.

View all claims
  • 2 Assignments
Timeline View
Assignment View
    ×
    ×