首页> 外国专利> EFFICACY MEASURES FOR UNSUPERVISED LEARNING IN A CYBER SECURITY ENVIRONMENT

EFFICACY MEASURES FOR UNSUPERVISED LEARNING IN A CYBER SECURITY ENVIRONMENT

机译:网络安全环境中未经监督的学习的有效措施

摘要

A method for performing searches of user information comprises: receiving an initial dataset including the user information; calculating distance metrics between pairs of data in the initial dataset; performing unsupervised learning on the initial dataset to obtain a plurality of total number of clusters, each total number of clusters covering the initial dataset; determining efficacy measures for each total number of clusters using a plurality of distance metric thresholds; determining a desired efficacy measure in the efficacy measures, the desired efficacy measure corresponding to a desired distance metric threshold in the plurality of distance metric thresholds; determining a desired total number of clusters in the plurality of total number of clusters, the desired total number of clusters corresponding to the desired distance metric threshold; and performing unsupervised learning on the initial dataset using the desired total number of clusters to obtain a number of data representations of the user information.
机译:一种执行用户信息搜索的方法,包括:接收包括用户信息的初始数据集;计算初始数据集中的数据对之间的距离度量;对初始数据集进行无监督学习,得到多个聚类总数,每个聚类总数覆盖初始数据集;使用多个距离度量阈值确定每个集群总数的功效度量;在所述功效度量中确定期望功效度量,所述期望功效度量对应于所述多个距离度量阈值中的期望距离度量阈值;确定所述多个集群总数中的期望集群总数,所述期望集群总数对应于所述期望距离度量阈值;并使用所需的群集总数在初始数据集上执行无监督学习,以获得用户信息的数据表示形式。

著录项

  • 公开/公告号US2020311596A1

    专利类型

  • 公开/公告日2020-10-01

    原文格式PDF

  • 申请/专利权人 AETNA INC.;

    申请/专利号US201916364663

  • 发明设计人 SALIL KUMAR JAIN;DAVID DYER;

    申请日2019-03-26

  • 分类号G06N20;G06K9/62;G06F21/31;

  • 国家 US

  • 入库时间 2022-08-21 11:22:27

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