公开(公告)号：US20190342173A1

公开(公告)日：2019-11-07

申请号：US15969462

申请日：2018-05-02

Applicant: Cisco Technology, Inc.

Inventor： Laurent Navarro ,  Jeffrey Markey ,  Matthew Robertson ,  Sunil Amin ,  Marc Dupont ,  Timothy Deeb-Swihart, II

IPC: H04L12/24 ,  H04L12/851 ,  H04L29/06

Abstract: In one example embodiment, a server obtains network flow metadata of a network flow of a host in a network. The server identifies one or more attributes of the network flow metadata. For each host group of a plurality of host groups, the server determines whether the one or more attributes of the network flow metadata satisfy one or more criteria for the host group. For each host group for which it is determined that the one or more attributes of the network flow metadata satisfy the one or more criteria, the server classifies the host as belonging to the host group.

公开(公告)号：US11157834B2

公开(公告)日：2021-10-26

申请号：US16031206

申请日：2018-07-10

Applicant: Cisco Technology, Inc.

Inventor： Timothy David Keanini ,  Jeffrey Markey ,  Jesse Craig-Goodell

IPC: G06F7/00 ,  G06N20/00 ,  G08C17/02

Abstract: In one example embodiment, a server compares derived values of a plurality of machine learning features with specified values of the plurality of machine learning features according to an ontology that defines a relationship between the plurality of machine learning features and a corresponding higher-order behavior for the specified values of the plurality of machine learning features. When the derived values of the plurality of machine learning features match the specified values of the plurality of machine learning features, the server aggregates the weights of the plurality of machine learning features to produce an aggregated weight. The server assigns the aggregated weight to the higher-order behavior so as to indicate a significance of the higher-order behavior in producing the machine learning result.

公开(公告)号：US20200019889A1

公开(公告)日：2020-01-16

申请号：US16031206

申请日：2018-07-10

Applicant: Cisco Technology, Inc.

Inventor： Timothy David Keanini ,  Jeffrey Markey ,  Jesse Craig-Goodell

IPC: G06N99/00 ,  G08C17/02

Abstract: In one example embodiment, a server compares derived values of a plurality of machine learning features with specified values of the plurality of machine learning features according to an ontology that defines a relationship between the plurality of machine learning features and a corresponding higher-order behavior for the specified values of the plurality of machine learning features. When the derived values of the plurality of machine learning features match the specified values of the plurality of machine learning features, the server aggregates the weights of the plurality of machine learning features to produce an aggregated weight. The server assigns the aggregated weight to the higher-order behavior so as to indicate a significance of the higher-order behavior in producing the machine learning result.