公开(公告)号：US11349911B1

公开(公告)日：2022-05-31

申请号：US17230150

申请日：2021-04-14

Applicant: AT&T Intellectual Property I, L.P.

Inventor： Brian Miles ,  Chad Hiestand ,  Anthony Librera ,  William Trost

IPC: H04L12/24 ,  H04L67/101 ,  G06N20/00 ,  H04L41/16 ,  H04L67/1029

Abstract: A system can receive a guardrail policy request that specifies a guardrail policy to assess for deployment on a server to protect at least a specific port of the server. The system can execute a fingerprint clustering machine learning model using server fingerprint data to generate cluster data that identifies a virtual machine cluster that includes a plurality of virtual machines executed by the server. The system can execute a traffic discovery machine learning model using server traffic data and the cluster data to generate a confidence score indicative of whether deployment of the guardrail policy would have an adverse impact on the server. The system can execute a risk assessment machine learning model using the application type data to generate a risk assessment score. The system can evaluate the confidence score and the risk assessment score and can determine whether the guardrail policy should be deployed on the server.

公开(公告)号：US20210037061A1

公开(公告)日：2021-02-04

申请号：US16527409

申请日：2019-07-31

Applicant: AT&T Intellectual Property I, L.P.

Inventor： William R. Trost ,  Chad Hiestand ,  David FengLin Chen ,  Anthony Librera ,  Brian Miles

IPC: H04L29/06 ,  G06N20/00

Abstract: Methods, systems, and apparatuses, may manage machine learned security for computer program products, which may create dynamic micro-perimeters.