公开(公告)号：US10931703B2

公开(公告)日：2021-02-23

申请号：US16003987

申请日：2018-06-08

Applicant: ProSOC, Inc.

Inventor： Ken Adamson ,  Jordan Knopp ,  Bradley Houston Taylor

IPC: H04L9/00 ,  H04L29/06 ,  H04L12/26

Abstract: Embodiments of the disclosure are related to a method, apparatus, and system for generating scores for the security threat coverage in a client network based on collected network environment data, comprising: determining a client device list; creating a client-specific threat matrix based on the client device list and a general threat matrix; and determining one or more security threat coverage scores for the client network based on the client-specific threat matrix.

公开(公告)号：US12301602B2

公开(公告)日：2025-05-13

申请号：US17946880

申请日：2022-09-16

Applicant: ProSOC, Inc.

Inventor： Jordan Knopp ,  Bradley Houston Taylor ,  Brad Catcott

IPC: H04L9/40

Abstract: Embodiments of the disclosure are related to a method, apparatus, and system for identity threat detection and response for a client computer network including: collecting network security logs for the client computer network; monitoring the network security logs; generating an alert if a condition of the network security logs matches a correlation rule or an anomaly is determined to meet a predefined condition; and, based upon the alert, initiating an automated response including disabling a user account of the client computer network.

公开(公告)号：US20230267340A1

公开(公告)日：2023-08-24

申请号：US17675704

申请日：2022-02-18

Applicant: ProSOC, Inc.

Inventor： Kristopher Chesney ,  Jordan Knopp

IPC: G06N5/02 ,  G06F16/2455 ,  G06F16/18

CPC classification number: G06N5/022 ,  G06F16/24564 ,  G06F16/1805

Abstract: Embodiments of the disclosure are related to a method, apparatus, and system for multi-tenancy machine-learning based on collected data from multiple clients, comprising: obtaining client data from multiple clients; sending the client data from the multiple clients to a database; pulling data from the database by a machine learning job based on job parameters; partitioning the data by each client for the machine learning job; analyzing the data from the multiple clients by the machine learning job; sending the results of the analysis of the data from the multiple clients by the machine learning job back to the database; querying the database for data specified by rules; and if rules are met by the queried data for one or more of the multiple clients, transmit an alert to an alerting platform.