公开(公告)号：US11563744B2

公开(公告)日：2023-01-24

申请号：US17181608

申请日：2021-02-22

Applicant: BANK OF AMERICA CORPORATION

Inventor： Scott Anderson Sims ,  Jeffrey Brian Bashore ,  Jeffrey David Finocchiaro ,  Craig Douglas Widmann

IPC: H04L9/40 ,  G06N20/00 ,  G06K9/62

Abstract: Systems, computer program products, and methods are described herein for detection and classification of intrusion using machine learning techniques. The present invention is configured to electronically receive, from a computing device of a user, an indication that the user has initiated a first resource interaction; retrieve information associated with the first resource interaction, wherein the information comprises at least one or more parameters associated with the first resource interaction; initiate a machine learning model on the one or more parameters associated with the first resource interaction; and classify, using the machine learning model, the first resource interaction into one or more classes, wherein the one or more classes comprises one or more access types.

公开(公告)号：US11985136B2

公开(公告)日：2024-05-14

申请号：US18072526

申请日：2022-11-30

Applicant: BANK OF AMERICA CORPORATION

Inventor： Scott Anderson Sims ,  Jeffrey Brian Bashore ,  Jeffrey David Finocchiaro ,  Craig Douglas Widmann

IPC: H04L9/40 ,  G06F18/24 ,  G06N20/00

CPC classification number: H04L63/102 ,  G06F18/24 ,  G06N20/00 ,  H04L63/1441 ,  H04L63/20

Abstract: Systems, computer program products, and methods are described herein for detection and classification of intrusion using machine learning techniques. The present invention is configured to electronically receive, from a computing device of a user, an indication that the user has initiated a first resource interaction; retrieve information associated with the first resource interaction, wherein the information comprises at least one or more parameters associated with the first resource interaction; initiate a machine learning model on the one or more parameters associated with the first resource interaction; and classify, using the machine learning model, the first resource interaction into one or more classes, wherein the one or more classes comprises one or more access types.

公开(公告)号：US20220272093A1

公开(公告)日：2022-08-25

申请号：US17181608

申请日：2021-02-22

Applicant: BANK OF AMERICA CORPORATION

Inventor： Scott Anderson Sims ,  Jeffrey Brian Bashore ,  Jeffrey David Finocchiaro ,  Craig Douglas Widmann

IPC: H04L29/06 ,  G06K9/62 ,  G06N20/00

Abstract: Systems, computer program products, and methods are described herein for detection and classification of intrusion using machine learning techniques. The present invention is configured to electronically receive, from a computing device of a user, an indication that the user has initiated a first resource interaction; retrieve information associated with the first resource interaction, wherein the information comprises at least one or more parameters associated with the first resource interaction; initiate a machine learning model on the one or more parameters associated with the first resource interaction; and classify, using the machine learning model, the first resource interaction into one or more classes, wherein the one or more classes comprises one or more access types.

公开(公告)号：US20230089968A1

公开(公告)日：2023-03-23

申请号：US18072526

申请日：2022-11-30

Applicant: BANK OF AMERICA CORPORATION

Inventor： Scott Anderson Sims ,  Jeffrey Brian Bashore ,  Jeffrey David Finocchiaro ,  Craig Douglas Widmann

IPC: H04L9/40 ,  G06N20/00 ,  G06K9/62

Abstract: Systems, computer program products, and methods are described herein for detection and classification of intrusion using machine learning techniques. The present invention is configured to electronically receive, from a computing device of a user, an indication that the user has initiated a first resource interaction; retrieve information associated with the first resource interaction, wherein the information comprises at least one or more parameters associated with the first resource interaction; initiate a machine learning model on the one or more parameters associated with the first resource interaction; and classify, using the machine learning model, the first resource interaction into one or more classes, wherein the one or more classes comprises one or more access types.