公开(公告)号：US20250055877A1

公开(公告)日：2025-02-13

申请号：US18230897

申请日：2023-08-07

Applicant: Bank of America Corporation

Inventor： Maneesh Sethia ,  Manimaran Sundaravel

IPC: H04L9/40

Abstract: A computing platform may train, using historical threat detection information, a threat detection model, which may configure the threat detection model to detect container threats for a plurality of containers deployed at a plurality of nodes on a cloud network. The computing platform may obtain, from a node monitoring system, operating conditions of the plurality of nodes. The computing platform may input, into the threat detection model, the operating conditions of the plurality of nodes, which may cause the threat detection model to identify a threat to at least one container deployed at the plurality of nodes. The computing platform may execute, based on identification of the threat, a security action to protect the at least one container. The computing platform may update, based on the operating conditions of the plurality of nodes and the threat, the threat detection model.

公开(公告)号：US20250106232A1

公开(公告)日：2025-03-27

申请号：US18473063

申请日：2023-09-22

Applicant: Bank of America Corporation

Inventor： Ngoc Anh Tran ,  Manimaran Sundaravel ,  Maneesh Kumar Sethia

IPC: H04L9/40 ,  G06N3/08

Abstract: A method includes intercepting requests, which are analyzed to identify authenticated and suspicious requests. The suspicious requests are grouped into request groups based on respective geolocation information. A rate of requests is determined for a request group. In response to determining that the rate of requests is less than or equal to a request rate threshold, parameters of a suspicious request of the request group are analyzed to determining values of the parameters. In response to determining that the value of the parameters do not match with respective malicious parameter values stored in a block list, the suspicious request is analyzed using a neural network to identify if the suspicious request is legitimate or malicious. In response to identifying that the suspicious request is malicious, a notification indicating that the suspicious request is identified as malicious is sent, and the values of the parameters are added to the block list.

公开(公告)号：US20250106238A1

公开(公告)日：2025-03-27

申请号：US18473000

申请日：2023-09-22

Applicant: Bank of America Corporation

Inventor： Ngoc Anh Tran ,  Manimaran Sundaravel ,  Maneesh Kumar Sethia

IPC: H04L9/40 ,  H04L41/16

Abstract: A method includes intercepting requests. The requests are analyzed to identify authenticated requests. Remaining requests are identified as suspicious requests. The suspicious requests are grouped into request groups based on respective geolocation information. A first rate of requests is determined for a first request group. In response to determining that the first rate of requests is less than or equal to a request rate threshold, parameters of a first suspicious request of the first request group are analyzed to determining values of the parameters. In response to determining that the values of the parameters are not within respective acceptable parameter value ranges, the first suspicious request is analyzed using a neural network to identify if the first suspicious request is legitimate or malicious. In response to identifying that the first suspicious request is malicious, a first notification indicating that the first suspicious request is identified as malicious is send.

公开(公告)号：US20250055862A1

公开(公告)日：2025-02-13

申请号：US18230839

申请日：2023-08-07

Applicant: Bank of America Corporation

Inventor： Manimaran Sundaravel ,  Maneesh Sethia

IPC: H04L9/40

Abstract: A computing platform may train, using historical node performance information and historical application parameter information, a node selection model, which may configure the model to select nodes for application cloud deployment. The computing platform may receive a request to deploy an application to a cloud network. The computing platform may select a node, of the plurality of nodes of the cloud network, to which the application should be deployed. The computing platform may queue, along with other applications scheduled for deployment to the plurality of nodes, the application for deployment to the node. After identifying that the application is first in the queue, the computing platform may deploy the application to the node of the cloud network, which may create, at the node, a container corresponding to the application.