公开(公告)号：US20250156748A1

公开(公告)日：2025-05-15

申请号：US18508472

申请日：2023-11-14

Applicant: Bank of America Corporation

Inventor： Anurajam Rajagopalan ,  Pushkar Taneja ,  Durga Prasad Kutthumolu

IPC: G06N20/00 ,  H04L9/40

Abstract: A method includes receiving and analyzing data items to generate a weight for each data item. In response to determining that a first weight of a first data item is within a first weight range, the method determines that the first data item has a high security level. In response to determining that a second weight of a second data item is within a second weight range, the method determines that the second data item has a medium security level. A first subset of the data items having the high security level are encrypted with a first cryptography algorithm to generate first encrypted data items. A second subset of the data items having the medium security level are encrypted with a second cryptography algorithm to generate second encrypted data items. An artificial intelligence/machine learning model is trained using the first and second encrypted data items as a training data set.

公开(公告)号：US20250071027A1

公开(公告)日：2025-02-27

申请号：US18237745

申请日：2023-08-24

Applicant: Bank of America Corporation

Inventor： Sakshi Bakshi ,  Pushkar Taneja ,  Subhasree Baskaran ,  Durga Prasad Kutthumolu

IPC: H04L41/16 ,  H04L9/40 ,  H04L41/069

Abstract: Arrangements for an intelligent monitoring platform using a cybersecurity mesh and graph neural networks (GNNs) are provided. A platform may train multiple machine learning models (e.g., a GNN model, a cybersecurity engine, and a monitoring model). The platform may generate, using a GNN model, a suspicion score for a received event processing request. Based on determining the suspicion score satisfies a threshold, the platform may generate a threat score using a cybersecurity engine. The platform may generate an anomaly record for the event processing request based on the threat score and using a monitoring model. The platform may determine a preferred node of a cybersecurity mesh for routing the event processing request based on the anomaly record. The platform may determine a threat prevention response based on the preferred node. The platform may initiate one or more security actions based on the threat prevention response.

公开(公告)号：US20250111042A1

公开(公告)日：2025-04-03

申请号：US18376298

申请日：2023-10-03

Applicant: BANK OF AMERICA CORPORATION

Inventor： Sakshi Bakshi ,  Sudha Ganapathy ,  Durga Prasad Kutthumolu ,  Pushkar Taneja

IPC: G06F21/55

Abstract: Embodiments of the present invention provide a system for generating data models secure from malfeasant manipulation for use in predictive modeling. The system is configured for retrieving training data associated with predictive modeling from a data source, processing the training data retrieved from the data source, transmitting the training data to a local linear model to generate a predictive output, retrieving historical data from the data source, transmitting the predictive output from the local linear model and the historical data retrieved from the data source to a Huber loss estimator module, validating, via the Huber loss estimator module, the predictive output received from the local linear model based on the historical data retrieved from the data source, and determining, via the Huber loss estimator module, if the training data has been manipulated by a malfeasant actor based on validating the one or more data points associated with the predictive output.