Bank of America Corporation

Joel Richard Townsend

Manu Jacob Kurian

Michael Robert Young

Tomas Mata Castrejon, Iii

David Nardoni

Peter George Kurrasch

William Anderson Hodges

John Raymond Omernik

Neal Aaron Slensker

Kevin P Thomsen

Daniel J August

A system is provided for remediation of security vulnerabilities in computing devices using continuous device-level scanning and monitoring. In particular, the system may perform a deep scan of the hardware and software elements of a computing device and/or application and compile the information from the deep scan into a hardware metadata list and a software metadata list associated with the computing device and/or application. The system may then, through a machine learning-based process, continuously scan the elements within the hardware metadata list and the software metadata list to identify the elements that are not involved in the operation of the computing device and/or application. The system may flag such elements for inspection to evaluate the safety of the elements and subsequently execute one or more remediation processes in response to detecting an unsafe element.

System for remediation of security vulnerabilities in computing devices using continuous device-level scanning and monitoring

A system is provided for detecting and remediating computing system breaches using computing network traffic monitoring. In particular, the system may identify one or more technology elements within a network as well as relationships between computing systems associated with said elements to determine a network topology. Based on the network topology, the system may use historical network traffic data associated with the technology elements in the network to generate predicted entry points and lateral pathways of a security breach that may take place within particular computing systems. Then, based on the technology elements affected as well as entry points and path traversals of the breach, the system may generate and/or implement one or more remediation steps to address existing and/or future breaches. In this way, the system may provide an intelligent method of augmenting the security of a computing network.

System for adaptive management of downstream technology elements

Systems, computer program products, and methods are described herein for dynamic communication channel switching for secure message propagation. The present invention may be configured to receive wireless signals from a plurality of devices and identify, from the plurality of devices and based on the wireless signals, a trusted device. The present invention may be configured to receive, from another device, a secure message, where the secure message includes information identifying a vulnerability in a network to which the trusted device is connected. The present invention may be configured to establish, based on receiving the secure message and using a first wireless communication interface, a communication link with a second wireless communication interface of the trusted device to establish a wireless data channel with the trusted device and transmit, via the wireless data channel, the secure message to the trusted device.

Systems and methods for dynamic communication channel switching for secure message propagation

Aspects of the disclosure relate to a payment device to detect real-time suspicious payment cards. Prior to a transaction, a payment device detects suspicious payment cards based on captured images and determined indicia of the payment card. An alert may be generated upon detection of any suspicious or fraudulent payment card. In some arrangements, the payment device may utilize machine learning models or machine learning capabilities to detect suspicious payment cards. A characterization score may be generated and used to determine if a payment card is suspicious. The characterization scores may be updated based on different card issuer criteria and transaction use of each payment card.

Detecting cloned payment cards

Aspects of the disclosure relate to a payment device to detect real-time suspicious payment cards. Prior to a transaction, a payment device detects suspicious payment cards based on captured images of the payment card. An alert may be generated upon detection of any suspicious or fraudulent payment card. In some arrangements, institutions may share information regarding suspicious payment cards to prevent fraud. The payment device may utilize machine learning models or machine learning capabilities to detect suspicious payment cards. A characterization score may be generated and used to determine if a payment card is suspicious. The characterization scores may be updated based on different card issuer criteria and transaction use of each payment card.

Aspects of the disclosure relate to a payment device to detect real-time suspicious payment cards. Prior to a transaction, a payment device detects suspicious payment cards based on captured images of the payment card. An alert may be generated upon detection of any suspicious or fraudulent payment card. In some arrangements, the payment device may utilize machine learning models or machine learning capabilities to detect suspicious payment cards. A characterization score may be generated and used to determine if a payment card is suspicious. The characterization scores may be updated based on different card issuer criteria and transaction use of each payment card.

An apparatus includes a memory and a processor. The memory stores biometric data obtained from an account holder at a first location and identification information provided by the account holder that was used to open an existing account at the first location. The processor receives, from a device, data corresponding to an applicant that includes biometric data obtained from the applicant at a second location and identification information provided by the applicant at the second location. The applicant is requesting to open a new account using the identification information at the second location. In response to receiving the applicant's data, the processor determines that the applicant's biometric data matches the account holder's biometric data. The processor also determines that the applicant's identification information does not match the account holder's identification information. In response, the processor transmits an alert to the device indicating that the applicant's identity could not be verified.

Identity verification through a centralized biometric database

Various systems, methods, and computer program products for providing data security using software library containers are provided. The method includes identifying a software library used by a first application with a potential vulnerability. The method also includes determining a software library permission for the software library used by the first application based on the potential vulnerability. The software library permission for the software library defines a level of functionality of at least a portion of the software library. The method further includes identifying one or more additional applications that use the software library. The method still further includes causing the software library permission to be applied to the software library installed on the first application and at least one of the one or more additional applications.

System and method for providing data security using software library containers

Systems, computer program products, and methods are described herein for deployable software vulnerability testing platform. The present invention is configured to receive, from a user input device, an initial request from a user to generate a deployable software vulnerability testing (SVT) engine; prompt an SVT dashboard for display on the user input device to receive one or more input parameters associated with the first vulnerability in response to receiving the initial request; receive, via the SVT dashboard, the one or more input parameters; generate the SVT engine based on at least the one or more input parameters; receive, from the user input device, one or more deployment parameters associated with a deployment of the SVT engine; and deploy the SVT engine within the distributed technology infrastructure to identify the one or more instances of the first vulnerability based on at least the one or more deployment parameters.

System for deployable software vulnerability testing platform

Systems, computer program products, and methods are described herein for component-level detection in third party software applications. An example system may receive, from a client device, data packets with payload containing a request to onboard a first application into a computing environment. In response, the system may determine, using an application ingestion subsystem, information associated with the first application, and subsequently deploy, using a machine learning subsystem, a trained machine learning model on the information to various components associated with the first application and component scanning parameters for each of the one or more components. Based on components, the system may build a software bill of materials (SBOM) for the first application.

System for enhanced component-level detection in software applications within a computing environment

Detection and notification of malware at a user device may be performed by a validation server. The user device may hash elements associated with a document object model of a webpage and send generated hash values to the validation server. The validation server may validate the hash values. Based on detection of hash values corresponding to elements maliciously-injected by malware, the validation server may send one or more notifications to other servers that may communicate with the user device.

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Spring Church, PA, United States of America

Joel Richard Townsend