Bank of America Corporation

Marci Landy

Elizabeth R Liuzzo

Michael Beveridge

Daniel Horne

George Albero

Olga Kocharyan

Daniel Joseph Serna

Cody Dean Searl

Keaton Drake

Harry Kirby

Tina Berumen Pachorek

Jessica Hope Thompson

Joseph Henry Pindell, Jr

Mrunal Mody

Robert A Levandowski

Julius D Jeffreys

Anthony D Pedretti

Systems and methods for automating the generation of a strong, secure, and memorable password using gamification. Systems may include a computer configured to receive words and phrases from a user using a gamification tool, use a machine learning model to eliminate words and phrases found when searching open-source intelligence (OSINT) available about the user, use the machine learning model to identify portions of the non-eliminated units of information, and use the machine learning model to combine the portions into memorable password options. Systems may include the computer configure to use the machine learning model to eliminate any of the password options found when searching OSINT, present a subset of the non-eliminated password options to the user, receive the selected password from the user, use the machine learning model to generate an obfuscated reminder for the selected password, and provide the obfuscated reminder to the user.

Memorable secure, and strong password generator enhanced with gamification and machine learning

Systems and methods for automating generation of a strong and memorable password. Systems may include a computer configured to receive units of information from the user, use a machine learning model to eliminate units of information found when searching open-source intelligence (OSINT) available about the user, use the machine learning model to identify portions of the non-eliminated units of information, and use the machine learning model to combine the portions into memorable password options. Systems may include the computer configure to use the machine learning model to eliminate any of the password options found when searching OSINT, present a subset of the non-eliminated password options to the user, receive the selected password from the user, use the machine learning model to generate an obfuscated reminder for the selected password, and provide the obfuscated reminder to the user.

Memorable and strong password generator automated with a machine learning model

An illustrative computing system for a weblink content scanning system scans an electronic message for the presence of one or more weblinks. The computing system accesses, in a sandbox computing environment, content linked to the one or more weblinks. The computing system generates a hash of the accessed content and/or content linked to weblinks accessible via the accessed content. The computing system scans the content accessed via the one or more weblinks for a presence of malicious content and categorizes the scanned content accessed via the one or more weblinks (e.g., safe, malicious, and the like), associates the categorization with each corresponding hash, and saves such information to a data store for future analysis. Based on a result of this analysis, the computing system allows delivery of the original electronic message or generates a modified electronic message for delivery to a recipient device.

Scanning of content in weblink

Systems, computer program products, and methods are described herein for verifying authentication credentials in an electronic network. The present invention is configured to receive a first IP address associated with a first access attempt; determine a first geolocation data based on the first IP address; receive a second IP address associated with a second access attempt; determine a second geolocation data based on the second IP address; determine a geolocation variance between the first and the second geolocation data; determine, based on the geolocation variance, an indication of potential movement between the access attempts; apply, based on the indication of potential movement, a verification machine learning model to the account identifier; generate, by the verification machine learning model, a privacy score; and generate, based on the privacy score, an alert user interface component to configure a GUI of a device associated with the user of the account.

Systems, methods, and apparatuses for verifying authentication credentials in an electronic network

Systems, computer program products, and methods are described herein for identifying unauthorized use of a user's authentication credentials to an electronic network based on non-public data access. The present invention is configured to receive a verified access attempt at a first time for a user account; receive an unverified access attempt at a second time for the user account; determine the unverified access attempt is a credential sharing event for the user account; determine the user account is an internal account; determine an unverified user associated with the unverified access attempt is an external user; receive unverified account access logs associated with the unverified access attempt, the unverified account access logs comprising access to non-public data; and generate an unverified data access interface component to configure a graphical user interface of a device associated with a manager of the system.

Systems, methods, and apparatuses for identifying unauthorized use of a user's authentication credentials to an electronic network based on non-public data access

Aspects of the disclosure relate to preventing unauthorized screen capture activity. A computing platform may detect, via an infrared sensor associated with a computing device, an infrared signal from a second device attempting an unauthorized image capture of contents being displayed by a display device of the computing device. Subsequently, the computing platform may determine, via the computing platform, the contents being displayed by the display device. Then, the computing platform may retrieve a record of the contents being displayed by the display device. Then, the computing platform may determine a risk level associated with the infrared signal. Subsequently, the computing platform may perform, via the computing platform and based on the risk level, a remediation task to prevent the unauthorized image capture.

Preventing unauthorized screen capture activity

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Waxhaw, NC, United States of America

Marci Landy