Bank of America Corporation

Benjamin F Tweel

Jack Lawson Bishop, Iii

Daniel Joseph Serna

James J Siekman

Jake Michael Yara

Robert Cain Durbin, Jr

Jo-Ann Taylor

Marcus Raphael Matos

Michael James Sbandi

Neal Aaron Slensker

Michael Robert Young

Gilbert M Gatchalian

Vijaya L Vemireddy

Richard Scot

Sanjay Arjun Lohar

Erik Dahl

Dinesh Kumar Agrawal

Steven Nathan Greene

John Raymond Omernik

Rebecca Lynn Pietro

Jennifer T Renckert

Frank J Yanan

Jeffrey Kyle Johnson

Shannon Sabina Rolinger

Basil C Doyle

Chanda Joyner

Sheng Tang Hsiang

Julie Stanley

Jack Franklin Price, Jr

Anna Burchman

Systems, computer program products, and methods are provided for code revision impact analysis. The method includes generating a system map based on data received from a plurality of network devices; receiving a data transmission including a text file; processing the text file via a natural language processing engine, where an output of the natural language processing engine comprises a plurality of expected updates; determining, based on the system map, at least one downstream effect of the plurality of expected updates; and performing a remedial action.

Natural language processing engine for component level data mapping and code revision impact analysis

Systems, computer program products, and methods are provided for automated detection of source code discrepancies. The method includes receiving a data transmission including a text file and a source code file; processing the source code file via a machine learning engine, where an output of the machine learning engine includes a plurality of identified updates; processing the text file via a natural language processing engine, where an output of the natural language processing engine includes a plurality of expected updates; identifying a difference between the plurality of identified updates and the plurality of expected updates; and performing a remedial action.

Natural language processing engine for automated detection of source code discrepancies

Aspects of the disclosure relate to a collaborative verified user platform using distributed ledger technology. A collaborative verified user platform may receive user information from a first user device. The platform may store the user information to a distributed ledger. The platform may update the distributed ledger based on one or more changes to the user information. The platform may receive a request to access the user information. The platform may determine whether to grant access to the user information based on one or more permissions. The platform may establish a secure channel between a first user device and a second user device. The platform may process secure requests associated with a venture corresponding to both the first user device and to the second user device. The platform may detect a permission violation. The platform may initiate one or more security actions based on the permission violation.

Collaborative verified user platform using distributed ledger technology

Arrangements for detecting unauthorized activity based on input method analysis and monitoring are provided. In some aspects, identity information associated with a user may be received and be stored. An input may be received from a computing device of the user. An input pattern of the received input may be determined. Using a machine learning model, the input pattern of the received input may be compared to input patterns of humans and input patterns of machines. Based on the comparison, it may be determined whether the user is a human user or a non-human user. Responsive to determining that the user is a non-human user, a request may be transmitted to the user to provide increased authentication credentials. Responsive to determining that the user is a human user, an identity of the user may be verified by comparing the input pattern of the received input to the stored identity information.

Unauthorized activity detection based on input analysis and monitoring

Arrangements for haptic feedback control are provided. In some aspects, registration data may be received from a user. For instance, a user may identify one or more devices (e.g., payment devices such as credit or debit cards, mobile devices, or the like) associated with the user. In some examples, the user may also select customized haptic feedback associated with different feedback outputs. After receiving registration data, the computing platform may receive a request to process a transaction. The transaction may be initiated at a point-of-sale system of an entity via the payment device or card of the user. Transaction details may be analyzed to determine or identify a feedback output (e.g., approved, denied, request additional information or the like). Based on the identified feedback output, a feedback output command or instruction may be generated and transmitted to the payment device for execution.

Event processing system having haptic feedback

Systems, computer program products, and methods are described herein for analyzing micro-anomalies in anonymized electronic data. The present disclosure is configured to import or retrieve a first data set, process the first data set to develop at least one event-outcome projection, define an outcome projection data set, import or receive a monitored user data set, anonymize the monitored user data set, define an avatar data set process the avatar data set, wherein the steps of import or receive a monitored user data set, anonymize the monitored user data set, and define an avatar data set are repeated one or more times.

System and method for analyzing micro-anomalies in anonymized electronic data

Arrangements for comprehensive threat mitigation are provided. In some aspects, an indication of threat or potential threat may be received from an external entity. In some examples, the threat may include a cybersecurity threat. In response to receiving the indication of threat, identifying data associated with the external entity may be extracted from the indication and used to retrieve pre-stored customizations associated with the desired mitigating actions of the external entity. The one or more mitigating actions may be identified and instructions to execute the one or more mitigating actions may be generated and transmitted to one or more computing devices for execution.

Multi-computer system for comprehensive threat detection and mitigation

An information security system that includes an information security engine configured to detect an attack by a malicious software element in a network. The information security engine is further configured to transfer the malicious software element from the network to the emulated network in response to detecting an attack. The information security engine is further configured to select defense strategies for restricting communications using different port configurations and to implement each defense strategy within the emulated network. The information security engine is further configured to execute a duplicate of the malicious software element in the emulated network and to determine a performance level for each of the defense strategies against the duplicate of the malicious software element. The information security engine is further configured to select a defense strategy with a highest performance level and to implement the selected defense strategy within the network.

Network threat prevention and information security using machine learning

An information security system that includes an information security engine configured to monitor data transmissions within a network and to detect a first attack by a malicious software element. The information security engine is further configured to detect a second attack by the malicious software element within a predetermined time interval from the first attack and to transfer the malicious software element from the network to an emulated network in response to detecting the second attack. The information security engine is further configured to generate an attack log comprising behavior characteristics for attacks performed by the malicious software element in the emulated network and to train a machine learning model based on behavior characteristics from the attack log.

Network threat detection and information security using machine learning

Growing community of inventors

Romeoville, IL, United States of America

Benjamin F Tweel