Bank of America Corporation

Ra Uf Ridzuan Bin Ma Arof

Harish Tammaji Kulkarni

Kumudini Choyal

Nhat Minh Nguyen

Hemant Meenanath Patil

Surendran Surendran

Pallavi Yerra

Min Cao

Yuvraj Singh

Benjamin John Ansell

Kaushik Mitra Chowdhury

Surendran

Aspects of the disclosure relate to multi-factor user authentication for card-based payment transactions using blockchain tokens. An computing platform may receive, from a computing device, transaction details associated with a card-based payment transaction corresponding to a user, wherein the transaction details comprise a card number of a payment card. The computing platform may determine, based on the card number, a user device associated with the user. The computing platform may send, to the user device, a one-time passcode (OTP). After sending the OTP, the computing platform may receive a security key. The security key may be generated based on the sent OTP and a blockchain token hash. The computing platform may, based on the received security key, send, to the computing device, a message indicating whether the transaction is approved or declined.

Multi-factor user authentication using blockchain tokens

A system for multi-factor user authentication for payment card-based transactions are described. The multifactor authentication may be based on a one-time passcode/password (OTP) as sent by an authentication server. A user device may, based on receiving the OTP, send an authentication code. The authentication code may be generated/determined based on the OTP. The authentication code may be augmented biometric identifier (ID) of a user associated with the user device. The authentication server may validate a transaction based on the authentication code.

Multi-factor user authentication

Systems, methods, and apparatus are provided for intelligent cybersecurity processing of a product update. A fetcher application may access an updated version of a software product, a prior version of the product, and a version control system for the product. A malicious software identification engine may process the data using batch or stream processing to identify suspect code and metadata anomalies in the updated version. The engine may decompile executable binary code to obtain source code for the updated version and the prior version. A machine learning engine may receive input from the malicious software identification engine and classify the input using an NER-based machine learning model. Based on output from the machine learning engine, a control dashboard may block installation of a malicious product update.

Intelligent detection of cyber supply chain anomalies

An apparatus includes a memory and a processor. The memory stores descriptions of known vulnerabilities and information generated by a monitoring subsystem. Each description of a known vulnerability identifies software components that are associated with the known vulnerability. The monitoring subsystem monitors software programs that are installed within a computer system. The information includes descriptions of issues that are associated with the software programs. The processor generates a set of mappings, based on a comparison between the text describing the known software vulnerabilities and the text describing the issues. Each mapping associates a software program that is associated with an issue with a known software vulnerability. The processor also uses a machine learning algorithm to predict that a given software program is associated with a particular software vulnerability. In response to predicting that the given software program is associated with the particular software vulnerability, the processor generates an alert.

System and method for automatically identifying software vulnerabilities using named entity recognition

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Singapore, Singapore

Ra Uf Ridzuan Bin Ma Arof