Bank of America Corporation

Maneesh Kumar Sethia

Abhijit Behera

Shailendra Singh

Saurabh Garg

Ngoc Anh Tran

Shahadat Hossain Mazumder

Sandeep Kumar Chauhan

Saurabh Arora

Manimaran Sundaravel

Gowri Sundar Suriyanarayanan

Ajay Kumar Gowni

Amit Mishra

Paul Martin Mattison, Sr

Yaksh Kumar Singh

Bhagat Kumar Allugubelly

Pavan Kumar Kanamarlapudi

Sivashalini Sivajothi

Rajasekhar Madala

Yingcai Peng

Sandeep Reddy Kothapeta

Sivashalini S

Madhusekhar Banda

Rajeswari Selvaraj

A computing platform may train, using historical threat detection information, a threat detection model, which may configure the threat detection model to detect container threats for a plurality of containers deployed at a plurality of nodes on a cloud network. The computing platform may obtain, from a node monitoring system, operating conditions of the plurality of nodes. The computing platform may input, into the threat detection model, the operating conditions of the plurality of nodes, which may cause the threat detection model to identify a threat to at least one container deployed at the plurality of nodes. The computing platform may execute, based on identification of the threat, a security action to protect the at least one container. The computing platform may update, based on the operating conditions of the plurality of nodes and the threat, the threat detection model.

Monitoring and preventing spoofing, tampering, and denial of service attacks on cloud containers

An acoustic fingerprinting system includes an initial request from an authentic user. The input comprises a password with a plurality of keys. A smart sensor measures sounds associated with the keys. The smart sensor converts the sounds into notes that correspond to the keys. The notes may be stored in a database. There may be a second request from a user to access the system. The second request may include a second input of the password. The second input may include a sound associated with the keys of the password. The sounds may be converted to second notes. The second notes may be compared to the notes stored in the database. The user may be assigned a score based on the comparison. In the event that the score is greater than a predetermined number, the user may be granted access to the system.

Apparatus for authentication leveraging acoustic profile parameters scored by generative artificial intelligence ('AI')

A computing platform may generate, using a generative AI model, voice based authentication prompts corresponding to a user. Upon receiving a registration request from the user, the computing platform may identify the voice based authentication prompts for the user. The computing platform may send, to a first computing device of the user, the voice based authentication prompts and may receive/store voice based authentication information corresponding to the voice based audio inputs. Based on receiving an access request, the computing platform may send the plurality of voice based authentication prompts, and may receive, from a second computing device, additional voice based audio inputs. The computing platform may score, based on the voice based authentication information, the additional voice based audio inputs. The computing platform may compare the score to a threshold. Based on identifying that the score fails to meet the threshold, the computing platform may initiate security actions.

Deep fake attack prevention for voice based authentication leveraging generative artificial intelligence (AI) and distributed ledger technology

This invention introduces a sophisticated system for deploying projects on cloud platforms, combining Unikernels, Cyber Security Mesh Architecture (CSMA), MQTT protocol with SHA256 encryption, an Unikernel Orchestration Rules Engine (UORE), generative AI, and an innovative caching mechanism. Unikernels offer a secure, isolated environment for applications, reducing overhead and boosting performance. CSMA provides extensive security through analytics, identity management, and policy enforcement. The MQTT protocol, secured with SHA256, ensures the integrity and confidentiality of communications. UORE automates deployment, integrating a TLS terminator and data management for streamlined operation. Generative AI proactively resolves deployment challenges, particularly for complex applications, while the caching mechanism enhances performance and efficiency by minimizing latency. This integrated approach automates and secures the deployment process, enabling scalable, efficient, and real-time project creation and deployment in the cloud, thereby addressing the key challenges of cloud application hosting.

Intelligent cognitive AI based secure protocol channel to create and deploy projects on demand in real time leveraging Unikernels

A device, during a first interaction in real time, accesses from optical cells a measurement of optical emissions generated by hardware components inside the device responsive to a data card interacting with the device, generates a first optical emission pattern based on the measurement, determines whether the first optical emission pattern matches a baseline optical emission pattern, upon determining a match, determines whether the data card is associated with prohibited interactions, upon determining the first data card is not associated with prohibited transactions, determines whether the device is connected to a malicious IP address, upon determining the device is not connected to a malicious IP address, approves the first interaction. The device declines the first interaction if the first optical emission pattern does not match the baseline optical emission pattern, the first data card is associated with prohibited interactions, or the device is connected to a malicious IP address.

System and method for improving device security based on optical absorption spectroscopy and artificial intelligence

An apparatus is provided, including a transaction card, having: a first microprocessor configured to exchange transaction data; a sensor configured to detect movement; and a second microprocessor in communication with the sensor. The second microprocessor may be configured to store arrays of hash values, representing a movement of a user and a card; obtain hash values based on observed movement patterns, and compare them to the stored values. Upon receiving a transaction request, the microprocessor may determine in real time whether the user is in locomotion and whether the card is being intentionally tapped by the user, based on the observed movement patterns. On this basis, the second microprocessor may determine whether to require an augmented authentication for the requested transaction.

Methods and systems for securing transactions

Systems, computer program products, and methods are described herein for flexible data routing in a cloud-based network consortium. The method includes initiating an XR display for display on a user device and, in response to a first control signal, generating a nonfungible token (NFT) and transmitting a data packet over a secured network. The method further includes initiating, via the XR display, a secure application programming interface based on the NFT, geographic data, and a unique XR identification value, based on one or more data parameters, identifying a preferred data path, and, in response to a second control signal, performing an encrypted data exchange via the preferred data path.

Touchless API for flexible data routing in a cloud-based network consortium

Systems and processes are disclosed to monitor and control toxic configuration in software container deployment. AI-based monitor in each node provides end-to-end fault management system that can detect, diagnose, classify, and suggest remediation actions for non-virtualized cloud-based misconfiguration vulnerabilities. Anomalies are diagnosed using pre-computed fault signatures and automated remediation is integrated with a cloud management stack. Multiple monitoring layers within nodes secure the container-based virtualization environment. Container-based 'CSTC security framework' virtualization technology provides security and identifies potential toxic configuration threats. CSTC security locates container-based systems at severe risk for DDOS attacks to kernel vulnerability/container breakout and ensures appropriate privilege configuration for user processes. Resource access limited based on policies and CSTC security profiles as well as provide AI-based monitoring of container runtime behavior to provide additional security layer that ensures safety and protection in container-based virtualization.

Monitor and control toxic configuration in container deployment

Systems, computer program products, and methods are described herein for system resiliency by aggregation with heterogenous communication networks. The method includes transmitting an interaction request associated with an interaction initiation device through a low earth orbit satellite network upon determining a cellular network associated with the interaction initiation device is unavailable. The method also includes receiving a plurality of data packets from the interaction initiation device. At least one of the plurality of data packets are received via the low earth orbit satellite network, and one or more of the plurality of data packets include identifying characteristics associated with the interaction initiation device. The method further includes consolidating the plurality of data packets received via the low earth orbit satellite network. The method still further includes causing an execution of the requested interaction by transmitting a confirmation indicator to the interaction initiation device via the low earth orbit satellite network.

System and method for interaction system resiliency by aggregation with heterogenous communication networks

A system includes a memory and a processor that validates an interaction between an initiating and a receiving device. An encrypted request is received to perform the interaction and the processor extracts an initiating device identification, a receiving device identification, and one or more numerical values associated with the interaction. Using this extracted information, the processor produces a verification key by applying a hash function and a verification checksum value using a checksum function. The verification key and verification checksum value are then compared with an authentication key and authentication checksum value that were produced in a same manner prior to the interaction being initiated. If it is determined that either the verification key and the authentication key do not have the same value, or verification checksum value and authentication checksum value do not have the same value, then the interaction is reversed.

System and method for validating an interaction using an agreement key

A method includes intercepting requests, which are analyzed to identify authenticated and suspicious requests. The suspicious requests are grouped into request groups based on respective geolocation information. A rate of requests is determined for a request group. In response to determining that the rate of requests is less than or equal to a request rate threshold, parameters of a suspicious request of the request group are analyzed to determining values of the parameters. In response to determining that the value of the parameters do not match with respective malicious parameter values stored in a block list, the suspicious request is analyzed using a neural network to identify if the suspicious request is legitimate or malicious. In response to identifying that the suspicious request is malicious, a notification indicating that the suspicious request is identified as malicious is sent, and the values of the parameters are added to the block list.

System and method for network intrusion detection using a neural network implemented by a cloud computing system

The disclosure provides a method that includes presenting a simulated user device in a virtual environment, and receiving user credentials in response to an avatar interacting with the simulated user device. The method includes comparing the user credentials to known user credentials. The method includes determining that the avatar is associated with the user based on the comparison. The method includes identifying a first prior interaction from a data log of interactions, where the first prior interaction is associated with a first digital item token, and identifying a second prior interaction from the data log of interactions, where the second prior interaction is associated with a second digital item token. The method includes retrieving the first digital item token and the second digital item token, and presenting the first digital item token and the second digital item token on the simulated user device.

System and method for validating a digital item token on a simulated user device

Provided herein is a spatial computing system. The system may include a spatial computing apparatus configured to receive an anchoring instruction and consequently anchor a virtual object to a physical object; an onboarding module configured to interface with an existing spatial computing software platform; a spatial telemetry extraction module configured to extract positioning metadata associated with the physical object; a spatial analyzer module configured to detect an anomaly in the anchoring instruction, wherein the anomaly may indicate a suspected attempt to glean personal information from an image of the physical object; an anchor validation module configured to monitor changes in anchoring instructions; a session management module configured to terminate a spatial computing session, upon receiving an indication of an anomaly; a spatial security rule orchestration module configured to block implementation of the anchoring instruction; and a deep learning module.

Apparatus and method to monitor and prevent malicious anchoring in a spatial computing environment

Systems, methods, and apparatus are provided for implementing a dynamically customized ATM payload. An ATM may extract a user identifier and home region during a session. The ATM may be a node on a distributed network and securely publish data to a distributed hash table. Extracted data may trigger smart contract logic including a transaction rule associated with the user and a transmission rule restricting network switches for payload delivery outside the home region. AI algorithms may generate a custom payload including the transaction rule and an interface adapted for the user home region. The payload may be pushed to the ATM via a set of spine-leaf switches based on the transmission rule. The payload may be deployed at the ATM during the session to modify a default ATM interface and a default ATM transaction rule. The ATM may purge the payload from memory when the session is terminated.

Dynamic custom automated teller machine (ATM) payload

Apparatus and methods for a li-fi payment network are provided. A customer's device may communicate, directly or through intermediate devices and computer systems, over a li-fi network with a point-of-sale terminal. A bill may be transmitted to the customer's device over the li-fi network. The customer may select an option to pay the bill. The selection, along with information to authorize and process the payment, may be transmitted over the li-fi network to the POS terminal. The POS terminal may process the payment and generate a receipt. The receipt may be transmitted to the customer's device over the li-fi network.

Growing community of inventors

Hyderabad Telangana, India

Maneesh Kumar Sethia