Bank of America Corporation

Rama Krishnam Raju Rudraraju

Durgadatta Belhekar

Johny Shaik

Om Purushotham Akarapu

Daniel Darnadi Widjanarko

Jitendra Upadhyay

A system for optimizing anomaly detection determines, based on a confidence score, user clustering information that indicates a cluster to which a user belongs, such that if the confidence score is more than a threshold score, the user clustering information indicates that the user belongs to a first cluster. Otherwise, the user clustering information indicates that the user belongs to a second cluster. The system determines, based on user activities in a virtual environment, user outlier information that indicates whether the user is associated with an unexpected activity. The system determines virtual resource routing information that comprises routings of virtual resources between the avatar and the other avatars within the virtual environment. The system updates the confidence score based at least in part upon at least one of the user clustering information, the user outlier information, or the virtual resource routing information.

Optimizing anomaly detection based on user clustering, outlier detection, and historical data transfer paths

A system for implementing anomaly detection accesses user activities associated with an avatar in a virtual environment. The system extracts features from the user activities, where the features provide information about interactions of the avatar with other avatars and entities in the virtual environment. The system determines a deviation range for each feature, where the deviation range indicates a deviation between the features among the avatars over a certain period. The system determines whether the deviation range for a feature is more than a threshold deviation. If it is determined that a deviation range of a feature is more than the threshold deviation, a confidence score associated with the user is updated based on the deviation range of the feature. If the confidence score is more than a threshold score, the user is not associated with an anomaly. Otherwise, the user is determined to be associated with an anomaly.

System and method for anomaly detection for information security

Aspects of the disclosure relate to apparatuses, method steps, and systems for optimized Internet of Things (IoT) data processing for real-time decision support systems. The systems are used for real-time processing prioritization using a prioritization code and/or processing code. Edge devices may generate processing codes that are used in optimizing the data processing. For example, the system receives sensor data and preprocesses the sensor data with a simplified state estimation module to calculate a variance that is used to determine a processing code and/or a prioritization code.

Optimized IoT data processing for real-time decision support systems

A query generation system receives, from a first device, a first input and a first project identifier and receives, from a second device, a second input and a second project identifier. The first and second inputs are the same and are in a natural language format that is not compatible with a downstream database management system. The system generates, based on the first input, a first database query. The system generates, based on the second input, a second database query. The first and second database queries are compatible with the downstream database management system. The system receives a first response to the first database query and a second response to the second database query from the downstream database management system. The system transmits the first response to the first device and the second response to the second device.

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Mumbai, India

Rama Krishnam Raju Rudraraju