US 12,464,019 B2
Optimizing anomaly detection based on user clustering, outlier detection, and historical data transfer paths
Rama Krishnam Raju Rudraraju, Telangana (IN); and Om Purushotham Akarapu, Telangana (IN)
Assigned to Bank of America Corporation, Charlotte, NC (US)
Filed by Bank of America Corporation, Charlotte, NC (US)
Filed on Jul. 29, 2022, as Appl. No. 17/815,965.
Prior Publication US 2024/0036900 A1, Feb. 1, 2024
Int. Cl. H04L 9/40 (2022.01)
CPC H04L 63/1483 (2013.01) [H04L 63/1425 (2013.01)] 17 Claims
OG exemplary drawing
 
1. A system for optimizing anomaly detection, comprising:
a memory configured to store:
user activities associated with a user within a virtual environment, wherein the user activities comprise one or more interactions between an avatar associated with the user and at least one other avatar within the virtual environment, wherein the virtual environment comprises a visual representation of a location;
a confidence score associated with the user, wherein the confidence score indicates whether the user is associated with an anomaly, wherein the anomaly represents an anomalous activity performed within the virtual environment, such that:
when the confidence score is more than a threshold score, the user is not associated with the anomaly; and
when the confidence score is less than the threshold score, the user is associated with the anomaly;
a processor operably coupled with the memory, and configured to:
determine, based at least in part upon the confidence score, user clustering information that indicates a cluster to which the user belongs, wherein:
in response to determining that the confidence score is more than the threshold score, the user clustering information indicates that the user belongs to a first cluster; and
in response to determining that the confidence score is less than the threshold score, the user clustering information indicates that the user belongs to a second cluster;
determine, based at least in part upon the user activities, user outlier information that indicates whether the user is associated with an unexpected user activity, wherein the unexpected user activity comprises performing more than a threshold number of interactions with the at least one other avatar after not accessing the virtual environment for more than a threshold period;
determine virtual resource routing information that comprises routings of virtual resources between the avatar and the other avatars within the virtual environment, wherein the virtual resources comprise a virtual file;
update 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;
determine that the user requests to perform an interaction with an entity in the virtual environment;
determine whether the confidence score is more than the threshold score; and
in response to determining that the confidence score is more than the threshold score, authorize the user to perform the interaction with the entity.