Method and apparatus for network fraud detection and remediation through analytics

Abstract

A system and method for assessing the identity fraud risk of an entity's (a user's, computer process's, or device's) behavior within a computer network and then to take appropriate action. The system uses real-time machine learning for its assessment. It records the entity's log-in behavior (conditions at log-in) and behavior once logged in to create an entity profile that helps identify behavior patterns. The system compares new entity behavior with the entity profile to determine a risk score and a confidence level for the behavior. If the risk score and confidence level indicate a credible identity fraud risk at log-in, the system can require more factors of authentication before log-in succeeds. If the system detects risky behavior after log-in, it can take remedial action such as ending the entity's session, curtailing the entity's privileges, or notifying a human administrator.