Abstract: Systems and methods for implementing network security are disclosed. These systems and methods may identify anomalous computer network activity in an online networked environment based on computer network data associated with a user's activity.

Abstract: Embodiments disclosed herein provide a behavioral based solution to user identity validation, useful in real-time detection of abnormal activity while a user is engaged in an online transaction with a financial institution. A risk modeling system may run two distinct environments: one to train machine learning algorithms to produce classification objects and another to score user activities in real-time using these classification objects. In both environments, activity data collected on a particular user is mapped to various behavioral models to produce atomic elements that can be scored. Classifiers may be dynamically updated in response to new behavioral activities. Example user activities may include login, transactional, and traverse. In some embodiments, depending upon configurable settings with respect to sensitivity and/or specificity, detection of an abnormal activity or activities may not trigger a flag-and-notify unless an attempt is made to move or transfer money.