Abstract: A method includes generating a static model for classifying transactions of a designated type, the static model being trained using predefined input data corresponding to a first set of features generic to transactions of the designated type, and generating a dynamic model for classifying transactions of the designated type, the dynamic model being trained using dynamic input data corresponding to a second set of features specific to subsets of transactions of the designated type. The method also includes combining the static and dynamic models to generate a combined model, detecting transactions of the designated type between client devices and an enterprise system, and utilizing the combined model to classify a given detected transaction between a given client device and the enterprise system as potentially malicious or benign. The method further includes modifying processing of the given detected transaction responsive to classifying the given detected transaction as potentially malicious.

Abstract: A method includes generating a static model for classifying transactions of a designated type, the static model being trained using predefined input data corresponding to a first set of features generic to transactions of the designated type, and generating a dynamic model for classifying transactions of the designated type, the dynamic model being trained using dynamic input data corresponding to a second set of features specific to subsets of transactions of the designated type. The method also includes combining the static and dynamic models to generate a combined model, detecting transactions of the designated type between client devices and an enterprise system, and utilizing the combined model to classify a given detected transaction between a given client device and the enterprise system as potentially malicious or benign. The method further includes modifying processing of the given detected transaction responsive to classifying the given detected transaction as potentially malicious.

Abstract: Techniques are provided for smoothing discretized values used, for example, for authentication or identity assurance. An illustrative method comprises obtaining at least one probability of transitioning between at least two discretized values of a given feature; computing a smoothed feature score for the given feature for a transition from a first one of the discretized values to a second one of the discretized values based on the probability of the transition from the first discrete value to the second discrete value; and performing one or more of authenticating a user and verifying an identity of a user based at least in part on the smoothed feature score. The probabilities of transitioning between the discretized values are optionally stored in a transition matrix. Feature scores for first and second discretized values are optionally weighted based on the probability of the transition from the first discrete value to the second discrete value.