Emc IP Holding Company LLC

Rsa Security Usa, LLC

Alex Zaslavsky

Sashka T Davis

Salah E Machani

Techniques are provided for determining reasons for unsupervised anomaly decisions. One method comprises obtaining values of predefined features associated with a remote user device; applying the predefined feature values to an unsupervised anomaly detection model that generates an unsupervised anomaly decision; applying the predefined feature values to a supervised anomaly detection model that generates a supervised anomaly decision; determining a third anomaly decision using the unsupervised anomaly decision; and determining reasons for the third anomaly decision by analyzing the supervised anomaly decision. The supervised anomaly detection model can be trained using the unsupervised anomaly decision and/or anomalous training data based on known anomalies. The third anomaly decision can be based on the supervised anomaly decision and the unsupervised anomaly decision using ensemble techniques.

Interpretable supervised anomaly detection for determining reasons for unsupervised anomaly decision

Techniques are provided for centralized processing of sensitive user data. One method comprises obtaining, by a service provider, values of predefined features based at least in part on personal information of a user, wherein the values of the predefined features are computed by the user; and processing, by the service provider, the values of the predefined features based on the personal information to detect one or more predefined anomalies associated with the user and/or a device of the user. The predefined anomalies comprise, for example, a risk anomaly, a security level anomaly, a fraud likelihood anomaly, an identity assurance anomaly, and/or a behavior anomaly. The predefined features relate to, for example, a location of the user and/or device-specific information for a device of the user.

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Brookline, MA, United States of America

Alex Zaslavsky