Microsoft Technology Licensing, LLC

Dana Scott Kaufman

Pui-Yin Winfred Wong

Laurentiu Bogdan Cristofor

David Steeves

Robert Alexander Sim

Mark A Nikiel

Luke Abrams

Maria Puertas Calvo

Harry Simon Katz

Aaron Small

Adrian Kreuziger

Alexa Lynn Keizur

Collin Tibbetts

Charles Hayden

Winfred Wong

Sergio Romero Zambrano

Andrew Numainville

Pui Yin Winfred Wong

Alexandre Kerametlian

Amit Dhariwal

Abbinayaa Subramanian

Lakshmi Priya Gopal

Eliza Kuzmenko

To detect identity spray attacks, a machine learning model classifies account access attempts as authorized or unauthorized, based on dozens of different pieces of information (machine learning model features). Boosted tree, neural net, and other machine learning model technologies may be employed. Model training data may include user agent reputation data, IP address reputation data, device or agent or location familiarity indications, protocol identifications, aggregate values, and other data. Account credential hash sets or hash lists may serve as model inputs. Hashes may be truncated to further protect user privacy. Classifying an access attempt as unauthorized may trigger application of multifactor authentication, password change requirements, account suspension, or other security enhancements. Statistical or heuristic detections may supplement the model. However, the model may adapt to changed attacker behavior through retraining with updated data, making the model-based approach more effective over time than rigid statistical or heuristic detection approaches.

Identity spray attack detection with adaptive classification

Methods, systems, and computer program products are provided for real-time compromise detection based on behavioral analytics. The detection runs in real-time, during user authentication, for example, with respect to a resource. The probability that the authentication is coming from a compromised account is assessed. The features of the current authentication are compared with the features from past authentications of the user. After comparison, a match score is generated. The match score is indicative of the similarity of the authentication to the user's history of authentication. This score is then discretized into risk levels based on the empirical probability of compromise based on known past compromised user authentications. The risk levels may be used to detect whether user authentication is occurring via compromised credentials.

Systems and methods for real-time detection of compromised authentication credentials

Embodiments are directed to having multiple lockout counters that apply to login requests from different origins. More specifically, one counter is associated with a user's familiar locations, another counter is associated with unfamiliar locations. In another embodiment, hashes of incorrect passwords are recorded so that lockout counters are not incremented multiple times when the same incorrect password is entered repeatedly.

Smart lockout

One or more techniques and/or systems are provided for risk assessment. Historical authentication data and/or compromised user account data may be evaluated to identify a set of authentication context properties associated with user authentication sessions and/or a set of malicious account context properties associated with compromised user accounts (e.g., properties indicative of whether a user recently visited a malicious site, created a fake social network profile, logged in from unknown locations, etc.). The set of authentication context properties and/or the set of malicious account context properties may be annotated to create an annotated context property training set that may be used to train a risk assessment machine learning model to generate a risk assessment model. The risk assessment model may be used to evaluate user context properties of a user account event to generate a risk analysis metric indicative of a likelihood the user account event is malicious or safe.

Risk assessment modeling

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Dana Scott Kaufman