Microsoft Technology Licensing, LLC

Laurentiu Bogdan Cristofor

Pui-Yin Winfred Wong

Maria Puertas Calvo

Dana Scott Kaufman

David Steeves

Tanmoy Dutta

Robert Alexander Sim

Mark A Nikiel

Luke Abrams

Harry Simon Katz

Sayed Hassan Abdelaziz

Rajat Luthra

Charles Hayden

Collin Tibbetts

Alexa Lynn Keizur

Adrian Kreuziger

Aaron Small

James Robert Hamilton

Yordan Ivanov Rouskov

Alexander Thomas Weinert

Girish Chander

Rodger N Kline

Caleb Geoffrey Baker

Raul Garcia

Carlos Adrian Lopez Castro

Michael Vincent McLaughlin

Elena Daniela Cristofor

Sung L Hsueh

Lakshmi Priya Gopal

Michael V Mclaughlin

Systems are provided for improving computer security systems that are based on user risk scores. These systems can be used to improve both the accuracy and usability of the user risk scores by applying multiple tiers of machine learning to different the user risk profile components used to generate the user risk scores and in such a manner as to dynamically generate and modify the corresponding user risk scores.

Supervised learning system for identity compromise risk computation

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

Methods, systems, and apparatuses in a computing device enable user access to a resource. The method includes receiving, from a user, a request for access to a resource; accessing an authentication flow for granting access to the resource; obtaining first claims for a user from a first claims provider in the authentication flow; determining a second claims provider in the authentication flow, the second claims provider having a trust relationship with the claims facilitator; directing the user to the second claims provider; receiving second claims for the user from the second claims provider; and enabling the user to access the resource in response to at least the received first and second claims.

Extensible framework for authentication

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

An indexing value may be determined, transparently with respect to a requester, based on a desired plaintext item of data and a cryptographic key. The indexing value may be used to access an entry in an indexing structure to obtain a corresponding database entry which includes a non-deterministically encrypted ciphertext item. In another embodiment, an indexing structure for a database may be accessed. Positions of items of the indexing structure may be based on corresponding plaintext items. References related to the corresponding plaintext items in the indexing structure may be encrypted and other information in the indexing structure may be unencrypted. A portion of the indexing structure may be loaded into a memory and at least one of the encrypted references related to one of the plaintext items may be decrypted. The decrypted reference may be used to access a corresponding non-deterministically encrypted data item from the database.

Encrypted data search

The systems and methods of the present invention facilitate database row-level security by utilizing SQL extensions to create and associate named security expressions with a query initiator(s). Such expressions include Boolean expressions, which must be satisfied by a row of data in order for that data to be made accessible to the query initiator. In general, a query is augmented with security expressions, which are aggregated and utilized during querying rows of data. The systems and methods variously place security expressions within a query in order to optimize query performance while mitigating information leaks. This is achieved by tagging security expressions as special and utilizing rules of predicate to pull or push non-security expressions above or below security expressions, depending on the likelihood of a non-security being safe, as determined via a static and/or dynamic analysis.

Systems and methods that optimize row level database security

Growing community of inventors

Redmond, WA, United States of America

Laurentiu Bogdan Cristofor