Microsoft Technology Licensing, LLC

Workman Nydegger

Harry Simon Katz

Joshua T Goodman

Robert L Rounthwaite

Robert George Atkinson

James M Lyon

Roy Williams

Khaja Ehteshamuddin Ahmed

David Steeves

Cynthia Dwork

Robert Alexander Sim

Andrew V Goldberg

Pui-Yin Winfred Wong

Mark A Nikiel

Robert L McCann

Luke Abrams

Laurentiu Bogdan Cristofor

Dana Scott Kaufman

Collin Tibbetts

Charles Hayden

Aaron Small

Adrian Kreuziger

Alexa Lynn Keizur

Geoffrey John Hulten

Xin Huang

Ryan Charles Colvin

Geoff Hulten

Chad W Mills

Joshua Korb

Eriola Kruja

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

Determining email filtering type based on sender classification. Incoming email is accessed and a sender of the incoming email is identified. The reputation of the sender of the incoming email is determined. An email sender classification is made based on the reputation of the sender. A determination of the type of filtering operations to be performed by an email filter on the email is made based on the classification. The parameters of the type of filtering operations to be performed are determined. The parameters of the type of filtering operations to be performed are provided to the email filter.

Determining email filtering type based on sender classification

A reputation data store provides reputation information to a user regarding the source of an electronic communication via a client. When the client receives an electronic communication, the client queries a reputation data store for reputation information regarding the source of the electronic communication. The client receives the reputation information from the reputation data store and displays the reputation information to the user. The user then decides whether to interact with the source of the electronic communication based on the displayed reputation information. Alternatively, an administrator may decide whether the user may interact with the source of the electronic communication.

Leveraging global reputation to increase personalization

The present invention provides for generating inputs that can be provided to a message classification module to facilitate more reliable classification of electronic messages, such as, for example, as unwanted and/or unsolicited. In one embodiment, a sending messaging server provides an appropriate response to address verification data thereby indicating a reduced likelihood of the sending messaging server using a forged network address. In another embodiment, it is determined if a messaging server is authorized to send electronic messages for a domain. In yet another embodiment, electronic message transmission policies adhered to by a domain are identified. In yet a further embodiment, a sending computer system expends computational resources to solve a computational puzzle and includes an answer document in an electronic message. A receiving computer system receives the electronic message and verifies the answer document.

Reducing unwanted and unsolicited electronic messages by exchanging electronic message transmission policies and solving and verifying solutions to computational puzzles

Reducing unwanted and unsolicited electronic messages by preventing connection hijacking and domain spoofing

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Bellevue, WA, United States of America

Harry Simon Katz