Microsoft Technology Licensing, LLC

Michael Abraham Betser

Weisheng Li

Ravi Kiran Reddy Poluri

Mihai Costea

John Carroll Gordon

Mohit Sewak

Cem Keskin

David Nicholas Yost

Milenko Drinic

William Blum

Sharada Shirish Acharya

Hua Ding

Christian Rudnick

Pak On Chan

Sihong Liu

Phanindra Pampati

An approach for clustering large sets of categorical data involves iteratively ordering the data points, partitioning the data into blocks based on the ordering, and clustering the data points within each block, where different iterations use different orderings and, thus, different partitionings. In some embodiments, the data points are represented by multi-dimensional categorical vectors, and the orderings are based on permutations of the categorical dimensions. The iterative clustering may be repeated for multiple successive time windows to track the clusters. Various applications of the disclosed clustering approach, including for cyber security, are also described.

Clustering and cluster tracking of categorical data

The technology described herein determines whether a candidate text is in a requested class by using a generative model that may not be trained on the requested class. The present technology may use of a model trained primarily in an unsupervised mode, without requiring a large number of manual user-input examples of a label class. The may produce a semantically rich positive example of label text from a candidate text and label. Likewise, the technology may produce from the candidate text and the label a semantically rich negative example of label text. The labeling service makes use of a generative model to produce a generative result, which estimates the likelihood that the label properly applies to the candidate text. In another aspect, the technology is directed toward a method for obtaining a semantically rich example that is similar to a candidate text.

Automatic labeling of text data

Methods, systems, and computer storage media for providing a multi-attribute cluster-identifier that supports identifying malicious activity in computing environments. An instance of an activity having an attribute set can be assessed. The attribute set of the instance of the activity is analyzed to determine whether the instance of the activity is a malicious activity. The attribute set of the instance of the activity is compared to a plurality of multi-attribute cluster-identifiers of previous instances of the activity, such that, a determination that the instance of the activity is a malicious activity is made when the attribute set of the instance of the activity corresponds to an identified multi-attribute cluster-identifier. The identified multi-attribute cluster-identifier has a risk score and an attribute set that indicate a likelihood that the instance of the activity is a malicious activity. A visualization that identifies the instance of the activity as a malicious activity is generated.

Campaign intelligence and visualization for combating cyberattacks

A user may be authenticated to access an account, computing device, or other resource based on the user's gaze pattern and neural or other physiological response(s) to one or more images or other stimuli. When the user attempts to access the resource, a computing device may obtain login gaze tracking data and measurement of a physiological condition of the user at the time that the user is viewing an image or other stimulus. Based on comparison of the login gaze tracking data and the measurement of the physiological condition to a model, the computing device can determine whether to authenticate the user to access the resource.

Authentication based on gaze and physiological response to stimuli

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Kirkland, WA, United States of America

Michael Abraham Betser