Microsoft Technology Licensing, LLC

Charles Strempler

Jason Joseph Weber

Abhishek Sagar

Mara Beth Fortini

Richard Joseph Murillo

Brian Keith Catlin

Sinclaire Renee Hamilton

Michael David Mccormack

Christian Stockwell

Charles David Ellis

Laurent Mollicone

James Randall Flynn

Christian Seifert

Michael Joe Woolf

Ehab Sobhy Deraz

William A Manis

Fredric W Pullen, Iii

Ali Alabbas

Kean E E Lim

Matthew Willem Asplund

Robert G Lefferts

Rupo Zhang

Paresh Maisuria

Nerses Ghevondyan

Devanshi M Gajjar

Millen May T Angeles

Cristian C Berejan

Shreya Salikram Chowdhary

Mathew B Olinger

Svetoslav G Paregov

Sameer Surendra Jagdale

Embodiments of the technology described herein identify and mitigate phishing attempts by analyzing user input received at the operating system level. Initially, a credential, such as a username or password, is registered with the threat detection system. The technology described herein intercepts user input at the operating system level, generates a hash of the input, and compares it with a hash of a credential being monitored. The technology described herein will perform a threat assessment when a secret entry is detected. The threat assessment may use the application context and the network context as inputs to the assessment. When the threat assessment results in an unknown classification or when the snapshot is otherwise requested, a snapshot is captured to supplement the threat assessment. Based on user settings, the snapshot is consumed by a snapshot phishing machine learning model. Various mitigation actions may be taken when a threat is detected.

Snapshot phishing detection and threat analysis

The technology described herein identifies and mitigates phishing attempts by analyzing user input received at the operating system level. Initially, a credential, such as a username or password, is registered with the threat detection system. The technology described herein intercepts user input at the operating system level, generates a hash of the input, and compares it with a hash of a credential being monitored. A credential entry is detected when a hash for the character string entered matches a hash for a credential being monitored. The technology described herein will perform a threat assessment when a secret entry is detected. The threat assessment may use the application context and the network context as inputs to the assessment. Various mitigation actions may be taken when a threat is detected.

Credential input detection and threat analysis

In non-limiting examples of the present disclosure, systems, methods and devices for assisting with providing dataset insights associated with datasets are presented. A dataset and a query relating to the dataset may be received. The dataset may be processed to determine metadata that describes one or more properties of the dataset. The dataset, the determined metadata, and the user query may be provided to one or more modular recommendation elements for processing into an insight result that indicates a result from data analysis directed to the query. The insight result may be transferred in a portable format for use by the productivity application in displaying one or more insight objects based on the insight result.

Modular data insight handling for user application data

Embodiments are directed to generating declarative model editors. A computer system receives a first user input indicating a selection of a native underlying schema that includes constraints that are to be followed by models based on the native schema. The computer system receives a second user input indicating a declarative entry that includes user-configurable model editor characteristics. The computer system automatically generates a declarative model editor based on the selected native schema and user-configurable model editor characteristics. Other embodiments are directed to editing models using declarative model editors. A computer system receives user input indicating a declarative entry to be applied to a model, generates a declarative model editor based on the declarative entry, receives a model that is editable by the generated declarative model editor, and edits user-configurable characteristics of the model. The model characteristics conform to the constraints in the native underlying schema.

Declarative model editor generation

Growing community of inventors

Seattle, WA, United States of America

Charles Strempler