Microsoft Technology Licensing, LLC

David Aaron Ward Snelling

Christine Jefson

Greg A Kohanim

Jonathan L Wiedemann

Joshua T Goodman

Robert L Rounthwaite

Geoffrey John Hulten

Manav Mishra

Anthony Paul Penta

Paul Stephen Rehfuss

Roderic C Deyo

Elliott Jeb Haber

Gopalakrishnan Seshadrinathan

Described is a technology by which phishing-related data sources are processed into aggregated data and a given site evaluated the aggregated data using a predictive model to automatically determine whether the given site is likely to be a phishing site. The predictive model may be built using machine learning based on training data, e.g., including known phishing sites and/or known non-phishing sites. To determine whether an object corresponding to a site is likely a phishing-related object are described, various criteria are evaluated, including one or more features of the object when evaluated. The determination is output in some way, e.g., made available to a reputation service, used to block access to a site or warn a user before allowing access, and/or used to assist a hand grader in being more efficient in evaluating sites.

Finding phishing sites

Content searching and configuration of search results are described. In an implementation, a method includes in response to a search query, selecting a keyword based on heuristic data which describes a plurality of previously performed searches. A search is performed utilizing the search query and the selected keyword to locate content.

Content searching and configuration of search results

Content searching and configuration of search results are described. In an implementation, a method includes in response to a search query, selecting a keyword based on heuristic data which describes a plurality of previously performed searched. A search is performed utilizing the search query and the selected keyword to locate content.

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Everett, WA, United States of America

David Aaron Ward Snelling