Microsoft Technology Licensing, LLC

Roderic C Deyo

Joshua T Goodman

Robert L Rounthwaite

Geoffrey John Hulten

Manav Mishra

Anthony Paul Penta

Sandeep S Sahasrabudhe

Paul Stephen Rehfuss

Sunil Swami

Christopher Hugh Pratley

Aaron H Averbuch

Kenneth Grey Richards

Elliott Jeb Haber

Gopalakrishnan Seshadrinathan

David Aaron Ward Snelling

Jeff Raikes

Brian D Price

Techniques are described which may provide a rendered image of a website from a potentially malicious party. In an implementation, a rendered image is collected of a web page that is identified as potentially malicious from a frame buffer. A communication is then formed to be communicated over a network that includes the collected image.

Rendered image collection of potentially malicious web pages

A framework for enabling a community of users to share help information about an application program. The framework includes a mechanism for users to generate and share help information and a mechanism for users to access help information created by other users. To generate help information, tools may be incorporated into an application to enable users to capture information about the application as they are performing functions. Those tools capture as a video clip a representation of screen displays created by the application as the user interacts with the application to perform a function about which help information is to be generated. Along with the scenes captured in the video clip, application context information and explanatory material, such as an audio narration, can be captured. The mechanism to access help information includes a server to which video clips can be posted and cataloged, allowing identification of clips in response to user input describing a required help topic.

Community-based software application help system

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

Techniques to filter media content based on entity reputation are described. An apparatus may comprise a reputation subsystem that manages an entity reputation score for an entity. The reputation subsystem comprising a reputation manager component and a reputation input/output (I/O) component. The reputation manager component may include a data collection module that collects reputation information for an entity from a selected set of multiple reputation sources. The reputation manager component may also comprise a feature manager module, communicatively coupled to the data collection module, that extracts a selected set of reputation features from the reputation information. The reputation manager component may further comprise a reputation scoring module, communicatively coupled to the feature manager module, that generates an entity reputation score based on the reputation features using a supervised or unsupervised machine learning algorithm. Other embodiments are described and claimed.

Techniques to filter media content based on entity reputation

Techniques to filter media content based on entity reputation are described. An apparatus may comprise a reputation subsystem operative to manage an entity reputation score for an entity. The reputation subsystem comprising a reputation manager component and a reputation input/output (I/O) component. The reputation manager component may comprise, among other elements, a data collection module operative to collect reputation information for an entity from a selected set of multiple reputation sources. The reputation manager component may also comprise a feature manager module communicatively coupled to the data collection module, the feature manager module operative to extract a selected set of reputation features from the reputation information. The reputation manager component may further comprise a reputation scoring module communicatively coupled to the feature manager module, the reputation scoring module operative to generate an entity reputation score based on the reputation features using a supervised or unsupervised machine learning algorithm. Other embodiments are described and claimed.

Systems and methods that integrate user assigned association among a plurality of resources or entities. The subject innovation employs an association component that relates such resources or entities, based on aggregate of user notions that are assigned for relationships; and/or based on how users perceive existence of relationships among such resources. Accordingly, resources can be related (e.g., linked, matched, tagged and the like) based on relevance of collective user behavior during tagging.

Relational linking among resoures

Phishing detection, prevention, and notification is described. In an embodiment, a messaging application facilitates communication via a messaging user interface, and receives a communication, such as an email message, from a domain. A phishing detection module detects a phishing attack in the communication by determining that the domain is similar to a known phishing domain, or by detecting suspicious network properties of the domain. In another embodiment, a Web browsing application receives content, such as data for a Web page, from a network-based resource, such as a Web site or domain. The Web browsing application initiates a display of the content, and a phishing detection module detects a phishing attack in the content by determining that a domain of the network-based resource is similar to a known phishing domain, or that an address of the network-based resource from which the content is received has suspicious network properties.

Growing community of inventors

Redmond, WA, United States of America

Roderic C Deyo