Microsoft Technology Licensing, LLC

Appesteem Corporation

Hong Jia

Ning Sun

Jigar J Mody

Chengyun Chu

Tony Lee

Jason Geffner

Frank Swiderski

Patrick Winkler

Dennis Batchelder

Bryan Young

Xun Wang

Natalya Grebenik

Kira Brodsky

Pavel Braginsky

Neil Comisioneru

Arunabh Trivedi

Keven Goh

Andrey Grebenik

Nicholas Batchelder

Daniel Ivanov

Methods, systems, and storage media are provided for accessibility services to assist users with disabilities in using touch-based interfaces and graphical user interfaces (GUIs). A client system generates and renders a GUI of a client application that comprises one or more graphical objects. The client system operates a service to identify graphical objects that refer or link to advertisements or other third party resources. The service applies a protective measure to the identified graphical objects, which prevent user inputs (e.g., 'taps') from being accepted by the client system. The protective measure may include a distinguishing effect to visually distinguish the protective measure from other graphical objects in the GUI. The user may remove the protective measure by performing a predefined gesture. Other embodiments may be described and/or claimed.

Technologies for indicating third party content and resources on mobile devices

Methods, systems, and storage media are described for indicating deceptive or trustworthy resources are provided. A client system generates and renders a graphical user interface (GUI) comprising one or more graphical objects in a client application. The client system identifies resources corresponding to individual graphical objects. The client system communicates the identified resources to a reputation system, and obtains indicators indicating deceptive resources to be obfuscated and/or trustworthy resources to be illuminated. The client system obfuscates the deceptive resources and illuminates the trustworthy resources according to the received indicators. Other embodiments may be described and/or claimed.

Technologies for indicating deceptive and trustworthy resources

Methods, systems, and storage media for distinguishing third party resources (TPRs) with respect to first party resources (FPRs) are provided. A client system generates and renders a graphical user interface (GUI) comprising one or more graphical objects in a client application. The client system identifies resources corresponding to individual graphical objects. The client system communicates the identified resources to a verification service, and obtains indicators indicating TPRs to be distinguished from FPRs. The client system applies a distinguishing effect to the TPRs based on the received indicators. Other embodiments may be described and/or claimed.

Technologies for indicating third party content and resources

Automated malware signature generation is disclosed. Automated malware signature generation includes monitoring incoming unknown files for the presence of malware and analyzing the incoming unknown files based on both a plurality of classifiers of file behavior and a plurality of classifiers of file content. An incoming file is classified as having a particular malware classification based on the analyzing of incoming unknown files and a malware signature is generated for the incoming unknown file based on the particular malware classification. Access is provided to the malware signature.

Automated malware signature generation

A region of HTML or PDF file bytecode run on a virtual machine is identified as possible malware, allowing a detection signature to be generated. A determination is made, based on code behavior, that malware may be present. Variables visible in this identification start state can be found by mapping the start state to scopes in an abstract syntax data structure. Searching previously executed states of the virtual machine for any assignment of a variable that belongs to the set of variables of interest provides a set of assignments of interest, even in obfuscated code. Nonterminated assignments of interest will lead in turn to other variables of interest and assignments of interest, until all assignments of interest are terminated. At that point, a region of code defined by the assignments of interest is identified as a malware detection signature generation candidate, and submitted to a human or automated analyst.

Identification of malware detection signature candidate code

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Hong Jia