Appthority, Inc.

Symantec Corporation

Kevin Watkins

Anthony John Bettini

Domingo J Guerra

Michael Price

Sasan Padidar

Steve Hanna

Ian Eyberg

Techniques for in-line filtering of insecure or unwanted mobile components or communications (e.g., insecure or unwanted behaviors associated with applications for mobile devices ('apps'), updates for apps, communications to/from apps, operating system components/updates for mobile devices, etc.) for mobile devices are disclosed. In some embodiments, in-line filtering of apps for mobile devices includes intercepting a request for downloading an application to a mobile device; and modifying a response to the request for downloading the application to the mobile device. In some embodiments, the response includes a notification that the application cannot be downloaded due to an application risk policy violation.

In-line filtering of insecure or unwanted mobile device software components or communications

Techniques for automated classification of mobile applications ('apps') battery consumption using simulation are disclosed. In one embodiment, a system for automated classification of mobile app battery consumption using simulation includes an app analyzer for performing an analysis of the app; and a classification engine for classifying the app into a battery consumption category based on the analysis performed using the app analyzer. In one embodiment, a process for automated classification of mobile app battery consumption using simulation includes receiving an app; performing an automated analysis of the app; and generating a battery consumption score for the app based on the automated analysis of the app.

Automated classification of mobile app battery consumption using simulation

Techniques for brand recognition and protection in applications ('apps') for mobile devices are disclosed. In some embodiments, brand recognition and protection in apps for mobile devices includes extracting a plurality of features of an app; comparing the plurality of features with a target set of features associated with an entity; and determining whether the app violates a policy for brand recognition and protection for the entity based on the comparing the plurality of features with the target set of features associated with the entity.

Brand recognition and protection in mobile applications

Automated classification of applications ('apps') for mobile devices is provided. In some embodiments, automated classification of apps for mobile devices includes receiving an application ('app'); performing an analysis of the app using a classification engine; and determining an app category for the app based on the analysis performed using the classification engine.

Automated classification of applications for mobile devices

Quantifying the risks of applications ('apps') for mobile devices is disclosed. In some embodiments, quantifying the risks of apps for mobile devices includes receiving an application for a mobile device; performing an automated analysis of the application based on a risk profile; and generating a risk score based on the automated analysis of the application based on the risk profile.

Quantifying the risks of applications for mobile devices

Techniques for off-device anti-malware protection for mobile devices are disclosed. In some embodiments, off-device anti-malware protection for mobile devices includes receiving a software inventory for a mobile device, in which the software inventory identifies a plurality of applications installed on the mobile device; and determining whether one or more of the plurality of applications identified in the software inventory are associated with malware based on a policy. In some embodiments, the off-device anti-malware protection for mobile devices further includes enforcing the policy on the mobile device. In some embodiments, the off-device anti-malware protection for mobile devices is provided as a cloud service.

Off-device anti-malware protection for mobile devices

Application malware filtering for advertising networks is disclosed. For example, techniques for providing a system and process for detecting malicious ad content (e.g., or other undesirable ad content) distributed by advertising (ad) networks are disclosed. In some embodiments, application ('app') malware filtering for advertising networks includes receiving ad content; processing the ad content; and automatically determining whether the ad content is associated with a malicious app.

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San Francisco, CA, United States of America

Kevin Watkins