Appthority, Inc.

Mcafee, Inc.

Symantec Corporation

Michael Price

Anthony John Bettini

Kevin Watkins

Domingo J Guerra

Stuart McClure

Steve Hanna

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 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

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

A system and method in one embodiment includes modules for identifying an asset with a vulnerability risk, identifying a service running on a port on the asset, identifying a connection to the port, calculating an operational dependence role of the asset as a function of the service and the connection, and modifying the vulnerability risk based on the operational dependence role. Other embodiments include identifying a protocol of a data packet at the port, classifying the protocol into a protocol category with a protocol importance score, calculating a connection average for the asset, classifying the connection average into a connection category with a connection score, and calculating a service dependence score. Other embodiments include calculating a host dependence score, assigning a data importance score to data communicated by the asset, and calculating the operational dependence role as a function of the host dependence score and data importance score.

System and method for network-based asset operational dependence scoring

By way of example, a system, method, and computer program product are provided for identifying vulnerabilities associated with data loaded in memory. In operation, a subset of data that is loaded in memory is identified, including for example by name and version number. The subset of data is compared to a known minimal version known to be good. Furthermore, there is a reaction based on the comparison, such as reporting a vulnerability.

System, method, and computer program product for identifying vulnerabilities associated with data loaded in memory

Each virtual machine in a set of virtual machines managed by the virtual machine manager is identified. For each virtual machine in the set, it is determined whether the respective virtual machine is online. For at least the virtual machines determined to be offline, a machine image is collected for each offline virtual machine. Security of the offline virtual machines is assessed from the collected images. For virtual machines identified as online, an agent is loaded on each online virtual machine in the set via the virtual machine manager. The loaded agents are used to assess security of the online virtual machines in the set.

Security assessment of virtual machine environments

A system, method, and computer program product are provided for identifying vulnerabilities associated with data loaded in memory. In operation, a subset of data that is loaded in memory is identified. Additionally, the subset of data is compared to a list of known data. Furthermore, there is a reaction based on the comparison.

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San Ramon, CA, United States of America

Michael Price