Mcafee, Inc.

Venkatasubrahmanyam Krishnapur

Dattatraya Kulkarni

Srikanth Nalluri

Kaushal Kumar Dhruw

Raja Sinha

Kamlesh Halder

Yogesh Jain

Susmita Nayak

Sailaja K Shankar

Aravind Doss

Kranthikumar Gadde

Shailaja Shankar

Venkata Krishnan Nagarajan

There is disclosed in one example a remediation server including: a hardware platform, including a processor, a memory, and a network interface; and instructions encoded within the memory to instruct the processor to: receive an application binary; create an application logic model of the application binary; and create personalization rules for the application binary based on the application logic model.

Managed software remediation

The present disclosure relates to a system and method for performing anti-malware scanning of data files that is data-centric rather than device-centric. In the example, a plurality of computing devices are connected via a network. An originating device creates or first receives data, and scans the data for malware. After scanning the data, the originating device creates and attaches to the data a metadata record including the results of the malware scan. The originating device may also scan the data for malware contextually-relevant to a second device.

Digital protection that travels with data

The present disclosure relates to a system and method for providing a service on a wearable device where the wearable device is limited in its functionality in some way when compared with a companion device. In particular, the disclosure describes use cases for configuring the wearable device, and use cases for configuring a wearable device and performing service application functions on the wearable device while leveraging a companion device.

Mechanisms for conserving resources of wearable devices

Code of a particular application is analyzed against a semantic model of a software development kit of a particular platform. The semantic model associates a plurality of application behaviors with respective application programming interface (API) calls of the particular platform. A set of behaviors of the particular application is identified based on the analysis of the code and a particular one of the set of behaviors is identified as an undesired behavior. The particular application can be automatically modified to remediate the undesired behavior. The particular application can be assigned to one of a plurality of device modes, and access to the particular application on a user device can be based on which of the plurality of device modes is active on the user device.

Mobile application management

Wearable device task offloading to conserve processing resources

A safety event is determined as affecting a user based at least in part context data collected at a user device associated with the user. In some aspects, context data is detected from sensors on the client device, the context data describing a present context of the user. A deviation of the present context from a historical context is determined to be beyond a threshold. Determining that the deviation is beyond the threshold can be determined to correspond to a safety event potentially jeopardizing safety of the user. In some aspects, an action can be launched in response to determining the safety event.

Personal safety and emergency services

The present disclosure relates to a system and method for performing antimalware scanning of data files that is data-centric rather than device-centric, In the example, a plurality of computing devices are connected via a network. An originating device creates or first receives data, and scans the data for malware, After scanning the data, the originating device creates and attaches to the data a metadata record including the results of the malware scan, The originating device may also scan the data for malware contextually-relevant to a second device.

According to one example, a system and method are disclosed for malware and grayware remediation. For example, the system is operable to identify applications that have some legitimate behavior but that also exhibit some undesirable behavior. A remediation engine is provided to detect malware behavior in otherwise useful applications, and allow the useful parts of the application to run while blocking the malware behavior. In an example method of 'healing,' this may involve modifying the application binary to remove undesirable behavior. In an example method of 'personalization,' this may involve inserting control hooks through the operating system to prevent certain subroutines from taking effect.

The present disclosure relates to a system and method for providing a secure context exchange cloud service which enables context information to be shared among devices, and providing a safety application which utilizes the context information being shared. In one example, the context exchange cloud service provides secure exchange of the context information, which in turn enables a safety application to enhance personal safety.

Secure context sharing for priority calling and various personal safety mechanisms

To provide a more seamless experience across multiple devices, task streaming systems and methods allow a user to create 'task-contexts' and manage metadata of files stored across multiple data storage devices and user preferences associated with capabilities of the multiple devices for operating on the file. Furthermore, the task streaming systems and methods are provided to allow task-contexts to be shared from one device to another device. A task-context specifies one or more files and one or more operations to be performed on the one or more files. By providing a task-context from one device to the other device, a user can accomplish a task with a particular file and seamlessly transition between devices with minimal disruption and effort.

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Bengaluru, India

Venkatasubrahmanyam Krishnapur