Mcafee, Inc.

Raj Vardhan

Dattatraya Kulkarni

Srikanth Nalluri

Himanshu Srivastava

Kamlesh Halder

Krishnapur Narasimhamurthy Venkatasubrahmanyam

Tirumaleswar Reddy Konda

Celeste R Fralick

Yonghong Huang

Raja Sinha

Susmita Nayak

Kranthikumar Gadde

Shankar Subramanian

Mitesh Kumar

Javed Hasan

Arvind Sankarasubramanian

Kranthi Kumar Gadde

Rangaswamy Narayana

Saravana Kumar Subramanian

Joydeb Mukherjee

Igor Tatourian

Gabriel G Infante-Lopez

Jeremy F Bennett

Samrat Chitta

Sonam Bothra

Reji Gopalakrishnan

Muralitharan Chithanathan

Methods, apparatus, systems and articles of manufacture are disclosed to verify application permission safety. An example apparatus to identify unsafe permissions associated with a candidate app disclosed herein includes an app classifier interface to retrieve a cluster of apps associated with the candidate app, the candidate app including a requested permission set (RPS), a white knight (WK) identifier to identify a set of WK apps within the cluster, the set of WK apps associated with a designation of trust, a safe permission set (SPS) evaluator to generate an SPS list associated with the set of WK apps within the cluster, and an RPS identifier to determine whether permissions of the RPS are listed in the SPS list, the SPS evaluator further to designate first respective ones of the permissions of the RPS as safe when the first respective ones of the permissions are listed in the SPS list, and designate second respective ones of the permissions of the RPS as unsafe when the second respective ones of the permissions are absent from the SPS list.

Methods, systems, articles of manufacture and apparatus to verify application permission safety

Disclosed examples include receiving an indication that a file is designated as a favorite file from a user device; retrieving the file from a remote location; storing the file as a first favorite file on a favorites storage device; re-retrieving the file from the remote location; and updating the first favorite file with the re-retrieved file.

Methods and apparatus for automatic creation and access to favorite personal cloud data

Particular embodiments described herein provide for system that can be configured to deliver a notification to a user based on the user's preference for each device that receives the notification. The user's preference is based on how the user interacted with similar notifications in the past and the system can change how it will deliver similar notifications to the user in the future based on how the user interacts with the notification.

Personalization of delivery of notifications

A system for controlling accesses to network enabled devices includes a network interface over which a hub communicates with network enabled devices, a processor, and a multilayer access control layer. The access control layer includes instructions that, when executed by the processor, cause the processor to detect, at the hub, a request representing an attempt by an application executing on a remote host device to access a network enabled device communicatively coupled to the hub, characterize the request according to a user of the remote host device, the application making the attempt, and the network enabled device, and determine whether to allow or deny the request based upon the characterization and a plurality of rules. The rules may include definitions of access rights, with respect to the network enabled device, for users, applications, commands or queries made by applications, remote host devices, and network domains.

Multilayer access control for connected devices

A mobile device including: a position locator; a user data engine; and a reputation engine client configured to: receive a location from the position locator; operate the user data engine to provide a user profile, intent, and context data for a user, the context data including dynamic factors about the user, and the profile including relative factors about the user that are relatively static with respect to the context data from the user data engine; and determine a reputation for the location, wherein the reputation is based at least in part on a combination of the user profile, intent, and context.

Context-aware reputation of a place

Methods, apparatus, systems and articles of manufacture are disclosed to perform malware detection using a generative adversarial network. An example apparatus includes a first encoder network to encode an input sample into a first encoded sample, the first encoder network implemented using a multilayer perception (MLP) network, a generator network to reconstruct the first encoded sample to generate a reconstructed sample, a discriminator network to, in response to obtaining the first encoded sample and the reconstructed sample, generate a loss function based on the reconstructed sample and the input sample, and an optimization processor to, when the loss function satisfies a threshold loss value, classify the input sample as malicious.

Methods and apparatus to perform malware detection using a generative adversarial network

Methods and apparatus to detect adversarial malware are disclosed. An example adversarial malware detector includes a machine learning engine to classify a first feature representation representing features of a program as benign or malware, a feature perturber to, when the first feature representation is classified as benign, remove a first one of the features to form a second feature representation, and a decider to classify the program as adversarial malware when the machine learning engine classifies the second feature representation as malware.

Methods and apparatus to detect adversarial malware

Methods, apparatus, systems and articles of manufacture are disclosed to verify application permission safety. An example apparatus to identify unsafe permissions associated with a candidate app disclosed herein includes an app classifier interface to retrieve a cluster of apps associated with the candidate app, the candidate app including a requested permission set (RPS), a trusted app (TA) identifier to identify a set of TAs within the cluster, the set of TAs associated with a designation of trust, a safe permission set (SPS) evaluator to generate an SPS list associated with the set of TAs within the cluster, and an RPS identifier to determine whether permissions of the RPS are listed in the SPS list, the SPS evaluator further to designate first respective ones of the permissions of the RPS as safe when the first respective ones of the permissions are listed in the SPS list, and designate second respective ones of the permissions of the RPS as unsafe when the second respective ones of the permissions are absent from the SPS list.

In an example, there is disclosed a computing apparatus, including a user notification interface; a context interface; and one or more logic elements forming a contextual privacy engine operable to: receive a notification; receive a context via the context interface; apply the context to the notification via a notification rule; and take an action via the user notification interface based at least in part on the applying. The contextual privacy engine may also be operable to mathematically incorporate user feedback into the notification rule. There is also described a method of providing a contextual privacy engine, and one or more computer-readable storage mediums having stored thereon executable instructions for providing a contextual privacy engine.

Contextual privacy engine for notifications

Distributed favorite files are stored in a favorite storage device. An agent dynamically monitors usage statistics for a plurality of files, wherein the plurality of files are located on a plurality of devices, designates a first file of the plurality of files as a favorite file based on usage statistics for the first file, and in response to designating the first file as a favorite file, stores a copy of the first file on a favorites storage device.

Mechanism for automatic creation and access to favorite personal cloud data

In an example, one or more computing devices operate to provide a context-aware reputation of a place, such as in relation to a human user. Context may include the user's identity and purpose, as well as environmental factors such as time of day, weather, and political drivers. The device may communicate with a server to receive globalized safety intelligence. When the user enters a zone, the device may determine a context-sensitive reputation, such as 'Green,' 'Yellow,' or &#x201c;Red.&#x201d; Depending on the reputation, the device may then take an appropriate action, such as warning the user or providing additional information.

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Bengaluru, India

Raj Vardhan