Abstract:
The various aspects provide a method for recognizing and preventing malicious behavior on a mobile computing device before it occurs by monitoring and modifying instructions pending in the mobile computing device's hardware pipeline (i.e., queued instructions). In the various aspects, a mobile computing device may preemptively determine whether executing a set of queued instructions will result in a malicious configuration given the mobile computing device's current configuration. When the mobile computing device determines that executing the queued instructions will result in a malicious configuration, the mobile computing device may stop execution of the queued instructions or take other actions to preempt the malicious behavior before the queued instructions are executed.
Abstract:
Methods, systems and devices for communicating behavior analysis information using an application programming interface (API) may include receiving data/behavior models from one or more third-party network servers in a client module of a mobile device and communicating the information to a behavior observation and analysis system via a behavior API. The third-party servers may be maintained by one or more partner companies that have domain expertise in a particular area or technology that is relevant for identifying, analyzing, classifying, and/or reacting to mobile device behaviors, but that do not have access to (or knowledge of) the various mobile device sub-systems, interfaces, configurations, modules, processes, drivers, and/or hardware systems required to generate effective data/behavior models suitable for use by the mobile device. The behavior API and/or client modules allow the third-party server to quickly and efficiently access the most relevant and important information on the mobile device.
Abstract:
Methods, and computing devices implementing the methods, use application-based classifier models to improve the efficiency and performance of a comprehensive behavioral monitoring and analysis system predicting whether a software application is causing undesirable or performance depredating behavior. The application-based classifier models may include a reduced and more focused subset of the decision nodes that are included in a full or more complete classifier model that may be received or generated in the computing device. The application groups may be represented by application groups formed of computing device applications sharing related features, and may be generated using one or more clustering algorithms. Lean classifier models may be generated for each of the application group and may incorporate historical user input regarding execution permissions for features of applications within an application group.
Abstract:
Systems and methods of limiting wireless discovery range. A transmitting device may limit wireless discovery range by adjusting one or more transmission attributes of a discovery message, measuring inter-device distance based on range determination messages, or any combination thereof. A receiving device may limit wireless discovery range based on one or more attributes of a received discovery message, measuring inter-device distance based on range determination messages, or any combination thereof. Discovery messages may include a range adaptation bit indicating whether range adaptation is to be performed.
Abstract:
Various methods, apparatuses and/or articles of manufacture are provided which may be implemented for use by a mobile device to alter a scan operation. Various methods, apparatuses and/or articles of manufacture are provided which may be implemented for use by one or more electronic devices to determine one or more scan factors for use by a mobile device in altering a scan operation.
Abstract:
Various methods, apparatuses and/or articles of manufacture are provided which may be implemented for use by a mobile device to alter a scan operation. Various methods, apparatuses and/or articles of manufacture are provided which may be implemented for use by one or more electronic devices to determine one or more scan factors for use by a mobile device in altering a scan operation.
Abstract:
Methods, devices, systems, and non-transitory process-readable storage media for a computing device to use machine learning to dynamically configure an application and/or complex algorithms associated with the application. An aspect method performed by a processor of the computing device may include operations for performing an application that calls a library function associated with a complex algorithm, obtaining signals indicating user responses to performance of the application, determining whether a user tolerates the performance of the application based on the obtained signals indicating the user responses, adjusting a configuration of the application to improve a subsequent performance of the application in response to determining the user does not tolerate the performance of the application, and storing data indicating the user responses to the performance of the application and other external variables for use in subsequent evaluations of user inputs.
Abstract:
Example methods, apparatuses, or articles of manufacture are disclosed herein that may be utilized, in whole or in part, to facilitate or support one or more operations or techniques for detecting location changes and monitoring assistance data via scanning for use in or with a mobile device. Briefly, in accordance with at least one implementation, a method may include obtaining, at a mobile device, a rough estimate of a location of the mobile device; identifying a plurality of transmitters within a signal acquisition range of the roughly estimated location; transmitting probe requests addressed to at least some of the transmitters; and selectively initiating a passive scan at a receiver of the mobile device if a number of responses to the probe requests received from the transmitters is less than a threshold number.
Abstract:
Apparatuses and methods for adjusting wireless-derived positions of a mobile station using a motion sensor are presented. One method includes estimating a position of a mobile station based upon wireless signal measurements and measuring a movement of the mobile station using a relative motion sensor. The method further includes detecting a displacement of the mobile station based upon the measured movement, determining that the displacement is below a threshold and then adjusting the estimated position of the mobile station using information from the relative motion sensor. An apparatus includes a wireless transceiver, a relative motion sensor, a processor coupled to the wireless transceiver and the relative motion sensor, and a memory coupled to the processor. The memory stores executable instructions and data for causing the processor to execute methods for adjusting wireless-derived positions using a motion sensor.
Abstract:
Systems, apparatus and methods for estimating a location of a mobile device are presented. Before computing a location estimate, the mobile device groups a plurality of access points into two or more categories (for example, a first list of access points having a first characteristic and a second list of access points having a second characteristic). Round-trip time (RTT) measurements are computed for access points in the first list. A Short Interframe Space (SIFS) value may be determined for each access point in the first list or generally SIFT representing the first list as a whole. The RTT measurements are compensated with the appropriate SIFS value. The mobile device then computes its location or position fix estimate using the compensated RTT values while excluding less accurate RTT values from other access points. As a result, the location estimate eliminates adverse influent from some access points.