Abstract:
Techniques are provided for adaptively sampling orientation sensors in positioning systems based on location (e.g., map) data. Embodiments can enable a device to use location, direction, and/or location information to anticipate an expected change in motion. The embodiments can then identify and prioritize a number of sampling strategies to alter sampling rates of orientation sensors, and implement at least one strategy, based on priority.
Abstract:
Methods, apparatuses, systems, and computer-readable media for integrating sensation functionalities into a mobile device using a haptic sleeve are presented. According to one or more aspects of the disclosure, a computing device may receive, via a haptic sleeve, sensation input captured by one or more haptic components of the haptic sleeve. Subsequently, the computing device may store haptic data corresponding to the received sensation input. For example, in storing such haptic data, the computing device may store information describing one or more electrical signals received via the one or more haptic components of the haptic sleeve during a period of time corresponding to a particular event, and this stored information may reflect various characteristics of the sensation input received by the computing device in connection with the particular event, such as the magnitude(s), position(s), duration, and/or type(s) of sensation(s) captured during the period of time.
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 compute and use the actual execution states of software applications to implement power saving schemes and to perform behavioral monitoring and analysis operations. A mobile device may be configured to monitor an activity of a software application, generate a shadow feature value that identifies actual execution state of the software application during that activity, generate a behavior vector that associates the monitored activity with the shadow feature value, and determine whether the activity is malicious or benign based on the generated behavior vector, shadow feature value and/or operating system execution states. The mobile device processor may also be configured to intelligently determine whether the execution state of a software application is relevant to determining whether any of the monitored mobile device behaviors are malicious or suspicious, and monitor only the execution states of the software applications for which such determinations are relevant.
Abstract:
Methods, and mobile devices implementing the methods, use application-specific and/or application-type specific classifier 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-specific and application-type specific 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 mobile device. The locally generated application-specific and/or application-type specific classifier models may be used to perform real-time behavior monitoring and analysis operations by applying the application-based classifier models to a behavior/feature vector generated by monitoring mobile device behavior. The various aspects focus monitoring and analysis operations on a small number of features that are most important for determining whether operations of a software application are contributing to undesirable or performance depredating behavior.
Abstract:
Example methods, apparatuses, or articles of manufacture are disclosed herein that may be utilized to facilitate or otherwise support one or more processes or operations in connection with binning venues into categories based, at least in part, on signal propagation characteristics associated with such venues.
Abstract:
A method of disambiguating a location of a mobile station within a structure includes: obtaining, at the mobile station, regional pressure indications and corresponding region indications indicating regions within a structure that are vertically displaced with respect to each other, each of the regional pressure indications indicating atmospheric pressure information associated with the corresponding region; determining mobile station pressure information associated with a present location of the mobile station; comparing the mobile station pressure information with the regional pressure indications; and based on the comparing, determining in which of the regions the mobile station presently resides.
Abstract:
A computing device may be configured to work in conjunction with another component (e.g., a server) to better determine whether a software application is benign or non-benign. This may be accomplished via the server performing static and/or dynamic analysis operations, generating a behavior information structure that describes or characterizes the range of correct or expected behaviors of the software application, and sending the behavior information structure to a computing device. The computing device may compare the received behavior information structure to a locally generated behavior information structure to determining whether the observed behavior of the software application differs or deviates from the expected behavior of the software application or whether the observed behavior is within the range of expected behaviors. The computing device may increase its level of security/scrutiny when the behavior information structure does not match the local behavior information structure.
Abstract:
Mobile computing devices may be equipped with hardware components configured to monitor key assets of the mobile device at a low level (e.g., firmware level, hardware level, etc.). The hardware component may also be configured to dynamically determine the key assets that are to be monitored in the mobile device, monitor the access or use of these key assets by monitoring data flows, transactions, or operations in a system data bus of the mobile device, and report suspicious activities to a comprehensive behavioral monitoring and analysis system of the mobile device. The comprehensive behavioral monitoring and analysis system may then use this information to quickly identify and respond to malicious or performance degrading activities of the mobile device.
Abstract:
Systems and methods of network based positioning include a server configured to assign priority levels to mobile devices locatable within the network, and allocate network resources for network based positioning of the locatable mobile devices, based on the corresponding priority levels assigned to the mobile devices. The server may further be configured to admit only a selected subset of the locatable mobile devices into the network for purposes of network based positioning and deny admission to the remaining locatable mobile devices, wherein the selected subset can be determined based on an attribute of the mobile device and/or a characteristic of the user of the mobile device.