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:
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.
Abstract:
Systems and methods for recognizing and reacting to malicious or performance-degrading behaviors in a mobile device include observing mobile device behaviors in an observer module within a privileged-normal portion of a secure operating environment to identify a suspicious mobile device behavior. The observer module may generate a concise behavior vector based on the observations, and provide the vector to an analyzer module in an unprivileged-secure portion of the secure operating environment. The vector may be analyzed in the unprivileged-secure portion to determine whether the mobile device behavior is benign, suspicious, malicious, or performance-degrading. If the behavior is found to be suspicious, operations of the observer module may be adjusted, such as to perform deeper observations. If the behavior is found to be malicious or performance-degrading behavior the user and/or a client module may be alerted in a secure, tamper-proof manner.
Abstract:
The various aspects provide a mobile device and methods implemented on the mobile device for modifying behavior models to account for device-specific or device-state-specific features. In the various aspects, a behavior analyzer module may leverage a full feature set of behavior models (i.e. a large classifier model) received from a network server to create lean classifier models for use in monitoring for malicious behavior on the mobile device, and the behavior analyzer module may dynamically modify these lean classifier models to include features specific to the mobile device and/or the mobile device's current configuration. Thus, the various aspects may enhance overall security for a particular mobile device by taking the mobile device and its current configuration into account and may improve overall performance by monitoring only features that are relevant to the mobile device.
Abstract:
Methods, devices and systems for detecting suspicious or performance-degrading mobile device behaviors intelligently, dynamically, and/or adaptively determine computing device behaviors that are to be observed, the number of behaviors that are to be observed, and the level of detail or granularity at which the mobile device behaviors are to be observed. The various aspects efficiently identify suspicious or performance-degrading mobile device behaviors without requiring an excessive amount of processing, memory, or energy resources.
Abstract:
A position fix for a mobile platform is determined using RSSI values for wireless signals received from access points (APs), at least one of which has dynamic transmission power control. The transmission power data for the APs is received from an entity separate from the APs, e.g., a central entity or a positioning assistance server. The RSSI values for wireless signals received from the APs are acquired, as is an RSSI heatmap. Using the transmission power data, the RSSI values and the RSSI heatmap, the position fix for the mobile platform is determined. The position fix may be determined by the mobile platform or a positioning assistance server. Additionally, a server may receive transmission power data for APs and may provide to a mobile platform RSSI heatmap information based on the transmission power data. The RSSI heatmap information may be, e.g., the transmission power data or a RSSI heatmap.
Abstract:
Described are devices, methods, techniques and systems for locating a portable services access transceiver (PSAT) for use in aiding emergency “911” services. In one implementation, one or more conditions indicative of movement of a PSAT may initiate a process for obtaining a new estimated location of the PSAT. In another implementation, a location of a PSAT may be determined or updated using indoor navigation techniques.
Abstract:
Systems, methods, and devices of the various aspects enable identification of anomalous application behavior. A computing device processor may detect network communication activity of an application on the computing device. The processor may identify one or more device states of the computing device, and one or more categories of the application. The processor may determine whether the application is behaving anomalously based on a correlation of the detected network communication activity of the application, the identified one or more device states of the computing device, and the identified one or more categories of the application.
Abstract:
Various embodiments include methods of evaluating device behaviors in a computing device and enabling white listing of particular behaviors. Various embodiments may include monitoring activities of a software application operating on the computing device, and generating a behavior vector information structure that characterizes a first monitored activity of the software application. The behavior vector information structure may be applied to a machine learning classifier model to generate analysis results. The analysis results may be used to classify the first monitored activity of the software application as one of benign, suspicious, and non-benign. A prompt may be displayed to the user that requests that the user select whether to whitelist the software application in response to classifying the first monitored activity of the software application as suspicious or non-benign. The first monitored activity may be added to a whitelist of device behaviors in response to receiving a user input.
Abstract:
Methods and apparatuses of providing assistance data of a venue to a mobile device are disclosed. According to aspects of the present disclosure, for the same area, multiple or different versions of assistance data may be generated. Restrictions may be applied by access area, such that positioning grid, heat maps, and maps can be restricted to certain sections of the venue; and point of interests (POIs) and search features may be provided based on at least one of user credentials, time-based restrictions, ticket-based restrictions, or loyalty-based restrictions, or any combination thereof.