Abstract:
Systems, apparatus and methods disclosed herein facilitate vision based mobile device location determination. In some embodiments, a method for estimating a position of a mobile device may comprise: detecting that the mobile device is in communication with at least one of a plurality of devices, where each of the plurality of devices associated with a corresponding device identifier. The capture of at least one image by an image sensor coupled to the mobile device may be triggered, based, in part on: the device identifier corresponding to the device in communication with the mobile device, and/or a field of view of the image sensor. A location of the mobile device may then be determined, based, in part, on the at least one captured image.
Abstract:
An estimated position of a pedestrian is obtained by detecting, by a movement sensor on the pedestrian, whether the pedestrian is moving, propagating particles based on a predictive pedestrian movement model upon detecting that the pedestrian is moving, and updating, by a wireless network, the position of the pedestrian upon failure by the movement sensor to detect that the pedestrian is moving. The movement sensor may be a pedometer. The wireless network may be a local wireless network such as a Wi-Fi network.
Abstract:
Various embodiments include systems and methods of determining whether a compromised access point is present in a communication network. A processor of a wireless communication device may predict one or more websites that the wireless communication device will access during a future session with the one or more websites. The processor may establish a secure connection with the communication network, request a digital certificate for one or more of the predicted websites, and store a digital certificate received from each of the predicted websites. The processor may determine whether a compromised access point is present in the communication network by comparing one of the digital certificates from the predicted websites with a digital certificate received from a website server during a current session.
Abstract:
Methods, systems and devices compute and use the execution session contexts of software applications to perform behavioral monitoring and analysis operations. A mobile device may be configured to monitor user activity and system activity of a software application, generate a shadow feature value that identifies actual execution session context of the software application during that activity, generate a behavior vector that incorporates context into the values describing behaviors, and determine whether the activity is malicious or benign based, at least in part, on the generated behavior vector. The mobile device processor may also be configured to intelligently determine whether the execution session context 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 session contexts of the software applications for which such determinations are relevant.
Abstract:
Systems, apparatus and methods for selecting a base station or a set of base stations for RTT measurements, or other interactive radio localization technique, to determine a position fix of a device are presented. The method imposes a processing load on only inactive or less active base stations. Busy or busier base stations are not used in the interactive radio localization technique. By imposing a processing load on only less active base stations, transmitting devices may be under loaded and encounter a more uniform processing delay, and thus provide a more accurate measurement resulting in a more accurate position fix.
Abstract:
Systems, apparatus, and methods for estimating the location of a wireless device include determining the location estimate based on accessible access points and without using a received signal strength indicator. In some embodiments, the location estimate is determined based on a center of mass of the accessible access points, a closest accessible access point, a center of mass of N access points, an average angle of the accessible access points, or a Parzen density of accessible access point distributions.
Abstract:
In an aspect, an apparatus obtains a first payload that is dynamically loaded by an application program of the apparatus. For example, the first payload may be dynamically loaded by an application program (e.g., during run time) for execution on the apparatus. The apparatus determines whether the first payload includes malicious content. The apparatus prevents execution of the first payload when the first payload includes the malicious content, and executes the first payload when the first payload does not include the malicious content.
Abstract:
Methods and systems for providing information associated with a location history of a mobile device to one or more applications are disclosed. A mobile device generates one or more location history records based on one or more locations of the mobile device, each location history record comprising one or more points of interest and a duration at the one or more points of interest, receives an information request from at least one application, determines a subset of the one or more location history records that meet criteria from the information request, determines a level of permission for the at least one application based on the information request and the subset of the one or more location history records, and provides information associated with the subset of the one or more location history records to the at least one application based on the level of permission.
Abstract:
A computing device processor may be configured with processor-executable instructions to implement methods of using behavioral analysis and machine learning techniques to evaluate the collective behavior of two or more software applications operating on the device. The processor may be configured to monitor the activities of a plurality of software applications operating on the device, collect behavior information for each monitored activity, generate a behavior vector based on the collected behavior information, apply the generated behavior vector to a classifier model to generate analysis information, and use the analysis information to classify a collective behavior of the plurality of software applications.
Abstract:
The position of a mobile device in a wireless network is refined by obtaining statistical samples of particles representing an initial estimated position of the device, obtaining one or more parameters of received signals from a set of one or more access points in the network, locking the set of one or more access points without using one or more additional access points to estimate the position, resampling the particles to obtain a second estimation of the position after locking the first set of one or more access points, and obtaining a refined estimated position based on a comparison of the first and second estimations of the position.