Abstract:
Various embodiments include methods, and computing devices implementing the methods, for authenticating vehicle information by polling selected sensors. A server computing device receiving vehicle information from a reporting vehicle may compare the received vehicle information to contextual information to generate a comparison result, and determine whether the received vehicle information should be evaluated with greater scrutiny based on the comparison result. The server computing device may select sensors for polling based on the received vehicle information (and in response to determining that the received vehicle information should be evaluated with greater scrutiny), and poll the selected sensors to received sensor information. The server computing device may use the received sensor information to corroborate the received vehicle information, and perform a responsive action based on the result of the corroboration.
Abstract:
The disclosure relates to software that provides fine-grained user control over when and how a software-based privacy filter is used to reduce clarity and/or visibility associated with content rendered on a display screen. For example, according to various aspects, the software may have access to the display screen and various other components that can be used to detect and/or track a current context associated with information displayed on the screen. As such, based on the current context, the software may determine an area displayed on a screen having current interest to one or more authorized users and activate a software-based privacy filter configured to reduce the clarity and/or visibility associated with information displayed on the screen outside the area having the current interest to the one or more authorized users as needed (e.g., based on a sensitivity level associated with the displayed information, sensor-based inputs indicating a sensitive context, etc.).
Abstract:
A method and apparatus for performing authentication may comprise: determining a first value of a dynamic password applicable for a first scenario, the dynamic password having a plurality of values for a plurality of scenarios defined by at least one parameter; authenticating a user in the first scenario by a device based on the first value of the dynamic password; determining a second value of the dynamic password applicable for a second scenario; and authenticating the user in the second scenario by the device based on the second value of the dynamic password.
Abstract:
In some embodiments, a method for generating a mobile device's user interface is provided. The method may include: receiving, via the mobile device, input from a user of the mobile device, the input being related to a property for presenting dynamic context-dependent informational cues; determining a context; identifying a subset of a set of informational cues, the subset being associated with the context; and predominately presenting, via the mobile device, the identified subset to the user, the presentation being predominate as compared to any presentation of other informational cues in the set of informational cues, wherein the presentation accords with the received user input.
Abstract:
In some embodiments, a method for generating a mobile device's user interface is provided. The method may include: receiving, via the mobile device, input from a user of the mobile device, the input being related to a property for presenting dynamic context-dependent informational cues; determining a context; identifying a subset of a set of informational cues, the subset being associated with the context; and predominately presenting, via the mobile device, the identified subset to the user, the presentation being predominate as compared to any presentation of other informational cues in the set of informational cues, wherein the presentation accords with the received user input.
Abstract:
Various embodiments include methods, and computing devices implementing the methods, for analyzing sensor information to identify an abnormal vehicle behavior. A computing device may monitor sensors (e.g., a closely-integrated vehicle sensor, a loosely-integrated vehicle sensor, a non-vehicle sensor, etc.) in the vehicle to collect the sensor information, analyze the collected sensor information to generate an analysis result, and use the generated analysis result to determine whether a behavior of the vehicle is abnormal. The computing device may also generate a communication message in response to determining that the behavior of the vehicle is abnormal, and send the generated communication message to an external entity.
Abstract:
The disclosure generally relates to behavioral analysis to automate monitoring Internet of Things (IoT) device health in a direct and/or indirect manner. In particular, normal behavior associated with an IoT device in a local IoT network may be modeled such that behaviors observed at the IoT device may be compared to the modeled normal behavior to determine whether the behaviors observed at the IoT device are normal or anomalous. Accordingly, in a distributed IoT environment, more powerful “analyzer” devices can collect behaviors locally observed at other (e.g., simpler) “observer” devices and conduct behavioral analysis across the distributed IoT environment to detect anomalies potentially indicating malicious attacks, malfunctions, or other issues that require customer service and/or further attention. Furthermore, devices with sufficient capabilities may conduct (local) on-device behavioral analysis to detect anomalous conditions without sending locally observed behaviors to another aggregator device and/or analyzer device.
Abstract:
Various embodiments include methods, and computing devices implementing the methods, for analyzing sensor information to identify an abnormal vehicle behavior. A computing device may monitor sensors (e.g., a closely-integrated vehicle sensor, a loosely-integrated vehicle sensor, a non-vehicle sensor, etc.) in the vehicle to collect the sensor information, analyze the collected sensor information to generate an analysis result, and use the generated analysis result to determine whether a behavior of the vehicle is abnormal. The computing device may also generate a communication message in response to determining that the behavior of the vehicle is abnormal, and send the generated communication message to an external entity.
Abstract:
Various embodiments include methods, and computing devices implementing the methods, for authenticating vehicle information by polling selected sensors. A server computing device receiving vehicle information from a reporting vehicle may compare the received vehicle information to contextual information to generate a comparison result, and determine whether the received vehicle information should be evaluated with greater scrutiny based on the comparison result. The server computing device may select sensors for polling based on the received vehicle information (and in response to determining that the received vehicle information should be evaluated with greater scrutiny), and poll the selected sensors to received sensor information. The server computing device may use the received sensor information to corroborate the received vehicle information, and perform a responsive action based on the result of the corroboration.
Abstract:
Systems, methods, and devices of the various aspects enable detecting anomalous electromagnetic (EM) emissions from among a plurality of electronic devices. A device processor may receive EM emissions of a plurality of electronic devices, wherein the receiving device has no previous information about any of the plurality of electronic devices. The device processor may cross-correlate the EM emissions of the plurality of electronic devices over time. The device processor may identify a difference of the cross-correlated EM emissions from earlier cross-correlated EM emissions. The device processor may determine that the difference of the cross-correlated EM emissions from the earlier cross-correlated EM emissions indicates an anomaly in one or more of the plurality of electronic devices.