Abstract:
The embodiments include methods and systems for detecting advertising fraud in a computing device by monitoring information received in a receiver component of the computing device, monitoring information received in a render component of the computing device, comparing the information received in the receiver component to the information received in the render component to generate comparison results, using the comparison results to determine whether there are discrepancies between the received information and the rendered information, and performing fraud prevention operations in response to determine that there are discrepancies between the received information and the rendered information. The fraud prevention operations may include dropping a connection to cease receiving the information in the receiver component, sending negative or position feedback to the service provider or a security server, and performing other similar operations.
Abstract:
Disclosed is a method for malicious activity detection in a mobile station of a particular model. In the method, generic malicious behavior patterns are received from a network-based malicious behavior profiling system. Mobile-station-model-specific-behavior-analysis algorithms are generated in the mobile station based on the generic malicious behavior patterns. Mobile station operations may be observed to generate a mobile station activity observation. The mobile station activity observation may be analyzed using the mobile-station-model-specific-behavior-analysis algorithms to generate an activity analysis. Malicious activity may be detected based on the activity analysis.
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:
A behavior-based security system of a computing device may be protected from non-benign behavior, malware, and cyber attacks by configuring the device to work in conjunction with another component (e.g., a server) to monitor the accuracy and performance of the security system, and determine whether the system is working correctly, efficiently, or as expected. This may be accomplished via the server generating artificial attack software, sending the generated artificial attack software to the mobile device to simulate non-benign behavior in the mobile device, such as a cyber attack, and determining whether the behavior-based security system of the mobile device responded adequately to the simulated non-benign behavior. The sever may send a dead-man signal to the mobile device in response to determining that the behavior-based security system of the mobile device did not respond adequately to the simulated non-benign behavior.
Abstract:
Techniques are provided for a mobile, which may be implemented in various methods, apparatuses, and/or articles of manufacture to obtain an encoded routability graph representative of feasible paths for an indoor environment, along with values indicative of likelihoods of transition at certain junctions identifiable in the encoded routability graph, and determine one or both of an estimated position or an estimated direction of travel of the mobile device.
Abstract:
Methods, systems and devices for classifying mobile device behaviors of a first mobile device may include the first mobile device monitoring mobile device behaviors to generate a behavior vector, and applying the behavior vector to a first classifier model to obtain a first determination of whether a mobile device behavior is benign or not benign. The first mobile device may also send the behavior vector to a second mobile device, which may receive and apply the behavior vector to a second classifier model to obtain a second determination of whether the mobile device behavior is benign or not benign. The second mobile device may send the second determination to the first mobile device, which may receive the second determination, collate the first determination and the second determination to generate collated results, and determine whether the mobile device behavior is benign or not benign based on the collated results.
Abstract:
The subject matter disclosed herein relates to a system and method for receiving incentives on a mobile device. A first message may be received based on a first location of the mobile device, such that the first message indicates to a user of the mobile device that an incentive will be provided if the user remains within a certain proximity a waypoint for a predetermined length of time, and a second message including may be received if a second location of the mobile device is within the certain proximity of the waypoint, such that an elapsed time between a determination of the first location and a determination of the second location is equal to or greater than the predetermined length of time.
Abstract:
Methods and systems for classifying mobile device behavior include generating a full classifier model that includes a finite state machine suitable for conversion into boosted decision stumps and/or which describes all or many of the features relevant to determining whether a mobile device behavior is benign or contributing to the mobile device's degradation over time. A mobile device may receive the full classifier model along with sigmoid parameters and use the model to generate a full set of boosted decision stumps from which a more focused or lean classifier model is generated by culling the full set to a subset suitable for efficiently determining whether mobile device behavior are benign. Results of applying the focused or lean classifier model may be normalized using a sigmoid function, with the resulting normalized result used to determine whether the behavior is benign or non-benign.
Abstract:
Handover parameter settings are automatically adapted in access points in a system to improve handover performance. Reactive detection techniques are employed for identifying different types of handover-related failures and adapting handover parameters based on this detection. Messaging schemes are also employed for providing handover-related information to access points. Proactive detection techniques also may be used for identifying conditions that may lead to handover-related failures and then adapting handover parameters in an attempt to prevent such handover-related failures. Ping-ponging may be mitigated by adapting handover parameters based on analysis of access terminal visited cell history acquired by access points in the system. In addition, configurable parameters (e.g., timer values) may be used to detect handover-related failures.
Abstract:
Methods and apparatuses are provided which may be implemented in various devices to provide navigation assistance data and/or the like to a mobile station with regard to at least one of a plurality of different indoor regions. For example, a computing platform of a map inference device may establish encoded metadata for at least a portion of an indoor region based, at least in part, on an electronic map, an access point locator, or some combination thereof, and provide such encoded metadata to a repository device that may provide at least a portion of the encoded metadata to a mobile station.