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, 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 systems for classifying mobile device behavior include configuring a server use a large corpus of mobile device behaviors to generate 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 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. Boosted decision stumps may be culled by selecting all boosted decision stumps that depend upon a limited set of test conditions.

Abstract: A computing device processor may be configured with processor-executable instructions to implement methods of using behavioral analysis and machine learning techniques to identify, prevent, correct, and/or otherwise respond to malicious or performance-degrading behaviors of the computing device. As part of these operations, the processor may perform multifactor authentication operations that include determining one or more of a transaction type criticality value, a user confidence value, a software integrity confidence value, and a historical behavior value, using the one or more of these values to determine a number of authentication factors that are be evaluated when authenticating a user of the computing device, and authenticating the user by evaluating the determined number of authentication factors.

Abstract: Various aspects provide systems and methods for optimizing hardware monitoring on a computing device. A computing device may receive a monitoring request to monitor a portion of code or data within a process executing on the computing device. The computing device may generate from the monitoring request a first monitoring configuration parameter for a first hardware monitoring component in the computing device and may identify a non-optimal event pattern that occurs while the first hardware monitoring component monitors the portion of code or data according to the first monitoring configuration parameter. The computing device may apply a transformation to the portion of code or data and reconfigure the first hardware monitoring component by modifying the first monitoring configuration parameter in response to the transformation of the portion of code or data.

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.

Abstract: Various embodiments include a honeypot system configured to trigger malicious activities by malicious applications using a behavioral analysis algorithm and dynamic resource provisioning. A method performed by a processor of a computing device, which may be a mobile computing device, may include determining whether or not a target application currently executing on the computing device is potentially malicious based, at least in part, on the analysis, predicting a triggering condition of the target application in response to determining the target application is potentially malicious, provisioning one or more resources based, at least in part, on the predicted triggering condition, monitoring activities of the target application corresponding to the provisioned one or more resources, and determining whether or not the target application is a malicious application based, at least in part, on the monitored activities. The resources may be device components (e.g., network interface(s), sensor(s), etc.

Abstract: Various embodiments include methods implemented on a computing device for analyzing a program executing within a virtual environment on the computing device. The methods may include determining whether the program is attempting to detect whether it is being executed within the virtual environment, and analyzing the program within a protected mode of the computing device in response to determining that the program is attempting to detect whether it is being executed within the virtual environment.

Abstract: Confusion resulting from assigning the same node identifier to multiple nodes is resolved through the use of confusion detection techniques and the use of unique identifiers for the nodes. In some aspects an access point and/or an access terminal may perform operations relating to detecting confusion and/or providing a unique identifier to resolve confusion.

Abstract: Method and devices of detecting a malware infection of a computing device in a communication network are disclosed. A computing device may monitor outputs of temperature sensors associated with elements of the computing device. The monitored outputs of the temperature sensors may be compared to a profile of temperatures associated with normal operation of the computing device. A deviation of the monitored temperatures from the profile of temperatures associated with normal operation may be reported. The profile of temperatures associated with the normal operation of the computing device may be learned based on temperature sensor data obtained during normal operations. Learning the profile of temperatures may include monitoring outputs of temperature sensors associated with elements of the computing device during normal operation of the computing device and storing the monitored outputs as one or more profiles of temperatures associated with normal operation of the computing device.

Abstract: Local breakout is provided by one or more nodes (e.g., a local access point and/or a local gateway) in a wireless network to facilitate access to one or more local services. In conjunction with local breakout, multiple IP points of presence relating to different levels of service may be provided for an access point. For example, one point of presence may relate to a local service while another point of presence may relate to a core network service.

Abstract: Methods, systems, and devices for providing data from a server to a UAV enable the UAV to navigate with respect to areas of restricted air space (“restricted areas”). A server may receive from a UAV, a request for restricted area information based on a position of the UAV. The server may determine boundaries of a surrounding area containing the position of the UAV and a number of restricted areas. The server may transmit coordinate information to the UAV defining the restricted areas contained within the surrounding area.

Abstract: Methods and apparatuses are provided which may be implemented in various devices to generate positioning assistance data and/or the like by mobile station with regard to at least one of a plurality of different indoor regions.

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: Methods, and computing devices implementing the methods, improve the efficiency and performance of a comprehensive behavioral monitoring and analysis system that is configured to predict whether a software application is causing undesirable or performance depredating behavior. The behavioral monitoring and analysis system may be configured to quickly and efficiently classify certain software applications as being benign by generating a behavior vector that characterizes the activities of the software application, determining whether the generated behavior vector includes a distinguishing behavior or behavioral clue identifying the software application as a trusted software application, and classifying the software application as benign in response to determining that the generated behavior vector includes a distinguishing behavior identifying the software application as a trusted software application.

Abstract: Methods, systems and devices for communicating behavior analysis information using an application programming interface (API) may include receiving via the API a request to register the second module to access an operation of a behavioral monitoring system of the mobile computing device, and exchanging authentication information between the first module and the second module to accomplish mutual authentication. Aspects may include receiving via the API a request for version identification information that may be used by the server to determine how to interpret, evaluate, or crowd-source information, and exchanging version identification information between the first module and the second module to cause the second module to send the information to the server. Aspects may further include receiving via the API a provision malware model request including a command causing the first module to send a malware or classifier model to a behavioral monitoring system of the mobile computing device.

Abstract: Various embodiments include methods for detecting software attacks on a process executing on a computing device. Various embodiment methods may include monitoring structural attributes of a plurality of virtual memory regions utilized by the process, and comparing the monitored structural attributes to the expected structural attributes of the plurality of VMRs. Various embodiment methods may further include determining whether the monitored structural attributes represent anomalous behavior of the process based on the comparison between the monitored structural attributes and the expected structural attributes.

Abstract: In one implementation, a method may comprise: determining a topological representation of an indoor portion of a building based, at least in part, on positions or number of lines in an image of the indoor portion of the building; and comparing the topological representation to one or more stored topological representations, for example in a digital map of the building, to determine a potential position of the indoor portion of the building.

Abstract: Confusion resulting from assigning the same node identifier to multiple nodes is resolved through the use of confusion detection techniques and the use of unique identifiers for the nodes. In some aspects a network may provide a time gap (e.g., an asynchronous time gap) during which an access terminal may temporarily cease monitoring transmissions from a source node so that the access terminal may acquire a unique identifier from a target node. In some aspects an access terminal may commence handover operations at a target node after determining whether the access terminal is allowed to access the target node. In some aspects a source node may prepare several target nodes for potential handover in the event confusion is detected or likely. Here, the source node may send information relating to the preparation of the potential target nodes to the access terminal whereby the access terminal uses the handover preparation information to initiate a handover at that target node.

Abstract: Techniques are provided which may be implemented using various methods and/or apparatuses for use in providing positioning assistance data to mobile stations. For example, a method in a first server in a cellular network may comprise sending a request for location information to a mobile station. A request for assistance data indicating an address of a second server may be received from the mobile station. Based on this address, a request for local mapping data may be sent to the second server. The local mapping data may be received from the second server. The assistance data based on the local mapping data and identifying a wireless signal transmitter may be sent to the mobile station. The location information, based on the assistance data and on a positioning operation based on a wireless signal transmitted by the identified wireless signal transmitter may be received from the mobile station.