Abstract: A file-level snapshot access service provides direct access to individual files included in a snapshot for virtual volume of a block-storage service without requiring a volume to be re-created from the snapshot, attached to a computing device, or mounted in a file system. For example, a user/client may directly retrieve individual files from specified snapshots via a user interface/API of the file-level snapshot access service. Additionally, the file-level snapshot access service is configured to provide a listing of files included in a given snapshot. In some embodiments, a file-level snapshot access service may provide direct access to individual files included in snapshots generated for other types of storage systems, such as an object-based storage system.

Abstract: Provided is a system for facilitating recovery of deleted computing resources in a cloud network environment. A centralized resource recovery service may be in network communication with a plurality of resource management services that are each configured to create, modify, or delete their respective computing resources such as data storage volumes, databases, compute instances, and the like. The resource recovery service may be configured to receive a delete request associated with a resource managed by one of the resource management services, and cause the resource to be retained in a recovery bin based on the resource satisfying one of a plurality of resource recovery conditions used to manage resource recovery across the resource management services.

Abstract: A file-level snapshot access service provides direct access to individual files included in a snapshot for virtual volume of a block-storage service without requiring a volume to be re-created from the snapshot, attached to a computing device, or mounted in a file system. For example, a user/client may directly retrieve individual files from specified snapshots via a user interface/API of the file-level snapshot access service. Additionally, the file-level snapshot access service is configured to provide a listing of files included in a given snapshot. In some embodiments, a file-level snapshot access service may provide direct access to individual files included in snapshots generated for other types of storage systems, such as an object-based storage system.

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. Various aspects may correct suspicious or performance-degrading mobile device behaviors. Various aspects may prevent identified suspicious or performance-degrading mobile device behaviors from degrading the performance and power utilization levels of a mobile device over time. Various aspects may restore an aging mobile device to its original performance and power utilization levels.

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, systems and devices for generating data models in a communication system may include applying machine learning techniques to generate a first family of classifier models using a boosted decision tree to describe a corpus of behavior vectors. Such behavior vectors may be used to compute a weight value for one or more nodes of the boosted decision tree. Classifier models factors having a high probably of determining whether a mobile device behavior is benign or not benign based on the computed weight values may be identified. Computing weight values for boosted decision tree nodes may include computing an exclusive answer ratio for generated boosted decision tree nodes. The identified factors may be applied to the corpus of behavior vectors to generate a second family of classifier models identifying fewer factors and data points relevant for enabling the mobile device to determine whether a behavior is benign or not benign.

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: 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. Various aspects may correct suspicious or performance-degrading mobile device behaviors. Various aspects may prevent identified suspicious or performance-degrading mobile device behaviors from degrading the performance and power utilization levels of a mobile device over time. Various aspects may restore an aging mobile device to its original performance and power utilization levels.

Abstract: Methods, devices and systems for monitoring behaviors of a mobile computing device include observing in a non-master processing core a portion of a mobile device behavior that is relevant to the non-master processing core, generating a behavior signature that describes the observed portion of the mobile device behavior, and sending the generated behavior signature to a master processing core. The master processing core combines two or more behavior signatures received from the non-master processing cores to generate a global behavior vector, which may be used by an analyzer module to determine whether a distributed software application is not benign.

Abstract: Methods, devices and systems for monitoring behaviors of a mobile computing device include observing in a non-master processing core a portion of a mobile device behavior that is relevant to the non-master processing core, generating a behavior signature that describes the observed portion of the mobile device behavior, and sending the generated behavior signature to a master processing core. The master processing core combines two or more behavior signatures received from the non-master processing cores to generate a global behavior vector, which may be used by an analyzer module to determine whether a distributed software application is benign or not benign.

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. Various aspects may correct suspicious or performance-degrading mobile device behaviors. Various aspects may prevent identified suspicious or performance-degrading mobile device behaviors from degrading the performance and power utilization levels of a mobile device over time. Various aspects may restore an aging mobile device to its original performance and power utilization levels.

Abstract: Mobile computing devices may be configured to compile and execute portions of a general purpose software application in an auxiliary processor (e.g., a DSP) of a multiprocessor system by reading and writing information to a shared memory. A first process (P1) on the applications processor may request address negotiation with a second process (P2) on the auxiliary processor, obtain a first address map from a first operating system, and send the first address map to the auxiliary processor. The second process (P2) may receive the first address map, obtain a second address map from a second operating system, identify matching addresses in the first and second address maps, store the matching addresses as common virtual addresses, and send the common virtual addresses back to the applications processor. The first and second processes (i.e., P1 and P2) may each use the common virtual addresses to map physical pages to the memory.

Abstract: Disclosed is a method for efficient behavioral analysis on a mobile station. In the method, one or more first behavioral characteristics associated with a first state of a finite state machine are observed. The one or more first behavioral characteristics may comprise a first subset of observable behavioral characteristics. The mobile station transitions from the first state to a second state. One or more second behavioral characteristics associated with the second state of the finite state machine are observed. The one or more second behavioral characteristics may comprise a second subset of the observable behavioral characteristics.

Abstract: Methods, devices and systems for monitoring behaviors of a mobile computing device include observing in a non-master processing core a portion of a mobile device behavior that is relevant to the non-master processing core, generating a behavior signature that describes the observed portion of the mobile device behavior, and sending the generated behavior signature to a master processing core. The master processing core combines two or more behavior signatures received from the non-master processing cores to generate a global behavior vector, which may be used by an analyzer module to determine whether a distributed software application is not benign.

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, 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: Methods, devices and systems for monitoring behaviors of a mobile computing device include observing in a non-master processing core a portion of a mobile device behavior that is relevant to the non-master processing core, generating a behavior signature that describes the observed portion of the mobile device behavior, and sending the generated behavior signature to a master processing core. The master processing core combines two or more behavior signatures received from the non-master processing cores to generate a global behavior vector, which may be used by an analyzer module to determine whether a distributed software application is benign or not benign.

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. Various aspects may correct suspicious or performance-degrading mobile device behaviors. Various aspects may prevent identified suspicious or performance-degrading mobile device behaviors from degrading the performance and power utilization levels of a mobile device over time. Various aspects may restore an aging mobile device to its original performance and power utilization levels.

Abstract: Methods, systems and devices for generating data models in a communication system may include applying machine learning techniques to generate a first family of classifier models using a boosted decision tree to describe a corpus of behavior vectors. Such behavior vectors may be used to compute a weight value for one or more nodes of the boosted decision tree. Classifier models factors having a high probably of determining whether a mobile device behavior is benign or not benign based on the computed weight values may be identified. Computing weight values for boosted decision tree nodes may include computing an exclusive answer ratio for generated boosted decision tree nodes. The identified factors may be applied to the corpus of behavior vectors to generate a second family of classifier models identifying fewer factors and data points relevant for enabling the mobile device to determine whether a behavior is benign or not benign.

Abstract: Methods, systems and devices for generating data models in a client-cloud communication system may include applying machine learning techniques to generate a first family of classifier models that describe a cloud corpus of behavior vectors. Such vectors may be analyzed to identify factors in the first family of classifier models that have the highest probably of enabling a mobile device to conclusively determine whether a mobile device behavior is malicious or benign. Based on this analysis, a a second family of classifier models may be generated that identify significantly fewer factors and data points as being relevant for enabling the mobile device to conclusively determine whether the mobile device behavior is malicious or benign based on the determined factors. A mobile device classifier module based on the second family of classifier models may be generated and made available for download by mobile devices, including devices contributing behavior vectors.