System, apparatus, and method for adaptive observation of mobile device behavior

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.

BACKGROUND

Cellular and wireless communication technologies have seen explosive growth over the past several years. This growth has been fueled by better communications, hardware, larger networks, and more reliable protocols. Wireless service providers are now able to offer their customers an ever-expanding array of features and services, and provide users with unprecedented levels of access to information, resources, and communications. To keep pace with these service enhancements, mobile electronic devices (e.g., cellular phones, tablets, laptops, etc.) have become more powerful and complex than ever. This complexity has created new opportunities for malicious software, software conflicts, hardware faults, and other similar errors or phenomena to negatively impact a mobile device's long-term and continued performance and power utilization levels. Accordingly, identifying and correcting the conditions and/or mobile device behaviors that may negatively impact the mobile device's long term and continued performance and power utilization levels is beneficial to consumers.

SUMMARY

The various aspects include methods of observing mobile device behaviors over a period of time to recognize mobile device behaviors inconsistent with normal operation patterns, the method including dynamically determining in a processor which mobile device behaviors are to be observed, and adaptively observing the determined mobile device behaviors to identify a suspicious mobile device behavior from a limited set of observations. In an aspect, the method may include controlling an observation granularity of the adaptive observation. In a further aspect, dynamically determining which mobile device behaviors are to be observed may include observing mobile device behaviors over a period of time to recognize mobile device behaviors that are inconsistent with normal operation patterns, and identifying a limited set of behaviors associated with inconsistent operations as the mobile device behaviors to be observed.

In a further aspect, the operations of dynamically determining which mobile device behaviors are to be observed and controlling the observation granularity of the adaptive observation are accomplished within an observer daemon operating within a system kernel. In a further aspect, identifying a limited set of behaviors associated with inconsistent operations as the mobile device behaviors to be observed may include receiving behavior inputs from one or more of a high-level application, a system kernel, and a driver API after filtering by an adaptive filter, receiving a context input regarding operations of the mobile device, performing spatial correlations of the received behaviors and the received context, and generating a behavior vector. In a further aspect, identifying a limited set of behaviors associated with inconsistent operations as the mobile device behaviors to be observed further may include performing temporal correlations of the received behaviors and the received context input, in which generating a behavior vector may include generating a behavior vector based on a result of the spatial and temporal correlations. In a further aspect, identifying a limited set of behaviors associated with inconsistent operations as behaviors to be observed further may include storing the generated behavior vector in a secure memory.

Further aspects include a computing device that may include a processor, means for dynamically determining which mobile device behaviors are to be observed to identify behaviors inconsistent with normal operation patterns, and means for adaptively observing the determined mobile device behaviors to identify a suspicious mobile device behavior from a limited set of observations. In an aspect, the computing device may include means for controlling an observation granularity of the adaptive observation. In a further aspect, means for dynamically determining which mobile device behaviors are to be observed may include means for observing mobile device behaviors over a period of time to recognize mobile device behaviors that are inconsistent with normal operation patterns, and means for identifying a limited set of behaviors associated with inconsistent operations as the mobile device behaviors to be observed.

In a further aspect, the computing device may include observer daemon means, in which the observer daemon means may include means for dynamically determining which mobile device behaviors are to be observed and means for controlling the observation granularity of the adaptive observation within an observer daemon operating within a system kernel. In a further aspect, means for identifying a limited set of behaviors associated with inconsistent operations as the mobile device behaviors to be observed may include means for receiving behavior inputs from one or more of a high-level application, a system kernel, and a driver API after filtering by an adaptive filter, means for receiving a context input regarding operations of the mobile device, means for performing spatial correlations of the received behaviors and the received context, and means for generating a behavior vector. In a further aspect, means for identifying a limited set of behaviors associated with inconsistent operations as the mobile device behaviors to be observed further may include means for performing temporal correlations of the received behaviors and the received context input, in which means for generating a behavior vector may include means for generating a behavior vector based on a result of the spatial and temporal correlations. In a further aspect, means for identifying a limited set of behaviors associated with inconsistent operations as behaviors to be observed further may include means for storing the generated behavior vector in a secure memory.

Further aspects include a computing device that includes a processor configured with processor-executable instructions to perform operations including dynamically determining which mobile device behaviors are to be observed to identify behaviors inconsistent with normal operation patterns, and adaptively observing the determined mobile device behaviors to identify a suspicious mobile device behavior from a limited set of observations. In an aspect, the processor is configured with processor-executable instructions to perform operations further including controlling an observation granularity of the adaptive observation.

In a further aspect, the processor is configured with processor-executable instructions such that dynamically determining which mobile device behaviors are to be observed may include observing mobile device behaviors over a period of time to recognize mobile device behaviors that are inconsistent with normal operation patterns, and identifying a limited set of behaviors associated with inconsistent operations as the mobile device behaviors to be observed. In a further aspect, the processor is configured with processor-executable instructions such that the operations of dynamically determining which mobile device behaviors are to be observed and controlling the observation granularity of the adaptive observation are accomplished within an observer daemon operating within a system kernel. In a further aspect, the processor is configured with processor-executable instructions such that identifying a limited set of behaviors associated with inconsistent operations as the mobile device behaviors to be observed may include receiving behavior inputs from one or more of a high-level application, a system kernel, and a driver API after filtering by an adaptive filter, receiving a context input regarding operations of the mobile device, performing spatial correlations of the received behaviors and the received context, and generating a behavior vector.

In a further aspect, the processor is configured with processor-executable instructions such that identifying a limited set of behaviors associated with inconsistent operations as the mobile device behaviors to be observed further may include performing temporal correlations of the received behaviors and the received context input, and in which the processor is configured with processor-executable instructions such that generating a behavior vector may include generating a behavior vector based on a result of the spatial and temporal correlations. In a further aspect, the processor is configured with processor-executable instructions such that identifying a limited set of behaviors associated with inconsistent operations as behaviors to be observed further may include storing the generated behavior vector in a secure memory.

Further aspects include a non-transitory server-readable storage medium having stored thereon processor-executable instructions configured to cause a computing device to perform operations that may include dynamically determining which mobile device behaviors are to be observed to recognize mobile device behaviors inconsistent with normal operation patterns, and adaptively observing the determined mobile device behaviors to identify a suspicious mobile device behavior from a limited set of observations. In an aspect, the stored processor-executable instructions may be configured to cause a processor of a mobile device to perform operations further including controlling an observation granularity of the adaptive observation.

In a further aspect, dynamically determining which mobile device behaviors are to be observed may include observing mobile device behaviors over a period of time to recognize mobile device behaviors that are inconsistent with normal operation patterns, and identifying a limited set of behaviors associated with inconsistent operations as the mobile device behaviors to be observed. In a further aspect, operations of dynamically determining which mobile device behaviors are to be observed and controlling the observation granularity of the adaptive observation are accomplished within an observer daemon operating within a system kernel. In a further aspect, identifying a limited set of behaviors associated with inconsistent operations as the mobile device behaviors to be observed may include receiving behavior inputs from one or more of a high-level application, a system kernel, and a driver API after filtering by an adaptive filter, receiving a context input regarding operations of the mobile device, performing spatial correlations of the received behaviors and the received context, and generating a behavior vector.

In a further aspect, identifying a limited set of behaviors associated with inconsistent operations as the mobile device behaviors to be observed further may include performing temporal correlations of the received behaviors and the received context input, in which generating a behavior vector may include generating a behavior vector based on a result of the spatial and temporal correlations. In a further aspect, identifying a limited set of behaviors associated with inconsistent operations as behaviors to be observed further may include storing the generated behavior vector in a secure memory.

The various aspects also include methods of performing on a mobile device processor real-time behavior analysis of mobile device behaviors to generate coarse observations, identifying suspicious behavior from the coarse observations, dynamically determining the mobile device behaviors that require further observation in greater detail, dynamically determining a level of detail required for the further observation, performing finer observations based on the determined level of detail required for the further observation, and identifying suspicious behavior from the finer observations. In an aspect, the method may include performing mobile devices operations to correct the identified suspicious behavior. In a further aspect, the method may include performing spatial and temporal correlations of observed mobile device behaviors to detect high-level mobile device behaviors.

Further aspects include a computing device that may include a processor, means for performing real-time behavior analysis of mobile device behaviors to generate coarse observations, means for identifying suspicious behavior from the coarse observations, means for dynamically determining the mobile device behaviors that require further observation in greater detail, means for dynamically determining a level of detail required for the further observation, means for performing finer observations based on the determined level of detail required for the further observation, and means for identifying suspicious behavior from the finer observations. In an aspect, the computing device may include means for performing mobile devices operations to correct the identified suspicious behavior. In a further aspect, the computing device may include means for performing spatial and temporal correlations of observed mobile device behaviors to detect high-level mobile device behaviors.

Further aspects include a computing device that includes a processor configured with processor-executable instructions to perform operations including performing real-time behavior analysis of mobile device behaviors to generate coarse observations, identifying suspicious behavior from the coarse observations, dynamically determining the mobile device behaviors that require further observation in greater detail, dynamically determining a level of detail required for the further observation, performing finer observations based on the determined level of detail required for the further observation, and identifying suspicious behavior from the finer observations. In an aspect, the processor is configured with processor-executable instructions to perform operations further including performing mobile devices operations to correct the identified suspicious behavior. In a further aspect, the processor is configured with processor-executable instructions to perform operations further including performing spatial and temporal correlations of observed mobile device behaviors to detect high-level mobile device behaviors.

Further aspects include a non-transitory server-readable storage medium having stored thereon processor-executable instructions configured to cause a computing device to perform operations that may include performing real-time behavior analysis of mobile device behaviors to generate coarse observations, identifying suspicious behavior from the coarse observations, dynamically determining the mobile device behaviors that require further observation in greater detail, dynamically determining a level of detail required for the further observation, performing finer observations based on the determined level of detail required for the further observation, and identifying suspicious behavior from the finer observations. In an aspect, the stored processor-executable software instructions are configured to cause a processor to perform operations including performing mobile devices operations to correct the identified suspicious behavior. In a further aspect, the stored processor-executable software instructions are configured to cause a processor to perform operations including performing spatial and temporal correlations of observed mobile device behaviors to detect high-level mobile device behaviors.

DETAILED DESCRIPTION

The terms “mobile computing device” and “mobile device” are used interchangeably herein to refer to any one or all of cellular telephones, smartphones, personal or mobile multi-media players, personal data assistants (PDA's), laptop computers, tablet computers, smartbooks, ultrabooks, palm-top computers, wireless electronic mail receivers, multimedia Internet enabled cellular telephones, wireless gaming controllers, and similar personal electronic devices which include a memory, a programmable processor for which performance is important, and operate under battery power such that power conservation methods are of benefit. While the various aspects are particularly useful for mobile computing devices, such as smartphones, which have limited resources and run on battery, the aspects are generally useful in any electronic device that includes a processor and executes application programs.

Computer program code or “program code” for execution on a programmable processor for carrying out operations of the various aspects may be written in a high level programming language such as C, C++, C#, Smalltalk, Java, JavaScript, Visual Basic, a Structured Query Language (e.g., Transact-SQL), Perl, or in various other programming languages. Program code or programs stored on a computer readable storage medium as used herein to refer to machine language code (such as object code) whose format is understandable by a processor.

The term “performance degradation” is used herein to refer to a wide variety of undesirable mobile device operations and characteristics, such as longer processing times, lower battery life, loss of private data, malicious economic activity (e.g., sending unauthorized premium SMS message), operations relating to commandeering the mobile device or utilizing the phone for spying or botnet activities, etc.

Many mobile computing devices operating system kernels are organized into a user space (where non-privileged code runs) and a kernel space (where privileged code runs). This separation is of particular importance in Android® and other general public license (GPL) environments where code that is part of the kernel space must be GPL licensed, while code running in the user-space may not be GPL licensed. It should be understood that the various software components/modules discussed here may be implemented in either the kernel space or the user space, unless expressly stated otherwise.

The term “system on chip” (SOC) is used herein to refer to a single integrated circuit (IC) chip that contains multiple resources and/or processors integrated on a single substrate. A single SOC may contain circuitry for digital, analog, mixed-signal, and radio-frequency functions. A single SOC may also include any number of general purpose and/or specialized processors (digital signal processors, modem processors, video processors, etc.), memory blocks (e.g., ROM, RAM, Flash, etc.), and resources (e.g., timers, voltage regulators, oscillators, etc.). SOCs may also include software for controlling the integrated resources and processors, as well as for controlling peripheral devices.

The term “multicore processor” is used herein to refer to a single integrated circuit (IC) chip or chip package that contains two or more independent processing cores (e.g., CPU cores) configured to read and execute program instructions. A SOC may include multiple multicore processors, and each processor in an SOC may be referred to as a core. The term “multiprocessor” is used herein to refer to a system or device that includes two or more processing units configured to read and execute program instructions.

Generally, the performance and power efficiency of a mobile device degrade over time. Recently, anti-virus companies (e.g., McAfee, Symantec, etc.) have begun marketing mobile anti-virus, firewall, and encryption products that aim to slow this degradation. However, many of these solutions rely on the periodic execution of a computationally-intensive scanning engine on the mobile device, which may consume many of the mobile device's processing and battery resources, slow or render the mobile device useless for extended periods of time, and/or otherwise degrade the user experience. In addition, these solutions are typically limited to detecting known viruses and malware, and do not address the multiple complex factors and/or the interactions that often combine to contribute to a mobile device's degradation over time (e.g., when the performance degradation is not caused by viruses or malware). For these and other reasons, existing anti-virus, firewall, and encryption products do not provide adequate solutions for identifying the numerous factors that may contribute to a mobile device's degradation over time, for preventing mobile device degradation, or for efficiently restoring an aging mobile device to its original condition.

Various other solutions exist for modeling the behavior of processes or application programs executing on a computing device, and such behavior models may be used to differentiate between malicious and benign process/programs on computing devices. However, these existing modeling solutions are not suitable for use on mobile devices because such solutions generally require the execution of computationally-intensive processes that consume a significant amount of processing, memory, and energy resources, all of which may be scarce on mobile devices. In addition, these solutions are generally limited to evaluating the behavior of individual application programs or processes, and do not provide an accurate or complete model of the performance-degrading mobile device behaviors. For these and other reasons, existing modeling solutions are not adequate for identifying the numerous factors that may contribute to a mobile device's degradation over time, for preventing mobile device degradation, or for efficiently restoring an aging mobile device to its original condition.

There are a variety of factors that may contribute to the degradation in performance and power utilization levels of a mobile device over time, including poorly designed software applications, malware, viruses, fragmented memory, background processes, etc. However, due to the complexity of modern mobile devices, it is increasingly difficult for users, operating systems, and/or application programs (e.g., anti-virus software, etc.) to accurately and efficiently identify the sources of such problems and/or to provide adequate remedies to identified problems. As a result, mobile device users currently have few remedies for preventing the degradation in performance and power utilization levels of a mobile device over time, or for restoring an aging mobile device to its original performance and power utilization levels.

The various aspects provide devices, systems, and methods for efficiently identifying, preventing, and/or correcting the conditions and/or mobile device behaviors that often degrade a mobile device's performance and/or power utilization levels over time.

As mentioned above, mobile devices are resource constrained systems that have relatively limited processing, memory, and energy resources. As also mentioned above, modern mobile devices are complex systems, and there are a large number (i.e., thousands) of factors that may contribute to the mobile device's degradation over time. Due to these constraints, it is often not feasible to monitor/observe all the various processes, behaviors, or factors (or combinations thereof) that may degrade performance and/or power utilization levels of the complex yet resource-constrained systems of modern mobile devices.

To overcome the above mentioned limitations of existing solutions, the various aspects intelligently, dynamically, and/or adaptively determine mobile device behaviors that are to be observed, the number of behaviors that are to be observed, and the level of detail (i.e., granularity) at which the mobile device behaviors are to be observed. The various aspects efficiently identify suspicious or performance-degrading mobile device behaviors without consuming an excessive amount of processing, memory, or energy resources. Various aspects may correct suspicious or performance-degrading mobile device behaviors. Various aspects may prevent the 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.

In an aspect, a mobile device processor may be configured to observe any or all of library application programming interface (API) calls, system call APIs, file-system operations, networking sub-system operations, driver API calls for the numerous sensors, state changes, and other similar events/operations at a high level, and perform real-time behavior analysis operations based on these high level observations to identify programs/processes that may contribute to the mobile device's degradation over time (e.g., programs that are actively malicious, poorly written, etc.). The mobile device processor may be configured to intelligently increase the level of detail (i.e., granularity) at which the mobile device behaviors are to be observed until enough information is available to identify and/or correct the cause of a suspicious or performance-degrading mobile device behavior.

In an aspect, the mobile device processor may be configured to dynamically change the set of observed behaviors (e.g., by selecting new behaviors to observe, observing fewer behaviors, etc.) based on the results of the on-line real-time analysis operations and/or the availability of system resources.

In various aspects, the mobile device processor may be configured to dynamically adjust the observation granularity (i.e., the level of detail at which mobile device behaviors are observed) based on the results of the real-time analysis operations and/or based on the availability of system resources. For example, in various aspects, the mobile device processor may be configured to recursively increase the granularity of one or more observations (i.e., make finer or more detailed observations) until a source of a suspicious or performance-degrading mobile device behavior is identified, until a processing threshold is reached, or until the mobile device processor determines that the source of the suspicious or performance-degrading mobile device behavior cannot be identified from further increases in observation granularity.

In an aspect, the mobile device processor may be configured to dynamically adjust the observation granularity based on the availability of system resources. For example, the mobile device processor may be configured to increase the observation granularity in response to determining that mobile device resources are available or underutilized, or that the mobile is currently connected to a power supply, and/or to reduce the observation granularity in response to determining that the computing device is under heavy load or low battery.

In an aspect, an observer process/daemon/module/sub-system (herein collectively referred to as a “module”) of the mobile device may instrument various application programming interfaces (APIs) at various levels of the mobile device system, collect information from the instrumented APIs, and generate a behavior vector based on the collected information. The observer module may send the generated behavior vector to an analyzer module (e.g., via a memory write operation, etc.) of the mobile device, which may generate spatial and/or temporal correlations based on information included in the behavior vector and/or information collected from various other mobile device sub-systems. The generated spatial and/or temporal correlations may be used by various modules (e.g., by an actuation module, etc.) of the mobile device to identify and/or respond to behaviors that are determined to have a high probably of negatively impacting the mobile device's performance or battery consumption levels.

The various aspects may be implemented on a number of single processor and multiprocessor systems, including a system-on-chip (SOC).FIG. 1is an architectural diagram illustrating an example system-on-chip (SOC)100architecture that may be used in computing devices implementing the various aspects. The SOC100may include a number of heterogeneous processors, such as a digital signal processor (DSP)102, a modem processor104, a graphics processor106, and an application processor108. The SOC100may also include one or more coprocessors110(e.g., vector co-processor) connected to one or more of the heterogeneous processors102,104,106,108. Each processor102,104,106,108,110may include one or more cores, and each processor/core may perform operations independent of the other processors/cores. For example, the SOC100may include a processor that executes a first type of operating system (e.g., FreeBSD, LINIX, OS X, etc.) and a processor that executes a second type of operating system (e.g., Microsoft Windows 8).

The SOC100may also include analog circuitry and custom circuitry114for managing sensor data, analog-to-digital conversions, wireless data transmissions, and for performing other specialized operations, such as processing encoded audio signals for games and movies. The SOC100may further include system components and resources116, such as voltage regulators, oscillators, phase-locked loops, peripheral bridges, data controllers, memory controllers, system controllers, access ports, timers, and other similar components used to support the processors and clients running on a computing device.

The system components116and custom circuitry114may include circuitry to interface with peripheral devices, such as cameras, electronic displays, wireless communication devices, external memory chips, etc. The processors102,104,106,108may be interconnected to one or more memory elements112, system components, and resources116and custom circuitry114via an interconnection/bus module124, which may include an array of reconfigurable logic gates and/or implement a bus architecture (e.g., CoreConnect, AMBA, etc.). Communications may be provided by advanced interconnects, such as high performance networks-on chip (NoCs).

The SOC100may further include an input/output module (not illustrated) for communicating with resources external to the SOC, such as a clock118and a voltage regulator120. Resources external to the SOC (e.g., clock118, voltage regulator120) may be shared by two or more of the internal SOC processors/cores (e.g., DSP102, modem processor104, graphics processor106, applications processor108, etc.).

In addition to the SOC100discussed above, the various aspects may be implemented in a wide variety of computing systems, which may include a single processor, multiple processors, multicore processors, or any combination thereof.

FIG. 2illustrates example logical components and information flows in a computing system200configured to perform dynamic and adaptive observations in accordance with the various aspects. In the example illustrated inFIG. 2, the computing system200includes a coarse observer module202, an analyzer module204, an external context information module206, and an actuation module208. In various aspects, such modules may be implemented in software, hardware, or any combination thereof. In various aspects, the modules may be implemented within parts of the operating system (e.g., within the kernel, in the kernel space, in the user space, etc.), within separate programs or applications, in specialized hardware buffers or processors, or any combination thereof. In an aspect, one or more of the modules may be implemented as software instructions executing on one or more processors of the mobile device.

The observer module202may be configured to instrument/coordinate application programming interfaces (APIs) at various levels/modules of the mobile device, and monitor/observe mobile device operations and events (e.g., system events, state changes, etc.) at the various levels/modules via the instrumented APIs, collect information pertaining to the observed operations/events, intelligently filter the collected information, generate one or more observations based on the filtered information, efficiently store the generated observations in a memory, and send (e.g., via memory writes, function calls, etc.) the generated observations to the analyzer module204.

The analyzer module204may include intelligence for utilizing the limited set of information (i.e., coarse observations) to identify behaviors, processes, or programs that are contributing to (or are likely to contribute to) the device's degradation over time, or which may otherwise cause problems on the device. For example, the analyzer module204may be configured to analyze information (e.g., in the form of observations) collected from various modules (e.g., the observer module202, external context information module206, etc.), learn the normal operational behaviors of the mobile device, generate behavior models of the mobile device's behaviors, and compare the generated models to information/observations received from the observer module202to identify suspicious mobile device behaviors.

As mentioned above, the observer module202may monitor/observe mobile device operations and events. In various aspects, observing mobile device operations and events may include collecting information pertaining to any or all of library API calls in an application framework or run-time libraries, system call APIs, file-system and networking sub-system operations, device (including sensor devices) state changes, and other similar events. In an aspect, the observer module202may monitor file system activity, which may include searching for filenames, categories of file accesses (personal info or normal data files), creating or deleting files (e.g., type exe, zip, etc.), file read/write/seek operations, changing file permissions, etc. In an aspect, the observer module202may monitor data network activity, which may include types of connections, protocols, port numbers, server/client that the device is connected to, the number of connections, volume or frequency of communications, etc. In an aspect, the observer module202may monitor phone network activity, which may include monitoring the type and number of calls or messages (e.g., SMS, etc.) sent out, received, or intercepted (e.g., the number of premium calls placed). In an aspect, the observer module202may monitor the system resources that are used, which may include monitoring the number of forks, memory uses, number of files open, etc. In an aspect, the observer module202may monitor the device state, which may include monitoring various factors, such as whether the display is on or off, whether the device is locked or unlocked, the amount of battery remaining, the state of the camera, etc. In an aspect, the observer module202may also monitor inter-process communications (IPC) by, for example, monitoring intents to crucial services (browser, contracts provider, etc.), the degree of inter-process communications, pop-up windows, etc.

To reduce the number of factors monitored to a manageable level, in operation1illustrated inFIG. 2, the observer module202may perform coarse observations by monitoring/observing a small subset of the factors that could contribute to the mobile device's degradation, and send the coarse observations to the analyzer module204. In an embodiment, the initial set of behaviors and/or subset of the factors may be selected by analysis of benign and problematic applications on mobile devices.

In operation2, the analyzer module204may receive the coarse observations from the observer module202and identify subsystems, processes, and/or applications associated with the received coarse observations that may potentially contribute to the mobile device's degradation. This may be achieved by, for example, the analyzer module204comparing the received information with contextual information received from the external context information module206.

In operation3, the analyzer module204may instruct the observer module202to perform or enable deeper logging/observations or final logging on the identified subsystems, processes or applications. In operation4, the observer module202may perform deeper observations on the identified subsystems, processes or applications. In operation5, the observer module202may send the results of the deeper observations to the analyzer module204for further (and deeper) analysis. Operations1-5may be repeated until the source of a problem is identified or until it is determined that the identified subsystems, processes or applications are not likely to cause problems or degradation. In operation6, the analyzer module204may send the results of the analysis to the actuation module208, which may receive the results and perform operations to heal, cure, isolate, or otherwise fix the identified problem.

In an aspect, the observer module202and the analyzer module204may provide, either individually or collectively, real-time behavior analysis of the computing system's behaviors to identify suspicious behavior from limited and coarse observations, to dynamically determine behaviors to observe in greater detail, and to dynamically determine the level of detail required for the observations. In this manner, the observer module202enables the computing system200to efficiently identify and prevent problems from occurring on mobile devices without requiring a large amount of processor, memory, or battery resources on the device.

In an aspect, the observer module202may store the observations in a space efficient and query-service-time efficient manner to reduce the performance-impact on benign applications. The observer module202may provide the system with various observer modes to enable multi-level logging (e.g., fine grained and coarse-grained logging). The observer module202may provide the ability to automatically and dynamically switch between the different observer modes. The observer module202may monitor and restrict process/application that may exhaust system resources. The observer module202may manage communications (e.g., non-secure to secure world) overhead, such that the overhead is minimal and flow control is maintained/performed efficiently.

In an aspect, the analyzer module204may be configured to receive and analyze information collected by various mobile device sub-systems and/or over various time periods to learn the normal operational behaviors of the mobile device under a variety of contexts and conditions, and generate models of normal mobile device behaviors under the various contexts/conditions. In an aspect, the analyzer module204may be configured to correlate the received observations against the generated behavior models, and perform behavior analysis operations based on the correlations to determine whether the received observations conflict with (or do not match) the learned normal operational behaviors.

FIG. 3illustrates example logical components and information flows in an observer module202of a computing system configured to perform dynamic and adaptive observations in accordance with an aspect. The observer module202may include an adaptive filter module302, a throttle module304, an observer mode module306, a high-level behavior detection module308, a behavior vector generator310, and a secure buffer312. The high-level behavior detection module308may include a spatial correlation module314and a temporal correlation module316.

The observer mode module306may receive control information from various sources, which may include an analyzer unit (e.g., the analyzer module204described above with reference toFIG. 2) and/or an application API. The observer mode module306may send control information pertaining to various observer modes to the adaptive filter module302and the high-level behavior detection module308.

The adaptive filter module302may receive data/information from multiple sources, and intelligently filter the received information to generate a smaller subset of information selected from the received information. This filter may be adapted based on information or control received from the analyzer module, or a higher-level process communicating through an API. The filtered information may be sent to the throttle module304, which may be responsible for controlling the amount of information flowing from the filter to ensure that the high-level behavior detection module308does not become flooded or overloaded with requests or information.

The high-level behavior detection module308may receive data/information from the throttle module304, control information from the observer mode module306, and context information from other components of the mobile device. The high-level behavior detection module308may use the received information to perform spatial and temporal correlations to detect or identify high level behaviors that may cause the device to perform at sub-optimal levels. The results of the spatial and temporal correlations may be sent to the behavior vector generator310, which may receive the correlation information and generate a behavior vector that describes the behaviors of particular process, application, or sub-system. In an aspect, the behavior vector generator310may generate the behavior vector such that each high-level behavior of a particular process, application, or sub-system is an element of the behavior vector. In an aspect, the generated behavior vector may be stored in a secure buffer312. Examples of high-level behavior detection may include detection of the existence of a particular event, the amount or frequency of another event, the relationship between multiple events, the order in which events occur, time differences between the occurrence of certain events, etc.

In the various aspects, the observer module202may perform adaptive observations and control the observation granularity. That is, the observer module202may dynamically identify the relevant behaviors that are to be observed, and dynamically determine the level of detail at which the identified behaviors are to be observed. In this manner, the observer module202enables the system to monitor the behaviors of the mobile device at various levels (e.g., multiple coarse and fine levels). The observer module202may enable the system to adapt to what is being observed. The observer module202may enable the system to dynamically change the factors/behaviors being observed based on a focused subset of information, which may be obtained from a wide verity of sources.

As discussed above, the observer module202may perform adaptive observation techniques and control the observation granularity based on information received from a variety of sources. For example, the high-level behavior detection module308may receive information from the throttle module304, the observer mode module306, and context information received from other components (e.g., sensors) of the mobile device. As an example, a high-level behavior detection module308performing temporal correlations might detect that a camera has been used and that the mobile device is attempting to upload the picture to a server. The high-level behavior detection module308may also perform spatial correlations to determine whether an application on the mobile device took the picture while the device was holstered and attached to the user's belt. The high-level behavior detection module308may determine whether this detected high-level behavior (e.g., usage of the camera while holstered) is a behavior that is acceptable or common, which may be achieved by comparing the current behavior with past behaviors of the mobile device and/or accessing information collected from a plurality of devices (e.g., information received from a crowd-sourcing server). Since taking pictures and uploading them to a server while holstered is an unusual behavior (as may be determined from observed normal behaviors in the context of being holstered), in this situation the high-level behavior detection module308may recognize this as a potentially threatening behavior and initiate an appropriate response (e.g., shutting off the camera, sounding an alarm, etc.).

In an aspect, the observer module202may be implemented in multiple parts.

FIG. 4illustrates logical components and information flows in an example computing system400implementing an observer module in accordance with an aspect. The illustrated computing system400includes an application framework402, a run time library404, a user log API406, and a logger library408in the user space. The computing system400may include a kernel core410, kernel drivers412, a kernel log API414, an observer logger424, a filter rules module416, a throttling rules module418, a ring buffer422, and an observer daemon420in the kernel space. In an aspect, the ring buffer422may be a fixed-sized and/or circular buffer. In an aspect, the combination of the user log API406and the kernel log API414may constitute the observer logger424. In an aspect, the combination of the observer daemon420and the observer logger424may constitute the observer module202.

The application framework402and the run time library404may be preexisting software code/components of the mobile device, each of which may be instrumented with logic to monitor activities and send information to the user log API406in the user space. The user log API406may provide an API that enables the user space applications to communicate with the kernel via the kernel log API414.

In an aspect, the observer logger424may be automatically invoked whenever a particular event, action, or API (e.g., an API identified in a list of APIs as being of particular importance) is invoked, and the corresponding information may be stored in the ring buffer422. The information stored in the ring buffer422may include, for example, information for identifying the caller, information for identifying the exact function being called, the parameters that have been passed to the function call, and other similar information. In an aspect, this information may be stored in the ring buffer422in a raw format. Alternatively, the ring buffer422may be used to store information after the log has been processed.

The observer logger424may be controlled by a set of filter and throttling rules416,418. The filter rules416may specify whether a particular API is to be logged or not. The throttling rules418may specify conditions under which the system is to termination the logging/monitoring of a specific API to prevent overloads.

The filter and throttling rules416,418may be created, updated, and/or maintained by the observer daemon420. For example, if after observing the mobile device for ten minutes, the observer daemon428decides that a particular API is no longer of interest (e.g., it is not providing the system with useful information), the observer daemon420may update the filter rules416such that events relating to that particular API are no longer monitored/logged.

FIG. 5Aillustrates logical components and information flows in a computing system500implementing an observer module202in accordance with another aspect. The computing system500illustrated inFIG. 5Aincludes all the components described above with reference toFIG. 4, except that the filter rules416are enforced on the user log API406in the user space and/or kernel space on the device. Thus, instead of each call coming to the observer logger424and the observer logger424deciding whether the call should be logged or not (as described with reference toFIG. 4), the filter rules416may be implemented within the instrumentations (e.g., user log API, etc.) such that the call itself will not reach the logger based on the filter rules416. Implementing the configuration illustrated inFIG. 5Amay further improve the mobile device efficiency because function calls do not need to be made to a logger inside the kernel.

FIG. 5Billustrates logical components and information flows in a computing system550implementing an observer module in accordance with yet another aspect. The computing system550illustrated inFIG. 5Bincludes all the components described above with reference toFIG. 5A, except that the observer daemon420is in the user space. In an aspect, the observer daemon420, filter rules416, throttling rules418, and observer logger424may be part of the same component Implementing the configuration illustrated inFIG. 5Bmay further improve the mobile device efficiency because the observer daemon420may update the filter rules without functions calls into the kernel space.

At any given time, several applications and several kernel threads may be attempting to store/write information in the ring buffer, which may cause contention issues that hinder scalability. In an aspect, the system's scalability may be improved via the inclusion of multiple ring buffers, as illustrated inFIGS. 6A-B. The computing system600illustrated inFIG. 6Aincludes all the components described above with reference toFIG. 5A, but includes multiple ring buffers430. The computing system600may include a ring buffer for each application, throttle, and kernel thread being monitored by the system. For example, the computing system600may include a ring buffer for a kernel thread being monitored by the system, and one or more ring buffers for each application and/or throttle being monitored by the system. Alternatively, the computing system600may include a ring buffer for groups of applications, groups of throttles, and/or groups of kernel threads being monitored by the system. The inclusion of multiple ring buffers enables the computing system600to avoid contention issues from arising and reduces bottle necks.

The computing system650illustrated inFIG. 6Bincludes all the components described above with reference toFIG. 6A, except that the observer daemon420is in the user space. Implementing the configuration illustrated inFIG. 6Bmay further improve the mobile device efficiency because the observer daemon420may update the filter rules without functions calls into the kernel space.

FIG. 7Aillustrates logical components and information flows in a computing system700implementing an aspect observer daemon420. The computing system700may include an analyzer component (e.g., the analyzer module204illustrated inFIG. 2), a filter rules416component, a throttling rules418component, multiple ring buffers430, a database702, a secure buffer704, and an observer daemon420. The observer daemon420may include a ring buffer API706, system health monitor708, a behavior detector712, a database engine714, a rules manager710, a secure buffer manager716, a query processor720, a query API718, a database API722. A logger (not illustrated) may store information in the ring buffers430. The observer daemon420may extract the information from the ring buffers430via the ring buffer API706. The behavior detector712may receive information from the ring buffer API706, and perform correlation and formatting operations on the received data to generate a behavior vector.

The generated behavior vector may be sent to the database engine714for storing in the database702. The database engine714may manage all of the specificities of the database implementation (e.g., kind of data structure that is implemented, types of information included in the data structure, etc.).

The rules manager710may be configured to receive inputs from different components (e.g., system health monitor, behavior detection unit, analyzer, etc.), and update the filter and throttle rules416,418based on the received inputs. For example, the rules manager710may receive log statistics from the behavior detector712and update the filter and throttle rules416,418based on the log statistics.

The system health monitor708may be configured to monitor system resources, and inform the rules manager710of the system health. For example, the system health monitor708may inform the rules manager710about the amount of energy that remains stored in the battery, how much memory is available, whether there are enough resources to perform a detailed observation, etc. The rules manager710may use the information received from the system health monitor708to update the rules. For example, if the system health monitor708indicates that the device battery state is below a certain threshold, the rules manager710may update the filter rules416such that the system performs more coarse observations in order to reduce power consumption.

The query processor720may be configured to perform conversions between various API's, such as from a query API718to a database-specific API722.

The secure buffer704may enable kernel space components (e.g., in the un-trusted region) to communicate with the user space components (e.g., in the trusted region).

The secure buffer manager716may be configured to control the communications that occur via the secure buffer704.

The database engine714may be configured to store the database response to the secure buffer manager716, which may perform flow control operations and store the information in the secure buffer704.

The information generated by the observer daemon420may be utilized by an analyzer204, which may be implemented in the kernel space, user space, or in a trusted computing base of a system-on-chip (SOC).

FIG. 7Billustrates logical components and information flows in a computing system750implementing another aspect observer daemon420. The computing system750may include an analyzer204component, a filter rules416component, a throttling rules418component, multiple ring buffers430, a secure buffer704, a secure buffer manager716, and an observer daemon420. The observer daemon420may include a ring buffer API706, system health monitor708, a behavior detector712, a database engine714, and a rules manager710. A logger (not illustrated) may store information in the ring buffers430. The computing system750may perform the same operations as the computing system700illustrated inFIG. 7A, except that the secure buffer manager716is in the kernel space and may control the data that is sent to an analyzer204in the user space.

FIG. 8Aillustrates logical components and information flows in a computing system800implementing another aspect observer daemon. The computing system800illustrated inFIG. 8Aincludes all of the components described above with reference toFIG. 7A, except for a query processor because the database in this aspect is included as part of the secure buffer. In this configuration, whenever the analyzer issues a query, the query may come directly from the database engine. Similarly, responses to the query may be sent directly from the secure buffer to the analyzer.

FIG. 8Billustrates logical components and information flows in a computing system800implementing yet another aspect observer daemon. In the example illustrated inFIG. 8B, the observer daemon includes a behavior detector712and a database engine714in the user space, and a secure buffer manager716, a rules manager710, and a system health monitor708in the kernel space.

The various aspects provide cross-layer observations on mobile devices encompassing webkit, SDK, NDK, kernel, drivers, and hardware in order to characterize system behavior. The behavior observations may be made in real time.

An important feature of the various aspects is that the observer module may perform adaptive observation techniques and control the observation granularity. As discussed above, there are a large number (i.e., thousands) of factors that could contribute to the mobile device's degradation, and it may not be feasible to monitor/observe all of the different factors that may contribute to the degradation of the device's performance. To overcome this, the various aspects dynamically identify the relevant behaviors that are to be observed, and dynamically determine the level of detail at which the identified behaviors are to be observed.

FIG. 9illustrates an example method900for performing dynamic and adaptive observations in accordance with an aspect. In block902, the mobile device processor may perform coarse observations by monitoring/observing a subset of large number factors/behaviors that could contribute to the mobile device's degradation. In block903, the mobile device processor may generate a behavior vector characterizing the coarse observations and/or the mobile device behavior based on the coarse observations. In block904, the mobile device processor may identify subsystems, processes, and/or applications associated with the coarse observations that may potentially contribute to the mobile device's degradation. This may be achieved, for example, by comparing information received from multiple sources with contextual information received from sensors of the mobile device. In block906, the mobile device processor may perform behavioral analysis operations based on the coarse observations. In determination block908, the mobile device processor may determine whether suspicious behaviors or potential problems can be identified and corrected based on the results of the behavioral analysis. When the mobile device processor determines that the suspicious behaviors or potential problems can be identified and corrected based on the results of the behavioral analysis (i.e., determination block908=“Yes”), in block918, the processor may initiate a process to correct the behavior and return to block902to perform additional coarse observations.

When the mobile device processor determines that the suspicious behaviors or potential problems can not be identified and/or corrected based on the results of the behavioral analysis (i.e., determination block908=“No”), in determination block909the mobile device processor may determine whether there is a likelihood of a problem. In an embodiment, the mobile device processor may determine that there is a likelihood of a problem by computing a probability of the mobile device encountering potential problems and/or engaging in suspicious behaviors, and determining whether the computed probability is greater than a predetermined threshold. When the mobile device processor determines that the computed probability is not greater than the predetermined threshold and/or there is not a likelihood that suspicious behaviors or potential problems exist and/or are detectable (i.e., determination block909=“No”), the processor may return to block902to perform additional coarse observations.

When the mobile device processor determines that there is a likelihood that suspicious behaviors or potential problems exist and/or are detectable (i.e., determination block909=“Yes”), in block910, the mobile device processor may perform deeper logging/observations or final logging on the identified subsystems, processes or applications. In block912, the mobile device processor may perform deeper and more detailed observations on the identified subsystems, processes or applications. In block914, the mobile device processor may perform further and/or deeper behavioral analysis based on the deeper and more detailed observations. In determination block908, the mobile device processor may again determine whether the suspicious behaviors or potential problems can be identified and corrected based on the results of the deeper behavioral analysis. When the mobile device processor determines that the suspicious behaviors or potential problems can not be identified and corrected based on the results of the deeper behavioral analysis (i.e., determination block908=“No”), the processor may repeat the operations in blocks910-914until the level of detail is fine enough to identify the problem or until it is determined that the problem cannot be identified with additional detail or that no problem exists.

When the mobile device processor determines that the suspicious behaviors or potential problems can be identified and corrected based on the results of the deeper behavioral analysis (i.e., determination block908=“Yes”), in block918, the mobile device processor may perform operations to correct the problem/behavior, and the processor may return to block902to perform additional operations.

In an aspect, as part of blocks902-918of method900, the mobile device processor may perform real-time behavior analysis of the system's behaviors to identify suspicious behavior from limited and coarse observations, to dynamically determine the behaviors to observe in greater detail, and to dynamically determine the precise level of detail required for the observations. This enables the mobile device processor to efficiently identify and prevent problems from occurring, without requiring the use of a large amount of processor, memory, or battery resources on the device.

FIG. 10illustrates an example observer method1000for performing dynamic and adaptive observations on a mobile device processor in accordance with an aspect. The observer method1000may be implemented as part of an observer module in the mobile device's kernel space, user space, or a combination thereof. In block1002, the observer module operating on the processor may receive data, control, and/or context information from various sources, which may include an analyzer unit (e.g., analyzer module204described inFIG. 2), application APIs, Driver APIs, kernel threads, user threads, processes, programs, mobile device sensors, etc. In block1004, the observer module operating on the processor may adaptively and intelligently filter the received information to generate a smaller subset of the received information. In block1006, the observer module operating on the processor may throttle control the filtered information to control/prevent flooding or overloading. In block1008, the observer module operating on the processor may perform spatial and temporal correlations to detect/identify high level behaviors that may cause the device to perform at sub-optimal levels. In block1010, the observer module operating on the processor may generate a behavior vector that describes the behaviors of particular process, application, or sub-system. In block1012, the observer module operating on the processor may store the generated behavior vector in a secure buffer.

FIG. 11illustrates another example method1100for perform dynamic and adaptive observations by a mobile device processor in accordance with another aspect. In block1102, the mobile device processor may dynamically identify the relevant behaviors that are to be observed on the mobile device. In block1104, the mobile device processor may dynamically determine the level of detail at which the identified behaviors are to be observed. In optional block1106, the mobile device processor may dynamically adapt to what is being observed. In optional block1108, the mobile device processor may dynamically change or update the parameters, factors, behaviors, processes, applications, and/or subsystems that are to be observed. The operations of blocks1102-1108may be repeated continuously or as is necessary to improve the mobile device performance (e.g., battery power consumption, processing speed, network communication speeds, etc.).

The various aspects may be implemented on a variety of mobile computing devices, an example of which is illustrated inFIG. 12in the form of a smartphone. A smartphone1200may include a processor1201coupled to internal memory1202, a display1203, and to a speaker. Additionally, the smartphone1200may include an antenna1204for sending and receiving electromagnetic radiation that may be connected to a wireless data link and/or cellular telephone transceiver1205coupled to the processor1201. Smartphone1200typically also include menu selection buttons or rocker switches1206for receiving user inputs.

A typical smartphone1200also includes a sound encoding/decoding (CODEC) circuit1212, which digitizes sound received from a microphone into data packets suitable for wireless transmission and decodes received sound data packets to generate analog signals that are provided to the speaker to generate sound. Also, one or more of the processor1201, wireless transceiver1205and CODEC1212may include a digital signal processor (DSP) circuit (not shown separately).

Portions of the aspect methods may be accomplished in a client-server architecture with some of the processing occurring in a server, such as maintaining databases of normal operational behaviors, which may be accessed by a mobile device processor while executing the aspect methods. Such aspects may be implemented on any of a variety of commercially available server devices, such as the server1300illustrated inFIG. 13. Such a server1300typically includes a processor1301coupled to volatile memory1302and a large capacity nonvolatile memory, such as a disk drive1303. The server1300may also include a floppy disc drive, compact disc (CD) or DVD disc drive13011coupled to the processor1301. The server1300may also include network access ports1304coupled to the processor1301for establishing data connections with a network1305, such as a local area network coupled to other broadcast system computers and servers.

The processors1201,1301may be any programmable microprocessor, microcomputer or multiple processor chip or chips that can be configured by software instructions (applications) to perform a variety of functions, including the functions of the various aspects described below. In some mobile devices, multiple processors1201may be provided, such as one processor dedicated to wireless communication functions and one processor dedicated to running other applications. Typically, software applications may be stored in the internal memory1202,1302,1303before they are accessed and loaded into the processor1201,1301. The processor1201,1301may include internal memory sufficient to store the application software instructions.