Patent Publication Number: US-9906542-B2

Title: Testing frequency control using a volatility score

Description:
BACKGROUND 
     Organizations with a large number of computers that run several different services typically monitor both hardware and software events for anomalies that can indicate security threats. To date, operational security assurance procedures are typically based on rules that analyze events for pre-defined patterns. For example, the rules may be run against logs of each computer. The pre-defined patterns can indicate a potential security threat which, once identified, can be addressed. This rule-based approach can fail to scale in at least two dimensions, thus rendering the approach difficult to flexibly implement. First, regarding rule-based implementations, such requires coding of the rules ahead of time, based on expert knowledge. This means that rule developers have to anticipate what is sought as a vulnerability. Rule developers may not, however, be knowledgeable of all potential vulnerabilities, thus leaving gaps in the approach. Second, during operation, the rule-based approach demands full scanning of all events, seeking for patterns in data or information that may have incomplete or incorrect data. 
     Additionally, conventional testing techniques for these services rely on scheduled testing or event-based testing. In scheduled testing, tests may become repetitive and result in significant over testing due to repetition of tests that pass cleanly regardless of whether or not there is a reason to suspect a different outcome, thus resulting in inefficient use of resources. In cases of conventional one-off event-triggered testing, code check-in and deployment may occur without each development team being aware in a complex or distributed service with many development teams working in parallel. Additionally, dependencies can then change underneath portions of the service, which may introduce failure states without the developers realizing anything has changed. 
     SUMMARY 
     Various implementations provide an approach to control of testing frequency based on the concept of behavior change detection or “volatility.” Behavior change detection is utilized, in place of a pre-defined patterns approach, to look at a system&#39;s behavior and detect any variances from what would otherwise be normal operating behavior. In operation, machine learning techniques are utilized as an event classification mechanism which facilitates implementation scalability. The machine learning techniques are iterative and continue to learn over time. Operational scalability issues are addressed by using the computed volatility of the events in a time series as input for a classifier. During a learning process (i.e., the machine learning process), the system identifies relevant features that are affected by security incidents. When in operation, the system evaluates those features in real-time and provides a probability that an incident is about to occur. 
     In at least some implementations, a behavior change detection system collects behavior from a service, such as an online service, and detects behavior changes, either permanent or transient, in the service. In this way, the changes may be used to compute a volatility score that describes an amount of change in the behaviors. Machine learning hierarchical (agglomerative) clustering techniques are utilized to compute deviations between clustered data sets representing an “answer” that the service presents to a series of requests. 
     The changes in behavior as reflected by the volatility scores are then usable to control a testing frequency of the services, such as URLs that are part of the service. This may be performed dynamically to reflect ongoing changes in volatility. In this way, testing frequency may be algorithmically set based on measured changes in a behavior of a service, independent of changes to content served by a service. 
     This Summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key features or essential features of the claimed subject matter. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The detailed description references the accompanying figures. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. The use of the same reference numbers in different instances in the description and the figures may indicate similar or identical items. 
         FIG. 1  illustrates an example operating environment in accordance with one or more implementations. 
         FIG. 2  illustrates an example system in accordance with one or more implementations. 
         FIG. 2 a    illustrates modeling a meta-domain descriptor as xml data in accordance with one implementation. 
         FIG. 2 b    illustrates modeling a matrix execution as xml data in accordance with one implementation. 
         FIG. 3  illustrates an example recognizer in accordance with one or more implementations. 
         FIG. 3 a    illustrates aspects of how an object feature can be collected in a specific context over time. 
         FIG. 3 b    illustrates an example of clustering the set values from  FIG. 3   b.    
         FIG. 3 c    illustrates aspects of how an object feature can be collected in a specific context over a time period. 
         FIG. 3 d    illustrates an example of clustering the set values from  FIG. 3   c.    
         FIG. 3 e    illustrates a computer deviation in accordance with one or more implementations. 
         FIG. 3 f    is a flow diagram that describes steps in a method in accordance with one or more implementations. 
         FIG. 3 g    is a flow diagram that describes steps in a method in accordance with one or more implementations. 
         FIG. 4  illustrates the example system undergoing an iterative, machine learning process. 
         FIG. 5  illustrates the example system undergoing an iterative, machine learning process. 
         FIG. 6  illustrates aspects of how an example schema is mapped to an example system in accordance with one or more implementations. 
         FIG. 7  illustrates aspects of how the example schema is mapped to the example system in accordance with one or more implementations. 
         FIG. 8  depicts an example system showing in general how synthetic attack generators of  FIG. 2  of the service testing system of  FIG. 1  are fed to perform testing. 
         FIG. 9  depicts an example system showing creation of a volatility maps having volatility scores usable to create an attack surface for control testing of one or more services. 
         FIG. 10  depicts an example system showing dynamic generation of testing frequencies using the volatility map generated in  FIG. 9 . 
         FIG. 11  is a flow diagram that describes steps in a method in accordance with one or more implementations. 
         FIG. 12  is a flow diagram that describes steps in a method in accordance with one or more implementations. 
         FIG. 13  is a flow diagram that describes steps in a method in accordance with one or more implementations. 
         FIG. 14  is an example device in accordance with one or more implementations. 
     
    
    
     DETAILED DESCRIPTION 
     Overview 
     Automated testing/scanning of service is generally applied in one of two ways, one-off event-triggered testing or scheduled, ongoing testing. Event-triggered testing is commonly tied to code check-ins or deployments. Scheduled testing is run based on time, whether statically or dynamically configured. As described above, these techniques may result in inefficient use of resources, introduction of failure states without developers realizing anything has changed due to introduction of dependencies underneath portions of the service, and so forth. 
     Techniques are described for testing and scanning that are based on changes measured in a behavior of a service, e.g., web applications and other services that are executed “in the cloud.” For example, a volatility map is created that is used to determine scanning frequencies for a target (e.g., the service) being tested. As the map changes the frequencies of the testing also changes, thereby supporting dynamic testing and efficient use of resources. In order to create the volatility map, a representative list of URLs for the target is chosen that represent unique paths and parameters of the service. 
     This list is fed to matrix execution to generate a meta-domain descriptor that represents the target. Matrix execution describes an object context generator and can be thought of as describing the “question” that is to be asked. A simple example using algorithmic results would be to render the results for specific queries, markets, languages, and the like. A meta-domain descriptor is utilized to describe what part of the domain behavior is being examined and analyzed for deviations. For example, a meta-domain descriptor (MDD) may be formed as a collection of objects described in an independent context. Consider, for example, applying this concept to “http://www.bing.com,” which is an HTML end point. The meta-domain descriptor in this examples appears as blue links as part of a search result page. 
     A volatility score for each URL of the matrix execution is computed using the meta-domain descriptor that represents the target that describes an amount of changes in the behaviors of a respective URL. The volatility map is then formed as a list indicating pairs having the URLs for the service and a corresponding volatility score. Using the volatility map, a frequency attack table is formed that describes an attack frequency for each URL that is to be provided to a synthetic attack generator for testing. The frequency attack table may also be dynamically adjusted based on changes to the volatility map, and thus may efficiently reflect new behavior changes of the service. 
     The frequency attack table can be utilized in a variety of ways for testing. For example, testing (e.g., attacks) can be performed in a manner that establishes frequency of testing as a function of the volatility score, e.g., directly proportion to the list of URLs and volatility score pairs such that as volatility increases so to does the frequency of testing. In this way, testing frequency may be algorithmically set based on measure changes in the behavior of a service, independent of changes to the content served by the service. 
     In the discussion that follows, a section entitled “Example Environment” describes an example environment in which the various embodiments can be utilized. Next, a section entitled “Example Schemas” describes an example schema that can be utilized in connection with various embodiments. Following this, a section entitled “Training a Recognizer” describes embodiments in which a recognizer can be initially trained for deployment in accordance with one or more embodiments. Next, a section entitled “Behavior Change Analysis for Services—Example” described but one approach that can be used to conduct behavior change analysis in accordance with one embodiment. Next, a section entitled “In Operation” describes operational aspects of a deployed recognizer and an iterative machine learning process in accordance with one or more embodiments. Following this, a section entitled “Relating the System to the Schema” provides a diagrammatic representation of how an example schema, described below, relates to the described system. A section titled “Testing Frequency Control using a Target Volatility Score” follows. Next, a section entitled “Example Methods” describes example methods in accordance with one or more embodiments. Last, a section entitled “Example Device” describes an example device in accordance with one or more embodiments. 
     Example Environment 
       FIG. 1  is an illustration of an environment  100  in an example implementation that is operable to employ the techniques described herein. The illustrated environment  100  includes a service provider  102  implemented using a plurality of servers, one or more client devices  104 , and a network  106  communicatively coupling the servers and client devices. 
     Although the client device  104  is illustrated as being implemented by a traditional desktop computer, the client device  104  may be implemented by a variety of different devices. For example, the client device  104  may be configured as a computer that is capable of communicating over the network  106 , such as a desktop computer, a mobile station, an entertainment appliance, a set-top box communicatively coupled to a display device, a wireless phone, a game console, a tablet computer, a netbook, and so forth. Thus, the client device  104  may range from a full resource device with substantial memory and processor resources (e.g., personal computers, game consoles) to a low-resource device with limited memory and/or processing resources (e.g., traditional set-top boxes, hand-held game consoles). Additionally, the devices may be representative of one or more devices, e.g., the functionality provided by the service provider may be implemented by a plurality of servers in a server farm as illustrated. 
     Although the network  106  is illustrated as the Internet, the network may assume a wide variety of configurations. For example, the network  106  may include a wide area network (WAN), a local area network (LAN), a wireless network, a public telephone network, an intranet, and so on. Further, although a single network  106  is shown, the network  106  may be configured to include multiple networks. 
     The service provider  102  may be implemented using any suitable type of server that can provide any suitable type of service  108  that can be consumed online. In at least some embodiments, the server or servers can be configured to operate in a so-called “cloud computing” environment. Cloud computing refers to a computing model that enables ubiquitous network access to a shared and virtualized pool of computing capabilities. Such computing capabilities can include, by way of example and not limitation, network, storage, processing, and memory capabilities that can be rapidly provisioned. Cloud computing can encompass a variety of cloud providers, as well as several infrastructure-as-a-service (Iaas) and platform-as-a-service (Paas) solutions. 
     Specifically, the service provider  102  may be implemented as, by way of example and not limitation, an application server that is dedicated to running certain software applications (e.g., business-related applications), a catalog server that can provide a central search point for information across a distributed network, a communications server that provides a computing platform for communication networks, a computation server intended for intensive computations (e.g., scientific calculations), a database server that provides database services to other computer programs or computers, a fax server that provides fax services for client devices, a file server that provides remote access to files, a game server that enables video game clients to connect in order to play online games, a mail server that handles transport of and access to e-mail and other communication services, a name server that provides name resolution, a print server that provides print services, a proxy server that acts as an intermediary for requests from clients seeking resources from other servers, a sound server that provides multimedia broadcasting or streaming, a Web server that allows a HTTP clients to connect in order to send commands and receive responses along with data contents, and the like. As such, the number and variety of services offered by the service provider  102  can vary greatly. 
     The individual servers can include a service testing system  110 , which is designed to utilize machine learning techniques to classify security events based on the concept of behavior change detection or “volatility.” Behavior change detection is utilized, in place of a pre-defined patterns approach, to look at a system&#39;s behavior and detect any variances from what would otherwise be normal operating behavior. In operation, the machine learning techniques are utilized as an event classification mechanism. The machine learning techniques are iterative and continue to learn over time. This constitutes an improvement over rule-based systems that require new rules to be authored whenever the system changes. Through machine learning techniques, the manual process typically associated with rule-based systems is eliminated in favor of an automatically-updatable and continuously-learning solution. 
     A user of the client device  104  may interact with a communication module  112 , which is representative of functionality of the client device  104  to interact with the network  106 , and hence interact with various online services provided by the service provider  102 . 
     Various embodiments described above and below can be implemented utilizing a computer-readable storage medium that includes instructions that enable a processing unit to implement one or more aspects of the disclosed methods as well as a system configured to implement one or more aspects of the disclosed methods. By “computer-readable storage medium” is meant all statutory forms of media. Accordingly, non-statutory forms of media such as carrier waves and signals per se are not intended to be covered by the term “computer-readable storage medium”. 
     Generally, any of the functions described herein can be implemented using software, firmware, hardware (e.g., fixed logic circuitry), manual processing, or a combination of these implementations. The terms “module,” “functionality,” and “logic” as used herein generally represent software, firmware, hardware, or a combination thereof. In the case of a software implementation, the module, functionality, or logic represents program code that performs specified tasks when executed on a processor (e.g., CPU or CPUs). The program code can be stored in one or more computer readable storage media. The features of the volatility-based classifier are platform-independent, meaning that the techniques may be implemented on a variety of commercial computing platforms having a variety of processors. 
     Having considered an example environment in which various embodiments of the service testing system  110  can be employed, consider now preliminarily, an example schema that defines data flow between modules that are described below. It is to be appreciated and understood, however, that the schema about to be described constitutes but one example of a schema and is not to be used to limit application of the claimed subject matter. Near the end of this document,  FIGS. 6 and 7  diagrammatically relate this schema to the system that is described just below. 
     Example Schemas 
     The schemas about to be discussed describe the data flows through a pipeline of processing modules for the volatility-based classifier. The schemas are first described, followed by a discussion of the various processing modules and how data described by the schemas flows through the processing modules. 
     “UsageData” is an open schema for usage data that describes how users interact with a particular service, e.g., online service requests to a particular web site, such as search queries in Bing.com. 
     “UsageDataDescription” is a schema for metadata about the UsageData. The metadata describes the UsageData in some particular way. 
     “OperationalData” is an open schema for the operational data for the datacenter hosting the services. OperationalData can include, by way of example and not limitation, aggregated CPU utilization, network traffic, memory usage, and the like. 
     “OperationalDataDescription” is a schema for metadata about the operation data. This metadata describes the OperationalData in some particular way. 
     “UsageVolatility” is an open schema for a time series with an indicated volatility of usage data. This can, for example, be a derivative for numerical features (like number of requests) or an open string capturing classes of changes, e.g., a string to capture that there are rendering differences in a web page within the last time period. 
     “OperationalVolatility” is an open schema for a time series with indicated volatility of operational data. As with the usage volatility, this can, for example, be a derivative for numerical features (like CPU utilization), or any other type of data that can summarize volatility within the target interval, e.g., categorical data, binary data or even null, in case the data was not captured. 
     “FeatureStream” is an open schema that captures the unified and correlated aggregation of both usage volatility and operational volatility in a time series bounded to pre-defined intervals (days, hours, minutes, seconds, milliseconds, etc.). 
     “RecognizedEvents” is an open schema that is used for the generated recognized events. Those are the points in time when usage and operational volatility deviate from historical data, based on annotated training data. 
     “RecognitionScoring” is an open schema that is used for the output of the scoring process of the recognized events against a so-called labeled “ground truth”. 
     Having considered example schemas in accordance with one or more embodiments, consider now a discussion of the tools and processes for training a so-called “recognizer” for various events. 
     Training a Recognizer 
     This section describes how a “recognizer” of the service testing system  110  of  FIG. 1  can be initially trained through machine learning techniques. 
     In the illustrated and described embodiments, the tools and processes that are utilized for training a recognizer include, by way of example and not limitation:
         (1) a service that generates synthetic attacks;   (2) data streams that capture both usage and operational data;   (3) processor modules for computing usage data volatility and operational data volatility;   (4) a correlation module for computing correlation of time series streams from usage volatility and operational volatility;   (5) a machine learning training framework for data clustering, classification, and regression; and   (6) a machine learning runtime environment.       

     As an example, consider  FIG. 2  which illustrates an example system in accordance with one or more embodiments of the service testing system  110  of  FIG. 1  generally at  200 . The system  200  includes one or more online services  202 , examples of which are provided above. In addition, system  200  includes a user traffic module  204 , a synthetic attack generator  206 , usage data  208 , operational data  210 , a recognizer  212  and recognized bad traffic  214 . The recognized bad traffic  214  constitutes recognized events at points in time where usage and operational volatility deviate from historical data, based on training data described below. 
     The user traffic module  204  is representative of functionality associated with the user traffic with respect to a particular online service  202 . Specifically, user traffic can constitute “good” traffic which typifies normal user interactions. These normal user actions would be those typically associated with how a user normally consumes or otherwise interacts with an online service. The user traffic can also constitute “bad” user traffic. Bad user traffic constitutes traffic that is otherwise not normal, including attack-type traffic, malicious traffic, and other suspicious interactions. 
     The synthetic attack generator  206  of the service testing system  110  is used to generate synthetic attacks on the online service  202 . The synthetic attacks can take any suitable form and, in at least some implementations, can be a function of the type of online service that is provided. The synthetic attack generator  206  can be used, for example, to generate special attack strings, inject attack code in various URLs and parameters discovered from various logs associated with online service. Many times, these known attacks can generate several million requests per day on an online service  202 . 
     Responsive to the input received from the user traffic module  204  and the synthetic attack generator  206 , the online service  202  produces two kinds of data—usage data  208  and operational data  210 . The usage data  208  can include any suitable type of data associated with a user&#39;s interaction with an online service. So, for example, a log can describe that a user navigated to a webpage at a certain point in time, made a query for a particular kind of cell phone, received results for the query, and then navigated to another webpage. 
     The operational data  210  describes aspects of the system&#39;s hardware and software operations. This can include, by way of example and not limitation, aggregated CPU utilization, network traffic, memory usage, and the like. So, for example, the operational data may describe that a particular machine, over a period of time, went from having a first percentage of its memory free to a second percentage of its memory free. 
     These two pieces of data—the usage data and the operational data—are correlated and sequenced in a manner described in more detail below. Specifically, the usage data and the operational data are input to and processed by recognizer  212 . The recognizer  212  utilizes machine learning techniques, as described in more detail below, to produce the recognized bad traffic  214 , i.e., recognized events that constitute departures from historical behavior. 
       FIG. 3  illustrates an example recognizer  212  in accordance with one or more implementations. In this example, the recognizer  212  is described as being deployed or operational and as illustrated includes a usage data volatility processor  300 , an operational data volatility processor  302 , a time series streams correlator  304 , and a trained model event recognizer  306 . 
     The usage data mentioned above is received and processed by the usage data volatility processor  300 . Likewise, the operational data is received and processed by the operational data volatility processor  302 . Each of the processors  300 ,  302  process their respective data to analyze behaviors associated with the data. That is, each of these processors performs behavior change analysis on the data that it receives. Any suitable type of behavior change analysis can be conducted. So, for example, the usage data volatility processor may note that yesterday, a user utilized the online service to conduct a search for tickets to travel to Montreal. The search produced a webpage that was further consumed by the user. Today, however, when a similar search was conducted by a different user, the system behaved quite differently by perhaps producing different search results. Likewise, during this time, the operational data volatility processor  302  may note that yesterday&#39;s operational data during this search varied meaningfully in its memory usage as compared to today&#39;s similar search. 
     Based on the processing conducted by processors  300 ,  302 , the observed behavioral change is quantified using a number or value which is correlated to the deviation of the behavioral change that is observed. Any suitable quantification scheme can be used in accordance with one or more embodiments. In at least some embodiments, the behavioral change is quantified using a value between 0 and 1. Quantification of the behavioral change in this manner enables classification of behavioral changes over time. This process produces a time series with user volatility and operational volatility data. But one example of how this can be done is provided below in a section entitled “Behavior Change Analysis for Online Services—Example”. 
     The quantified behavioral change data is provided to the time series streams correlator  304  for processing. The correlator analyzes the data that it receives from the usage data volatility processor  300  and looks to see if any data received from the operational data volatility processor  302  indicates any behavioral change at a corresponding time. So, for example, if a usage behavioral deviation occurred at a particular time, the time series streams correlator  304  examines data from that time received from the operational data volatility processor  302  to ascertain whether operations were normal or varied in some meaningful degree, e.g., did CPU usage jump from 50% to 100%? Thus, correlation of the volatility of usage data and operational data can help to identify whether a particular behavioral deviation was even more out of the ordinary. This process produces a feature stream that captures the unified and correlated aggregation of both usage volatility and operational volatility in a time series bounded to a pre-defined interval. 
     The feature stream is provided to the trained model event recognizer  306 . The recognizer  306  is configured to recognize, from the feature stream that it receives, normal versus abnormal behavior. So, for example, at a given time the operational data may indicate a behavioral change associated with a machine being turned off. When the correlated time series stream is examined, the trained model event recognizer  306  may note that the usage data associated with that time is normal. Accordingly, as of this time, the system was operating normally. However, the time series stream may indicate an operational data variance along with usage data that is out of the normal operating range. In this instance, this is recognized as bad traffic, i.e. a recognized event in which usage and operational volatility deviate from historical data, and a notification can be generated by the system to cause further investigation to be performed. 
     Behavior Change Analysis for Services—Example 
     Any issue in any service (e.g., security, privacy, regression, and the like) over time can be considered as a change in behavior. One example of a service is an online or Web-based service. In the approach described below, an initial behavior phase is utilized to collect an initial behavior for a service over a period of time. An exercise behavior phase is then utilized to collect an exercised behavior over a different period of time. The initial behavior phase and exercise behavior phase produce two data sets for which a deviation is computed. The deviation can indicate behavior changes and find issues or legitimate mutations. 
     In one or more implementations, to compute deviations in behavior for services, such as online services, two concepts are utilized—the meta-domain descriptor and matrix execution, each of which is discussed below. These concepts enable a behavior to be “built.” Other approaches can also be used without departing from the spirit and scope of the claimed subject matter. 
     Meta-Domain Descriptor 
     A meta-domain descriptor describes elements out of context. Consider, for example, applying this concept to “http://www.bing.com,” which is an HTML end point. One can see an example of this when extracting out algorithmic search results (e.g., blue links that appear in a search result page). In this instance, account environment factors are not taken into account such as the query, market, language, or any other parameter that can contribute at the context in which the algorithmic results gets manifested. Thus, a meta-domain descriptor (MDD) is a collection of objects described in an independent context. 
     In the illustrated and described example, an MDD object has an extractor and a collection of features. A feature is a collection of actions. Examples of actions for an algorithmic result can include: instances number, order of instances (for a particular context in which order is displayed), page position, and the like. 
     In the following, let Extractor be represented as “E” and Feature be represented as “F” and Action be represented as “A,” then an object can be expressed as the following:
 
 O ={( E,F ( i )) with  i= 1 . . .  n} 
 
where  F={A ( i ), with  i= 1 . . .  n}.  
 
Then a meta-domain descriptor can be expressed by MDD={O(i) with i=1 . . . n:O(i) context independent}
 
     An example of modeling a meta-domain descriptor as XLM data is shown in  FIG. 2 a    (using an example describing the Bing algorithmic result). In this particular example, “algo results” (i.e., the blue links that appear in a search results page which are illustrated in  FIG. 2 a    as included within apostrophes) are described. The meta-domain descriptor essentially describes or defines what an “algo result” means out of context. Here, there is an extractor, features, and actions. The extractor extracts the specific object from a particular page. In this instance, the extractor extracts “algo results.” The features assist in defining what a particular behavior means for the “algo results,” i.e., the blue links within the apostrophes. In this particular instance, the “algo results” are extracted and the behavior is analyzed. In this example, actions including color, page position for each blue link, and instances information all contribute to the behavior. In this manner, the features define properties that are of interest with respect to a particular service. 
     To conclude, a meta-domain descriptor is utilized to describe what part of the domain behavior is being examined and analyzed for deviations. 
     Matrix Execution 
     Matrix execution describes an object context generator and can be thought of as describing the “question” that is to be asked. A matrix execution puts a MDD in various kinds of contexts. A simple example using algorithmic results would be to render them for specific queries, markets, languages, and the like. 
     In the following, let a context be “C” and expressed by C={O(i) with i=1 . . . n:O(i) context dependent}. Then a matrix execution can be expressed by MTX={C(i), with i=1 . . . n}. And, subsequently MDD is subset of C. The MTX can be thought of as the mechanism that consumes an MDD. 
     An example of modeling a matrix execution as XML data is illustrated in  FIG. 2 b    using an example generating Bing contexts. In the illustrated and described example, matrix execution is built on using dynamic parameters. Each parameter replaces one variable from the endpoint value. In this example, there is a parameter “query” having values “Microsoft” and “Johann Sebastian Bach”. At runtime, there will be a market “en-US” and a query of “Microsoft” and a query of “Johann Sebastian Bach”. Likewise, there will be a market of “en-GB” and a query of “Microsoft” and a query of “Johann Sebastian Bach”. 
     Behavior Collection Initial Phase 
     Consider now a behavior collection initial phase. Specifically, now that a MTX and MDD have been established, collection of a behavior is described. A MDD is examined in a specific context triggered by MTX. Then the MDD is expressed in a specific context by MDD(MTX)={O(i,C(j)), with i=1 . . . n,j=1 . . . m} where O(i,C(j))={(F(i,C(j)) with i=1 . . . n,j=1 . . . m} than by replacement in the first expression the following expression is obtained: MDD(MTX)={F(i,C(j)), with i=1 . . . n,j=1 . . . m}. So if a finite time period is defined as T={t(1), . . . t(k)} and a feature is collected for a specific context one over this time period, then this may be expressed as follows: F(1,C(1),T)={A(i,C(1),t(j)), with i=1 . . . n,j=1 . . . k} 
     The next step after obtaining those sets of values (for a feature in a specific context over a time period) is to cluster them using a machine learning hierarchical clustering algorithm (e.g., agglomerative approach). Looking at  FIG. 3 a   , Set 1 , Set 2 , and Set 3  are clustered. So at the end a behavior for an object feature in a specific context over a time period is a cluster of clusters. Let Cluster be CL={Set(i), with i=1 . . . n} and let a cluster of clusters be CLCL={CL(i), with i=1 . . . n}. As illustrated in  FIG. 3 a   , if CL 1 ={Set 1 ,Set 2 } and CL 2 ={Set 3 } then CLCL 1 ={CL 1 ,CL 2 }. As another example, consider  FIG. 3 b    which illustrates an example of clustering the set of values from  FIG. 3   a.    
     To conclude, a behavior is a cluster of clusters, computed using the hierarchical clustering algorithm (e.g., agglomerative approach), using data collected for an object feature in specific context over a period of time. This can be expressed as follows: let Behavior be B={CLCL(T)}, where CLCL is the cluster of clusters and T is the time period. 
     Behavior Collection Exercise Phase 
     Consider now a behavior collection exercise phase in accordance with one or more implementations. This phase is collected and computed in the same way as the initial phase (explained above). The difference here is the time (T). So if for an initial behavior phase a T={t(i), with i=1 . . . n} is chosen then for the exercise phase T′={t(j), with j=n+m, . . . k and m&gt;=1 and k&gt;m+n} is chosen. In other words, there is no overlap between the two time periods. So in this example of the exercise phase is T does not overlap with the T′ from the initial phase.  FIG. 3 c    provides an example of this. 
     The behavior for the exercise phase is shown in  FIG. 3 d   . The sets are collected over a different period of time therefore the cluster of clusters may look different. It should be noted that the T and T′ from initial and exercise behavior intervals are equal to achieve a symmetrical distribution data for each behavior phases. The context is uniformly spread across T, thus if j=i+1 and k=j+1 then t(j)−t(i)=t(k)−t(j) with i,j,k from 1 . . . n. 
     Computing Behavior Deviations 
     Consider now the computation of behavior deviations in accordance with one embodiment. That is, with the two phases of behavior explained above, the behavior deviation may not be computed. To compute a deviation between two distinct phases of the same behavior, the symmetrical difference is computed between the two clusters of clusters. So a symmetrical difference between:
 
 CLCL ( T ) ΔCLCL ( T ′)= CLCL ( T ) UCLCL ( T ′)− CLCL ( T )∩ CLCL ( T ′); or
 
 B ( T )Δ B ( T ′)= B ( T ) UB ( T ′)− B ( T )∩ B ( T ′)
 
is the actual deviation between the two phases of the same behavior. That is, the symmetrical difference is the union of the two clusters minus the intersection of the two clusters.  FIG. 3 e    illustrates an example of this. B(T)ΔB(T′) is greater than or equal to zero and less than or equal to one.
 
     A deviation equal to zero means no change in behavior; consequently a value equal to one means that the behavior has completely changed. Now a decision can be made as to what this deviation means, whether it is an issue or not, and if it is an issue, what kind of issue, e.g., security, regular regression, privacy, and so forth. 
     By collecting behavior deviations, classifying them, and making the system remember them, issues can be found including security, regular regression, privacy issues. The testing coverage is highly improved, is not highly human dependent as time goes by, and learns as well as is able to identify unknown issues. 
       FIG. 3 f    is a flow diagram that describes steps in behavior change detection method in accordance with one or more embodiments. The method can be implemented in connection with any suitable hardware, software, firmware, or combination thereof. 
     Step  320  collects data associated with an initial behavior phase of a service. Examples of how this can be done are provided above and below. 
     Step  322  collects data associated with an exercised behavior phase of the service. Examples of how this can be done are provided above and below. 
     Step  324  computes a deviation between the initial behavior phase and the exercised behavior phase. Examples of how this can be done are provided above. 
       FIG. 3 g    is a flow diagram that describes steps in a behavior change detection method that can be used to implement the method of  FIG. 3 f    in accordance with one or more embodiments. The method can be implemented in connection with any suitable hardware, software, firmware, or combination thereof. 
     Step  350  constructs a meta-domain descriptor. Examples of how this can be done are provided above. 
     Step  352  constructs a matrix execution to place the meta-domain descriptor in a specific context. 
     Step  354  collects an initial behavior using the meta-domain descriptor in a specific context triggered by the matrix execution. 
     Step  356  collects exercised behavior using the meta-domain descriptor in a specific context triggered by the matrix execution. 
     Step  358  computes a deviation between the initial behavior and the exercised behavior. 
     Having considered how a recognizer can be trained and deployed for use, and how behavior change analysis can be conducted, consider now a discussion of an example overall process for connecting various processing modules and performing operational evaluation, including continued training using machine learning techniques. 
     In Operation 
     The following discussion describes an iterative process through which security events can be recognized by a recognizer, and machine learning techniques can be employed to automatically and continuously enable the recognizer to further learn how to recognize security events. 
     In the discussion that follows, both  FIGS. 4 and 5  are utilized.  FIG. 4  describes the iterative process from the standpoint of the initial deployment of the recognizer, and  FIG. 5  describes how the recognizer can be further trained or “boosted.” For purposes of the discussion and because of spacing constraints, the names of the individual elements or modules have been removed. However, the corresponding numerical designators for each element have been carried through from the previous discussion. 
     Referring to  FIG. 4 , initial training occurs through the use of what is referred to as the initial ground truth for training data. The initial ground truth includes data that describes, for a particular online service, behaviors that appear to be normal and behaviors that appear to be not normal. This data can be developed over time and can be iteratively boosted by subsequent machine learning techniques, as will become apparent below. This data can reside in the form of both usage data and operational data as described above. 
     Training of the recognizer  212  using the initial ground truth takes place as described above. Once initially trained, the recognizer can be deployed as indicated by the arrow extending from the trained model event recognizer  306  to the rightmost recognizer  212 . 
     Referring now to  FIG. 5 , the deployed recognizer, i.e. the left-most recognizer  212 , is ready to take part in the iterative, machine learning process. In operation, when the recognizer  212  is online, it receives usage data  208  and operational data  210  and processes the data as described above. Specifically, the usage data  208  and operational data  210  are processed to produce recognized bad traffic or recognized events. 
     The system then employs an evaluation and scoring process during which time the recognized bad traffic is evaluated and scored for purposes of further honing the system&#39;s ability to recognize bad traffic. In this example, the evaluation and scoring process is represented by an arrow that extends from the left most recognized bad traffic  214  to a scoring table  500 . Each instance of recognized bad traffic is scored as either a “true positive”, “true negative”, “false positive”, or “false negative”. 
     As will be appreciated by the skilled artisan, “true positives” and “true negatives” are instances where the system is behaving as intended. That is to say, the system is correctly identifying and recognizing bad traffic and not recognizing traffic that is not bad traffic. The instances in which the evaluation and scoring process identifies a “false positive” or a “false negative” constitute instances in which an iterative learning process can be employed to further boost the accuracy with which the system can identify security threats. 
     Specifically, a “false positive” is a situation in which traffic was identified as bad but, in fact, the traffic was not bad. A “false negative” is a situation in which something should have been identified as bad traffic but was not identified as bad traffic. In both of these instances, an action is taken to boost the ground truth by providing this information back to the recognizer in the form of additional ground truth training data—both usage data and operational data—that can further be processed by the system. This data is also used as validation data for the user traffic module  204 . The result of using this additional training data is that the deployed recognizer can be boosted as indicated by the arrow extending from the trained model event recognizer  306  to the leftmost recognizer  212 . 
     This process can continue automatically to develop additional training data that is fed back into the system for both training and validation which, in turn, increases the effectiveness with which the recognizer can perform its operations. 
     Relating the System to the Schema 
     Earlier, the notion of a schema was introduced to describe data that is processed by the system as described above. The following discussion relates the schema to the system that was just described above. Similar to the manner in which  FIGS. 4 and 5  were discussed,  FIGS. 6 and 7  are now provided. Also provided is a table  600  that includes a mapping of numbers to schema elements. These numbers are then encircled and provided onto the diagrams in each figure to show where in the process, data of the schema elements is utilized. 
     Beginning with  FIG. 6 , metadata ( 01   d  and  02   d ) associated with usage data and operational data, respectively, is utilized to describe usage data ( 01 ) and operational data ( 02 ) respectively. The usage data and operational data are processed by their respective volatility processors  300 ,  302  to produce, respectively, usage volatility ( 03 ) and operational volatility ( 04 ) time series, as described above. These time series are processed by the time series streams correlator  304  to produce a feature stream ( 05 ). The feature stream captures the unified and correlated aggregation of both usage volatility and operational volatility in the time series bounded to pre-defined intervals such as, by way of example and not limitation, days, hours, minutes, seconds, milliseconds, and the like. The feature stream is processed by the trained model event recognizer  306  to produce recognized events ( 06 ), referred to as “bad traffic” in the above description. 
     Shifting now to  FIG. 7 , the recognized events ( 06 ) undergo an evaluation and scoring process to produce recognition scoring data ( 07 ) in which the recognized events are scored against the labeled ground truth. The process then continues as described above. That is, the recognizer can be boosted through machine learning techniques that employ identified false positives and false negatives to improve the system&#39;s ability to identify bad traffic or recognized events. 
     Testing Frequency Control Using a Volatility Score 
       FIG. 8  depicts an example system  800  showing in general how synthetic attack generators  210  of  FIG. 2  of the service testing system  110  of  FIG. 1  are fed to perform testing. As previously described, synthetic attack generators  210  are used in detecting security vulnerabilities and architectural weaknesses in services, such as one or more web applications implemented by the service. The service testing system  110  performs black-box testing, rather than white-box (knowing the source code) testing. 
     When deciding what to attack from a target, the synthetic attack generators  210  use a list  802  of URLs  804  provided by the user or automatically by the service testing system  110  as described above to create a target map. The synthetic attack generators  210  uses those URLs as a source to test (e.g., “attack”) the target. 
     The synthetic attack generator  210  performs this testing at a testing frequency, which describes a frequency of the attacks being performed as part of the testing. Functionality representative of computation of the frequency of attacks is represented by a frequency selection module  806 . For example, after the list  802  of URLs  804  that are to be tested is established, a testing frequency for the URLs  804  is selected by the frequency selection module  806 . The testing frequency describes a frequency to visit a URL, which can be static or dynamic. In a static case a fixed frequency is defined, whereas in a dynamic case various algorithms can be used. Static frequencies have a disadvantage by potentially overloading the system with unnecessary attacks as previously described. Therefore by choosing an algorithm to generate a dynamic frequency can reduce unnecessary attacks. 
     In the following, techniques are described to generate dynamic testing frequencies. Beforehand a volatility map is created and then used to determine the scanning frequencies for a target. Further these techniques may be employed dynamically such that as the volatility map changes, the frequencies of testing change as well. 
       FIG. 9  depicts an example system  900  showing creation of a volatility maps having volatility scores usable to create an attack surface for control testing of one or more services  108 . The first step in dynamically generate the scanning frequencies for the synthetic attack generator  210  of  FIG. 8  is to create a volatility map  902  for the service (i.e., target) being tested. Concepts including matrix execution and the meta-domain descriptor as described above are utilized as part of this discussion. 
     A representative list  802  of URLs  804  for a service is selected as described above, e.g., which represent unique paths and parameter combinations for the one or more services being tested, e.g., manually or automatically without user intervention by the service testing system  110 . This list  802  is then fed into the matrix execution  904  to generate a meta-domain descriptor  906  that represents the one or more services, i.e., the target. 
     As previously described, matrix execution  904  describes an object context generator and can be thought of as describing the “question” that is to be asked. A simple example using algorithmic results would be to render the results for specific queries, markets, languages, and the like. A meta-domain descriptor (MDD)  906  is utilized to describe what part of the domain behavior is being examined and analyzed for deviations. For example, a meta-domain descriptor  906  may be formed as a collection of objects described in an independent context. Consider, for example, applying this concept to “http://www.bing.com,” which is an HTML end point. 
     A volatility score  908  is then computed for each URL  804  by the volatility score computation module  910  for each URL as part of the matrix execution  904  using the meta-domain descriptor  906  representing the target. In this way, the volatility map  902  is formed having URLs  804  and associated volatility scores  908  that describe an amount of change in behavior of the respective URLs  804  as described above in relation to the machine-learning techniques. 
     For example, volatility involves behavior change detection to look at a service&#39;s behavior and detect any variances from what would otherwise be normal operating behavior. In operation, machine learning techniques are utilized as described above as an event classification mechanism which facilitates implementation scalability. The machine learning techniques are iterative and continue to learn over time. Operational scalability issues are addressed by using the computed volatility of the events in a time series as input for a classifier. During a learning process (i.e., the machine learning process), the system identifies relevant features that are affected by security incidents. When in operation, the system evaluates those features in real-time and provides a probability that an incident is about to occur. In this way, the volatility described measured changes in a behavior of a service independent of changes to content served by the service. The volatility map  902  is then used to control testing frequency, further discussion of which is described in the following and shown in a corresponding figure. 
       FIG. 10  depicts an example system  1000  showing dynamic generation of testing frequencies using the volatility map generated in  FIG. 9 . Using the volatility map  902  generated in  FIG. 9 , a frequency attack table  1002  is generated by the frequency selection module  806  in which a testing frequency  1004  is determined for URLs  804  to be tested by the synthetic attack generator  210 . Additionally, the frequency selection module  906  may be configured to adjust the testing frequencies  1004  dynamically responsive to changes in the volatility score  908  of the volatility map  902 , and in this way may efficiently address changing behaviors. 
     The testing frequency  1004  may be calculated in a variety of ways. In one or more implementations, the testing frequency  1004  is directly proportional to the volatility score  908  of a respective URL  804  such that as the volatility of a behavior increases, so too does the testing frequency  1004 . For example, a frequency limit  1006  may be chosen that describes a maximum amount of testing for a given URL  904 . For each of the URLS  804 , then the testing frequency  1004  is the frequency limit  1006  divided by the volatility score  908 . A variety of other examples are also contemplated without departing from the spirit and scope thereof, further discussion of which may be found in relation to the following procedures. 
     Example Procedures 
     The following discussion describes testing frequency control techniques that may be implemented utilizing the previously described systems and devices. Aspects of each of the procedures may be implemented in hardware, firmware, or software, or a combination thereof. The procedures are shown as a set of blocks that specify operations performed by one or more devices and are not necessarily limited to the orders shown for performing the operations by the respective blocks. In portions of the following discussion, reference will be made to the figures described above. 
     Functionality, features, and concepts described in relation to the examples of  FIGS. 1-10  may be employed in the context of the procedures described herein. Further, functionality, features, and concepts described in relation to different procedures below may be interchanged among the different procedures and are not limited to implementation in the context of an individual procedure. Moreover, blocks associated with different representative procedures and corresponding figures herein may be applied together and/or combined in different ways. Thus, individual functionality, features, and concepts described in relation to different example environments, devices, components, and procedures herein may be used in any suitable combinations and are not limited to the particular combinations represented by the enumerated examples. 
       FIG. 11  is a flow diagram that describes steps in a training procedure used to train a recognizer in accordance with one or more embodiments. The procedure can be implemented in connection with any suitable hardware, software, firmware, and the like. In at least some embodiments, the procedure or aspects thereof can be implemented by a suitably-configured recognizer, such as the recognizers described above. 
     Step  1100  produces usage data associated with an online service. Step  1102  produces operational data associated with the online service. Steps  1100  and  1102  can be performed in any suitable way. For example, in at least some embodiments, usage and operational data are produced by observing how users interact with the online service. As noted above, this can include both good and bad interactions. In addition, usage and operational data can be produced from synthetic attack patterns that are processed by the online service. Examples of synthetic attack patterns are provided above. Data produced by steps  1100  and  1102  can be produced in parallel. 
     Step  1104  processes the usage data and the operational data to produce a measure of behavioral changes over time. This step can be performed in any suitable way. For example, in the embodiments described above, volatility processors process the usage data and operational data, respectively, to produce a quantified measure that is correlated to the deviation of behavioral change over time. Step  1106  correlates behavioral changes of the usage data and the operational data. Examples of how this can be done are provided above. The usage data and operational data can be processed in parallel. 
     Step  1108  processes the correlated behavioral changes to recognize one or more events in which usage and operational behavioral changes deviate from historical data. 
       FIG. 12  is a flow diagram that describes steps in a procedure in which a trained recognizer, through machine learning techniques, can be continuously and automatically boosted to more effectively identify bad traffic or recognized events, in accordance with one or more embodiments. The procedure can be implemented in connection with any suitable hardware, software, firmware, and the like. In at least some embodiments, the procedure or aspects thereof can be implemented by a suitably-configured recognizer, such as the recognizer&#39;s described above. 
     Step  1200  provides a recognizer that has been trained with usage data and operational data. An example of how this can be done is provided above. Step  1202  processes received usage data and operational data to recognize one or more events in which usage and operational behavioral changes deviate from historical data. An example of how this can be done is provided above. Specifically, with respect to the method described in  FIG. 10 , one way in which this step can be performed is through the combination of steps  804 ,  806 , and  808 . 
     Step  1204  scores the recognized events to identify false positives and false negatives. Examples of how this can be done are provided above. Step  1206  uses the false positives and false negatives to further train the recognizer. Examples of how this can be done are provided above. The method can then return to step  1202  to continue receiving and processing usage and operational data as described above. 
       FIG. 13  is a flow diagram that describes steps in a procedure in which a trained recognizer, through machine learning techniques, can be continuously and automatically boosted to more effectively identify bad traffic or recognized events, in accordance with one or more embodiments. The procedure can be implemented in connection with any suitable hardware, software, firmware, and the like. In at least some embodiments, the procedure or aspects thereof can be implemented by a suitably-configured frequency selection module, volatility score computation module, and/or other components of the service testing system  110  as described above. 
     Step  1300  obtains a volatility map for one or more services to be tested by one or more computing devices, the volatility map having a listing of universal resource locators (URLs) of the one or more services with a respective volatility score that describes an amount of change in behavior associated with the URLs. 
     Step  1302  calculates a testing frequency by the one or more computing devices for one or more of the URLs based at least in part on the respective volatility scores of the volatility map for the one or more services. 
     Step  1302  controls testing by the one or more computing devices of the one or more URLs using the calculated testing frequency. 
     Example System and Device 
       FIG. 14  illustrates an example system generally at  1400  that includes an example computing device  1402  that is representative of one or more computing systems and/or devices that may implement the various techniques described herein, as illustrated through inclusion of the service testing system  110 . The computing device  1402  may be, for example, a server of a service provider, a device associated with a client (e.g., a client device), an on-chip system, and/or any other suitable computing device or computing system. 
     The example computing device  1402  as illustrated includes a processing system  1404 , one or more computer-readable media  1406 , and one or more I/O interface  1408  that are communicatively coupled, one to another. Although not shown, the computing device  1402  may further include a system bus or other data and command transfer system that couples the various components, one to another. A system bus can include any one or combination of different bus structures, such as a memory bus or memory controller, a peripheral bus, a universal serial bus, and/or a processor or local bus that utilizes any of a variety of bus architectures. A variety of other examples are also contemplated, such as control and data lines. 
     The processing system  1404  is representative of functionality to perform one or more operations using hardware. Accordingly, the processing system  1404  is illustrated as including hardware element  1410  that may be configured as processors, functional blocks, and so forth. This may include implementation in hardware as an application specific integrated circuit or other logic device formed using one or more semiconductors. The hardware elements  1410  are not limited by the materials from which they are formed or the processing mechanisms employed therein. For example, processors may be comprised of semiconductor(s) and/or transistors (e.g., electronic integrated circuits (ICs)). In such a context, processor-executable instructions may be electronically-executable instructions. 
     The computer-readable storage media  1406  is illustrated as including memory/storage  1412 . The memory/storage  1412  represents memory/storage capacity associated with one or more computer-readable media. The memory/storage component  1412  may include volatile media (such as random access memory (RAM)) and/or nonvolatile media (such as read only memory (ROM), Flash memory, optical disks, magnetic disks, and so forth). The memory/storage component  1412  may include fixed media (e.g., RAM, ROM, a fixed hard drive, and so on) as well as removable media (e.g., Flash memory, a removable hard drive, an optical disc, and so forth). The computer-readable media  1406  may be configured in a variety of other ways as further described below. 
     Input/output interface(s)  1408  are representative of functionality to allow a user to enter commands and information to computing device  1402 , and also allow information to be presented to the user and/or other components or devices using various input/output devices. Examples of input devices include a keyboard, a cursor control device (e.g., a mouse), a microphone, a scanner, touch functionality (e.g., capacitive or other sensors that are configured to detect physical touch), a camera (e.g., which may employ visible or non-visible wavelengths such as infrared frequencies to recognize movement as gestures that do not involve touch), and so forth. Examples of output devices include a display device (e.g., a monitor or projector), speakers, a printer, a network card, tactile-response device, and so forth. Thus, the computing device  1402  may be configured in a variety of ways as further described below to support user interaction. 
     Various techniques may be described herein in the general context of software, hardware elements, or program modules. Generally, such modules include routines, programs, objects, elements, components, data structures, and so forth that perform particular tasks or implement particular abstract data types. The terms “module,” “functionality,” and “component” as used herein generally represent software, firmware, hardware, or a combination thereof. The features of the techniques described herein are platform-independent, meaning that the techniques may be implemented on a variety of commercial computing platforms having a variety of processors. 
     An implementation of the described modules and techniques may be stored on or transmitted across some form of computer-readable media. The computer-readable media may include a variety of media that may be accessed by the computing device  1402 . By way of example, and not limitation, computer-readable media may include “computer-readable storage media” and “computer-readable signal media.” 
     “Computer-readable storage media” may refer to media and/or devices that enable persistent and/or non-transitory storage of information in contrast to mere signal transmission, carrier waves, or signals per se. Thus, computer-readable storage media refers to non-signal bearing media. The computer-readable storage media includes hardware such as volatile and nonvolatile, removable and non-removable media and/or storage devices implemented in a method or technology suitable for storage of information such as computer readable instructions, data structures, program modules, logic elements/circuits, or other data. Examples of computer-readable storage media may include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical storage, hard disks, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or other storage device, tangible media, or article of manufacture suitable to store the desired information and which may be accessed by a computer. 
     “Computer-readable signal media” may refer to a signal-bearing medium that is configured to transmit instructions to the hardware of the computing device  1402 , such as via a network. Signal media typically may embody computer readable instructions, data structures, program modules, or other data in a modulated data signal, such as carrier waves, data signals, or other transport mechanism. Signal media also include any information delivery media. The term “modulated data signal” means a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal. By way of example, and not limitation, communication media include wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared, and other wireless media. 
     As previously described, hardware elements  1410  and computer-readable media  1406  are representative of modules, programmable device logic and/or fixed device logic implemented in a hardware form that may be employed in some embodiments to implement at least some aspects of the techniques described herein, such as to perform one or more instructions. Hardware may include components of an integrated circuit or on-chip system, an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), a complex programmable logic device (CPLD), and other implementations in silicon or other hardware. In this context, hardware may operate as a processing device that performs program tasks defined by instructions and/or logic embodied by the hardware as well as a hardware utilized to store instructions for execution, e.g., the computer-readable storage media described previously. 
     Combinations of the foregoing may also be employed to implement various techniques described herein. Accordingly, software, hardware, or executable modules may be implemented as one or more instructions and/or logic embodied on some form of computer-readable storage media and/or by one or more hardware elements  1410 . The computing device  1402  may be configured to implement particular instructions and/or functions corresponding to the software and/or hardware modules. Accordingly, implementation of a module that is executable by the computing device  1402  as software may be achieved at least partially in hardware, e.g., through use of computer-readable storage media and/or hardware elements  1410  of the processing system  1404 . The instructions and/or functions may be executable/operable by one or more articles of manufacture (for example, one or more computing devices  1402  and/or processing systems  1404 ) to implement techniques, modules, and examples described herein. 
     As further illustrated in  FIG. 14 , the example system  1400  enables ubiquitous environments for a seamless user experience when running applications on a personal computer (PC), a television device, and/or a mobile device. Services and applications run substantially similar in all three environments for a common user experience when transitioning from one device to the next while utilizing an application, playing a video game, watching a video, and so on. 
     In the example system  1400 , multiple devices are interconnected through a central computing device. The central computing device may be local to the multiple devices or may be located remotely from the multiple devices. In one embodiment, the central computing device may be a cloud of one or more server computers that are connected to the multiple devices through a network, the Internet, or other data communication link. 
     In one embodiment, this interconnection architecture enables functionality to be delivered across multiple devices to provide a common and seamless experience to a user of the multiple devices. Each of the multiple devices may have different physical requirements and capabilities, and the central computing device uses a platform to enable the delivery of an experience to the device that is both tailored to the device and yet common to all devices. In one embodiment, a class of target devices is created and experiences are tailored to the generic class of devices. A class of devices may be defined by physical features, types of usage, or other common characteristics of the devices. 
     In various implementations, the computing device  1402  may assume a variety of different configurations, such as for computer  1414 , mobile  1416 , and television  1418  uses. Each of these configurations includes devices that may have generally different constructs and capabilities, and thus the computing device  1402  may be configured according to one or more of the different device classes. For instance, the computing device  1402  may be implemented as the computer  1414  class of a device that includes a personal computer, desktop computer, a multi-screen computer, laptop computer, netbook, and so on. 
     The computing device  1402  may also be implemented as the mobile  1416  class of device that includes mobile devices, such as a mobile phone, portable music player, portable gaming device, a tablet computer, a multi-screen computer, and so on. The computing device  1402  may also be implemented as the television  1418  class of device that includes devices having or connected to generally larger screens in casual viewing environments. These devices include televisions, set-top boxes, gaming consoles, and so on. 
     The techniques described herein may be supported by these various configurations of the computing device  1402  and are not limited to the specific examples of the techniques described herein. This functionality may also be implemented all or in part through use of a distributed system, such as over a “cloud”  1420  via a platform  1422  as described below. 
     The cloud  1420  includes and/or is representative of a platform  1422  for resources  1424 . The platform  1422  abstracts underlying functionality of hardware (e.g., servers) and software resources of the cloud  1420 . The resources  1424  may include applications and/or data that can be utilized while computer processing is executed on servers that are remote from the computing device  1402 . Resources  1424  can also include services provided over the Internet and/or through a subscriber network, such as a cellular or Wi-Fi network. 
     The platform  1422  may abstract resources and functions to connect the computing device  1402  with other computing devices. The platform  1422  may also serve to abstract scaling of resources to provide a corresponding level of scale to encountered demand for the resources  1424  that are implemented via the platform  1422 . Accordingly, in an interconnected device embodiment, implementation of functionality described herein may be distributed throughout the system  1400 . For example, the functionality may be implemented in part on the computing device  1402  as well as via the platform  1422  that abstracts the functionality of the cloud  1420 . 
     Conclusion and Example Implementations 
     Example implementations described herein include, but are not limited to, one or any combinations of one or more of the following examples: 
     In one or more examples, a method includes of testing frequency control of one or more services, the method comprising obtaining a volatility map for one or more services to be tested by one or more computing devices, the volatility map having a listing of universal resource locators (URLs) of the one or more services with a respective volatility score that describes an amount of change in behavior associated with the URLs; calculating a testing frequency by the one or more computing devices for one or more of the URLs based at least in part on the respective volatility scores of the volatility map for the one or more services; and controlling testing by the one or more computing devices of the one or more URLs using the calculated testing frequency. 
     An example as described alone or in combination with any of the above or below examples, wherein the changes in the behavior are independent of changes to content served by the service. 
     An example as described alone or in combination with any of the above or below examples, wherein the calculating is performed such that the testing frequency is increased in response to increases the volatility score. 
     An example as described alone or in combination with any of the above or below examples, wherein the calculating is performed dynamically to reflect changes in the volatility map. 
     An example as described alone or in combination with any of the above or below examples, wherein the amount of changes are calculated by: collecting data associated with an initial behavior phase of the one or more service; collecting data associated with an exercised behavior phase of the one or more services; and computing a deviation between the initial behavior phase and the exercised behavior phase. 
     An example as described alone or in combination with any of the above or below examples, wherein: collecting data associated with the initial behavior phase comprises using a meta-domain descriptor in a specific context triggered by a matrix execution; or collecting data associated with the exercised behavior phase comprises using a meta-domain descriptor in a specific context triggered by a matrix execution. 
     An example as described alone or in combination with any of the above or below examples, wherein collecting data associated with the initial behavior phase comprises using a meta-domain descriptor in a specific context triggered by a matrix execution; and wherein collecting data associated with the exercised behavior phase comprises using the meta-domain descriptor in a specific context triggered by the matrix execution. 
     An example as described alone or in combination with any of the above or below examples, wherein the meta-domain descriptor is a collection of objects described in an independent context. 
     An example as described alone or in combination with any of the above or below examples, wherein the meta-domain descriptor includes an extractor that extracts specific objects from a page and a collection of features which define what a behavior means. 
     An example as described alone or in combination with any of the above or below examples, wherein collecting data associated with the initial behavior phase and collecting data associated with the exercised behavior phase are performed over different finite time periods having the same length. 
     An example as described alone or in combination with any of the above or below examples, wherein collecting data associated with the initial behavior phase defines a set of values and wherein collecting data associated with the exercised behavior phase defines another set of values and further comprising clustering both sets of values into respective clusters. 
     A service testing system to control testing frequency of one or more services, the system comprising one or more computing devices having one or more modules implemented at least partially in hardware, the one or more modules configured to perform operations comprising: obtaining a volatility map for one or more services to be tested, the volatility map having a listing of universal resource locators (URLs) of the one or more services with a respective volatility score that measures changes in a behavior of the one or more services independent of changes to content served by the one or more services; calculating a testing frequency for one or more of the URLs based at least in part on the respective volatility scores of the volatility map for the one or more services; and controlling testing of the one or more URLs using the calculated testing frequency. 
     An example as described alone or in combination with any of the above or below examples, wherein the volatility scores describe an amount of change in behavior associated with the URLs. 
     An example as described alone or in combination with any of the above or below examples, wherein the calculating is performed such that the testing frequency is increased in response to increases the volatility score. 
     An example as described alone or in combination with any of the above or below examples, wherein the calculating is performed dynamically to reflect changes in the volatility map. 
     A computing device to control testing frequency of one or more services, the computing device comprising: one or more processors; and one or more computer-readable storage media comprising instructions stored thereon that, responsive to execution by the one or more processors, causes the one or more processors to perform operations comprising: calculating a volatility map for one or more services to be tested, the volatility map having a listing of universal resource locators (URLs) of the one or more services with a respective volatility score that measures changes in a behavior of the one or more services independent of changes to content served by the one or more services; calculating a testing frequency for one or more of the URLs based at least in part on the respective volatility scores of the volatility map for the one or more services; and controlling testing of the one or more URLs using the calculated testing frequency. 
     An example as described alone or in combination with any of the above or below examples, wherein the volatility scores describe an amount of change in behavior associated with the URLs. 
     An example as described alone or in combination with any of the above or below examples, wherein the calculating is performed such that the testing frequency is increased in response to increases the volatility score. 
     An example as described alone or in combination with any of the above or below examples, wherein the calculating is performed dynamically to reflect changes in the volatility map. 
     An example as described alone or in combination with any of the above or below examples, wherein the changes are calculated by collecting data associated with an initial behavior phase of the one or more services; collecting data associated with an exercised behavior phase of the one or more services; and computing a deviation between the initial behavior phase and the exercised behavior phase. 
     Although the example implementations have been described in language specific to structural features and/or methodological acts, it is to be understood that the implementations defined in the appended claims is not necessarily limited to the specific features or acts described. Rather, the specific features and acts are disclosed as example forms of implementing the claimed features.