Abstract: Various implementations provide an approach to control testing frequency based on behavior change detection. Behavior change detection is utilized, instead of a pre-defined patterns approach, to look at a system's behavior and detect any variances from what would otherwise be normal operating behavior. 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, which the system uses to control testing frequency of one or more services, such as URLs that are part of a particular service.

Abstract: Vulnerability testing tasks can be received and distributed, via a work scheduler, to computer test environments. Each of the test environments can have a detector computing component running in the environment. Each detector component can respond to receiving one of the tasks from the work scheduler by conducting a vulnerability test on an endpoint of a target, detecting results of the vulnerability test, generating output indicating the results of the vulnerability test, and sending the output to an output processor. The work scheduler can initiate dynamic scaling of the test environments by activating and deactivating test environments in response to determining that the test environments are overloaded or underloaded, respectively. Also an overall time-based limit on testing for a target can be enforced via the work scheduler.

Abstract: Template identification techniques for control of testing are described. In one or more implementations, a method is described to control testing of one or more services by one or more computing devices using inferred template identification. Templates are inferred, by the one or more computing devices, that are likely used for documents for respective services of a service provider that are available via corresponding universal resource locators (URLs) to form an inferred dataset. Overlaps are identified by the one or computing devices in the inferred dataset to cluster services together that have likely used corresponding templates. Testing is controlled by the one or more computing devices of the one or more services based at least in part on the clusters.

Abstract: An encryption/decryption method are described, which includes that a terminal performs an encryption operation according to information of an encryption sub-node of an Encrypt And Decrypt Management Object (EADMO) node of a local Device Management (DM) tree and reports to a DM server encrypted data state information generated after the encryption succeeds; and the terminal performs a decryption operation according to the encrypted data state information sent from the DM server. An encryption/decryption system and device are also described. By means of technical solutions of embodiments of the disclosure, operations are simple without causing data loss, and a problem that data of a non-local terminal cannot be encrypted is solved.

Abstract: Template identification techniques for control of testing are described. In one or more implementations, a method is described to control testing of one or more services by one or more computing devices using inferred template identification. Templates are inferred, by the one or more computing devices, that are likely used for documents for respective services of a service provider that are available via corresponding universal resource locators (URLs) to form an inferred dataset. Overlaps are identified by the one or computing devices in the inferred dataset to cluster services together that have likely used corresponding templates. Testing is controlled by the one or more computing devices of the one or more services based at least in part on the clusters.

Abstract: 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's behavior and detect any variances from what would otherwise be normal operating behavior. 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. 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.

Abstract: An encryption/decryption method are described, which includes that a terminal performs an encryption operation according to information of an encryption sub-node of an Encrypt And Decrypt Management Object (EADMO) node of a local Device Management (DM) tree and reports to a DM server encrypted data state information generated after the encryption succeeds; and the terminal performs a decryption operation according to the encrypted data state information sent from the DM server. An encryption/decryption system and device are also described. By means of technical solutions of embodiments of the disclosure, operations are simple without causing data loss, and a problem that data of a non-local terminal cannot be encrypted is solved.

Abstract: Integrated fuzzing techniques are described. A fuzzing system may employ a container configured as a separate component that can host different target pages to implement fuzzing for an application. A hosted target file is loaded as a subcomponent of the container and parsed to recognize functionality of the application invoked by the file. In at least some embodiments, this involves building a document object model (DOM) for a browser page and determining DOM interfaces of a browser to call based on the page DOM. The container then operates to systematically invoke the recognized functionality to cause and detect failures. Additionally, the container may operate to perform iterative fuzzing with multiple test files in an automation mode. Log files may be created to describe the testing and enable both self-contained replaying of failures and coverage analysis for multiple test runs.

Abstract: Integrated fuzzing techniques are described. A fuzzing system may employ a container configured as a separate component that can host different target pages to implement fuzzing for an application. A hosted target file is loaded as a subcomponent of the container and parsed to recognize functionality of the application invoked by the file. In at least some embodiments, this involves building a document object model (DOM) for a browser page and determining DOM interfaces of a browser to call based on the page DOM. The container then operates to systematically invoke the recognized functionality to cause and detect failures. Additionally, the container may operate to perform iterative fuzzing with multiple test files in an automation mode. Log files may be created to describe the testing and enable both self-contained replaying of failures and coverage analysis for multiple test runs.

Abstract: Integrated fuzzing techniques are described. A fuzzing system may employ a container configured as a separate component that can host different target pages to implement fuzzing for an application. A hosted target file is loaded as a subcomponent of the container and parsed to recognize functionality of the application invoked by the file. In at least some embodiments, this involves building a document object model (DOM) for a browser page and determining DOM interfaces of a browser to call based on the page DOM. The container then operates to systematically invoke the recognized functionality to cause and detect failures. Additionally, the container may operate to perform iterative fuzzing with multiple test files in an automation mode. Log files may be created to describe the testing and enable both self-contained replaying of failures and coverage analysis for multiple test runs.

Abstract: Integrated fuzzing techniques are described. A fuzzing system may employ a container configured as a separate component that can host different target pages to implement fuzzing for an application. A hosted target file is loaded as a subcomponent of the container and parsed to recognize functionality of the application invoked by the file. In at least some embodiments, this involves building a document object model (DOM) for a browser page and determining DOM interfaces of a browser to call based on the page DOM. The container then operates to systematically invoke the recognized functionality to cause and detect failures. Additionally, the container may operate to perform iterative fuzzing with multiple test files in an automation mode. Log files may be created to describe the testing and enable both self-contained replaying of failures and coverage analysis for multiple test runs.

Abstract: A method of accessing a shared data structure in parallel by multiple threads in a parallel application program is disclosed. A lock of the shared data structure is granted to one thread of the multiple threads, an operation of the thread which acquires the lock is performed on the shared data structure, an operation of each thread of the multiple threads which does not acquire the lock is buffered, and the buffered operations are performed on the shared data structure when another thread of the multiple threads subsequently acquires the lock. A corresponding apparatus and program product are also disclosed.

Abstract: A method of accessing a shared data structure in parallel by multiple threads in a parallel application program is disclosed, in which a lock of the shared data structure is granted to one thread of the multiple threads, an operation of the thread which acquires the lock is performed on the shared data structure, then an operation of each thread of the multiple threads which does not acquire the lock is buffered, and finally the buffered operations are performed on the shared data structure when another thread of the multiple threads subsequently acquires the lock. By using this method, the operations of other threads which do not acquire the lock of the shared data structure can be buffered automatically when the shared data structure is locked by one thread, and all the buffered operations can be performed when another thread acquires the lock.