Test tool teaching integrated device

Abstract

The embodiment of the invention discloses a test tool teaching integrated device. According to one specific embodiment, the test tool teaching integrated device comprises a service module, an interface module, a resource module and a tool plug-in, and the service module is configured to store teaching data, generate a control instruction, issue the control instruction to the interface module and process data information sent by the interface module; an interface module configured to: coordinate communication between different resource modules and service modules; and the resource module is configured to register an address at the interface module, upload the state data and receive an instruction issued by the interface module. According to the embodiment, reasonable utilization of teaching resources can be realized.

Description

Technical Field

[0001] Embodiments of the present disclosure relate to the field of test tool teaching integration, and in particular to a test tool teaching integration device. Background Art

[0002] The knowledge in the field of software testing is vast and rich, and learning resources are scattered across various channels and sources. The teaching integration platform integrates various learning resources, including tutorials, tools, documents, laboratory environments, etc., so that students can obtain the required learning materials on the same platform, avoiding the trouble of searching and collecting information in various places.

[0003] When setting up a real security test environment, installing test tools requires a lot of time and resources, including hardware, software, and network resources, etc. Especially in the network environment of a school or company, it may be restricted and difficult to build a complete experimental environment.

[0004] During the experimental operation, students often make deviations due to the complexity of using the testing tools, resulting in inaccurate experimental results. During the experimental guidance, teachers are unable to obtain the details of students' operations, resulting in teaching effects that fail to meet expectations.

[0005] When conducting software testing teaching experiments, some real system or network operations may be involved, which may bring certain security risks, such as abnormal test execution or system paralysis caused by erroneous operation.

[0006] However, the prominent problem in related technologies and products lies in the integrated management of tools and tested environments. Since various testing tools have unique ways of use, the inconsistency of the ways of use increases the cost of using the tools, affecting the efficiency of teaching use.

[0007] The above information disclosed in this Background section is only for enhancement of understanding of the background of the inventive concept and therefore it may contain information that does not form the prior art that is already known in this country to a person of ordinary skill in the art. Summary of the invention

[0008] The content of this disclosure is used to introduce concepts in a brief form, which will be described in detail in the detailed implementation section below. The content of this disclosure is not intended to identify the key features or essential features of the technical solution claimed for protection, nor is it intended to limit the scope of the technical solution claimed for protection.

[0009] Some embodiments of the present disclosure propose a test tool teaching integrated device to solve one or more of the technical problems mentioned in the above background technology section.

[0010] In the first aspect, some embodiments of the present disclosure provide a test tool teaching integration device, which includes: a service module, an interface module, a resource module and a tool plug-in, wherein the service module is configured to: store teaching data, generate control instructions, send them to the interface module, and process data information sent by the interface module; the interface module is configured to: coordinate the communication between different resource modules and the service module; the resource module is configured to: register an address at the interface module, upload status data, and receive instructions sent by the interface module.

[0011] Optionally, the above service module is deployed on a high-performance server; the above service module is configured to: share teaching course arrangements, provide teaching suggestions and experimental materials, store student operation processes and experimental data, and monitor the tool operation status and resource usage in each host.

[0012] Optionally, the above-mentioned interface module is deployed on a high-performance server; the above-mentioned interface module is configured to: receive operation instructions issued by the service module, share teaching handouts, experimental materials and experimental environment addresses, collect status data sent by the resource module, feed back the status data sent by the resource module to the service module, allocate resources required for teaching activities, forward teaching handouts, experimental materials and experimental environment addresses to the resource module, issue teaching experiment operation tasks, and collect experimental operation data.

[0013] Optionally, the communication interface between the above interface module and the resource module is an application program interface of REST API, which locates services and allocates access priorities through a gateway.

[0014] Optionally, the above-mentioned resource module is deployed on the student host and the high-performance server; the above-mentioned resource module integrates the tool plug-in; the above-mentioned resource module is configured to: manage test tool resources, process computing resources and disk storage resources, generate test tool driver scripts, start the test tool, execute test experiment operations, collect operation step information and experimental results, receive instructions issued by the interface module, and uniformly manage the tools used for teaching in the form of plug-ins, and reasonably allocate resources through central deployment and edge computing mode.

[0015] Optionally, the resource module is configured to send a device identifier to the interface module, and keep the device online through regular heartbeat data interaction, wherein the heartbeat data includes local performance indicators and tool operation status.

[0016] Optionally, the tool plug-in includes: a tool driver module and a test processing module, wherein the tool driver module includes: a tool driver with an interface and a tool driver without an interface; the tool driver with an interface is configured to: simulate operations through icon driving and interface UI logo; the tool driver without an interface is configured to: simulate operations through command execution and interface driving.

[0017] Optionally, the test processing module is configured to obtain test results through tool interfaces, result files, and interface screenshots, perform data conversion on the test results to generate unified result interface information, and send the unified result interface information to the interface module.

[0018] Optionally, the tool plug-in also includes: a status monitoring module and an operation recording module, wherein the status monitoring module is configured to: monitor the tool operation status information, identify the normal operation status information, store the normal operation status information in the operation log, and periodically send feedback data to the resource module, wherein the tool operation status information includes: tool process information, execution log, command line print output information, and the feedback data includes: process identification, tool name, operation status, execution log file path, task identification; the tool driver module is configured to: parse the tool name, query the tool path, execution file, interface startup status, command template, startup script, and feedback the tool operation process identification; the operation recording module is configured to: record the student experiment operation step information.

[0019] Optionally, the operation recording module is further configured to: query the correct input parameters from the database based on the experiment course identifier; in response to receiving the student input parameters input by the student host, compare the student input parameters with the correct input parameters to generate a parameter comparison result; in response to determining that the parameter comparison result meets the preset abnormal parameter conditions, send the preset abnormal information and the correct input parameters to the student host to perform alarm processing on the student host; determine the student input parameters and the parameter comparison result as student operation step information.

[0020] Optionally, the above-mentioned operation recording module is further configured to: query the correct input parameters from the database based on the experimental course identifier; send a graphic page to the student host to receive the input operation information of the student host on the graphic page; dispatch a camera to shoot the student processing video of the above-mentioned student host; perform image recognition processing on the above-mentioned student processing video to generate student processing information; in response to receiving the input operation information sent by the above-mentioned student host, compare the above-mentioned correct input parameters, the above-mentioned input operation information, and the above-mentioned student processing information to generate a parameter comparison result; in response to determining that the above-mentioned parameter comparison result meets the preset abnormal parameter condition, send the preset abnormal information and the above-mentioned correct input parameters to the above-mentioned student host to perform alarm processing on the above-mentioned student host; determine the above-mentioned input operation information, the above-mentioned student processing information and the above-mentioned parameter comparison result as student operation step information.

[0021] Optionally, the resource module is further configured to: deploy the test tools that meet the preset resource conditions in a high-performance server; and deploy the test tools that do not meet the preset resource conditions in a student host.

[0022] The above-mentioned embodiments of the present disclosure have the following beneficial effects: the present disclosure relates to a test tool teaching integrated device, and relates to the field of test teaching and training, wherein the test tool teaching integrated device includes: a service module, an interface module, a resource module and a tool plug-in. The resource module is installed on the student host and the test server, receives the service module instructions, and is used to manage the test tool resources, process computing resources, disk storage resources, etc.; the interface module is used to receive the teaching experiment operation tasks, provide teaching handouts and experimental materials according to the experimental course arrangement, generate the test tool driving script, and collect and analyze the test tool results; the service module sends the tool driving script to the student host or the test server according to the experimental course according to the feedback information of the resource module, and starts the test tool, executes the test experiment content, and monitors the tool operation and resource occupancy; the tool plug-in module is used to integrate different types of test tools, and has unified input and output. Therefore, the center plus edge computing method is used to complete the integration of the test tool and the environment, and the functional encapsulation is completed by customizing the encapsulation of various tools and environments, so that the remote call of the tool can be realized, and the execution results can be collected, which can effectively realize the flexible expansion of the platform to the tool, and can support various network security testing tasks in a distributed environment. Therefore, the rational use of teaching resources can be achieved through the unified scheduling of experimental tools by the test tool teaching integrated device. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the accompanying drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and that components and elements are not necessarily drawn to scale.

[0024] Figure 1 is a schematic diagram of an application scenario of the test tool teaching integrated device according to the present disclosure; Figure 2 is a schematic diagram of another application scenario of the test tool teaching integrated device according to the present disclosure; Figure 3 It is a schematic diagram of the structure of some embodiments of the test tool teaching integrated device according to the present disclosure; Figure 4 It is a structural schematic diagram of some embodiments of the tool plug-in of the test tool teaching integrated device according to the present disclosure; Figure 5 It is a structural schematic diagram of an image recognition model according to a public testing tool teaching integrated device. DETAILED DESCRIPTION

[0025] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments set forth herein. On the contrary, these embodiments are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

[0026] It should also be noted that, for ease of description, only the parts related to the invention are shown in the drawings. In the absence of conflict, the embodiments and features in the embodiments of the present disclosure can be combined with each other.

[0027] It should be noted that the concepts such as "first" and "second" mentioned in the present disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0028] It should be noted that the modifications of "one" and "plurality" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0029] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0030] The present disclosure will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments.

[0031] Figure 1 It is a schematic diagram of an application scenario of the testing tool teaching integrated device according to the present disclosure.

[0032] exist Figure 1 In the application scenario, when the student host uses the test tool teaching integrated device, the resource module 30, the interface module 20 and the tool plug-in 40 are executed.

[0033] During the teaching process, when the student host starts the test tool teaching integrated device, the resource module 30 registers information in the interface module 20 and waits for the teaching data sent by the interface module 20. When the student starts to learn the experimental content, the experimental tool is selected in the resource module 30. The tool without interface is started by filling in the execution parameters; the tool with interface starts the tool human-machine interface.

[0034] The resource module 30 uses a unified data format to encapsulate the experimental tool parameters into a unified interface information (the data format is JSON format {"key":"value"}), and calls the experimental tool command line, API (Application Programming Interface) interface and human-computer interface through the tool plug-in 40. The interface module 40 processes the input parameters, where the key (keyword) represents the selected experimental course identifier, experimental course name, experimental course content, experimental tool type, experimental tool execution command and command parameter type, and the value (attribute value) is mainly used to carry specific data content. The processed communication data uses JSON (JavaScript Object Notation, a lightweight data exchange format) format.

[0035] For the tool plug-in 40, different experimental contents and input parameters are assembled into specific experimental instructions and sent to the corresponding experimental tools. That is to say, after receiving the data from the resource module 30, the tool plug-in 40 parses the specific operation content and parses it into corresponding execution commands, API interface calls and human-computer interfaces according to different experimental tools (for example, tool 1, tool 2... tool N). The interface plug-in implements the specific drive execution of the tool.

[0036] The tool plug-in 40 also has a recording module that collects the commands, parameters and specific graphic operations input by students and saves them locally as experimental execution records. If an operation error occurs, guidance prompts can also be provided.

[0037] The tool plug-in 40 can also monitor the status of the tool during the experiment execution process, feedback normal or abnormal information, and send the monitored intermediate data to the resource module 30 in real time, which is sorted by the resource module 30 and sent to the interface module 20 in the form of a heartbeat packet; The tool plug-in 40 also collects and processes the result data returned by the tool after completing the experimental task. The result data is mainly structured data and unstructured data. Students can view and export the execution results through the resource module 30 for writing experimental reports. Students store the experimental reports in the shared directory in the format specified by the teacher, and the experimental data files are named: Experiment Name_Student ID_Completion Time.

[0038] After completing the experiment, the student imports the completed experiment report through the resource module 30 according to the specified format, and feeds back the experiment data and experiment report to the interface module 20.

[0039] Figure 2 It is a schematic diagram of another application scenario of the testing tool teaching integrated device according to the present disclosure.

[0040] exist Figure 2In the application scenario, the classroom host executes the service module 10, the interface module 20 and the resource module 30 when using the test tool teaching integrated device.

[0041] Before the experimental course begins, the teacher uploads the experimental handouts and formulates the experimental steps through the service module 10. After the experimental course begins, it is sent to each resource module 30 (including: resource module 1, resource module 2...resource module N) through the interface module 20.

[0042] During the experimental course, the teacher can obtain the data of the students' execution process in real time through the interface module 20. If the students encounter unsolvable problems during the experiment, such as various operational abnormalities, deviations in experimental results, etc., the teacher can view the various real-time data collected by the interface module 20 through the service module 10 to help students locate the problems.

[0043] During the experimental course, after completing the experimental content, students upload the experimental report and experimental data to the interface module 20 through the resource module. After the interface module 20 collects the experimental data, the collected experimental data includes structured data and unstructured data. The structured data is stored in the database, and the unstructured data is stored in the shared folder of the object storage.

[0044] After the experimental course is completed, the teacher can view the students' experimental results and experimental reports through the service module 10 and score the quality of the students' experimental completion.

[0045] refer to Figure 3 , shows a schematic diagram of the structure of some embodiments of the test tool teaching integrated device according to the present disclosure. Figure 3 As shown, the above-mentioned test tool teaching integrated device includes: a service module 10, an interface module 20, a resource module 30, and a tool plug-in 40.

[0046] In some embodiments, the service module 10 is configured to store teaching data, generate control instructions, send them to the interface module, and process data information sent by the interface module. The interface module 20 is configured to coordinate the communication between different resource modules and the service module. The resource module 30 is configured to register an address in the interface module, upload status data, and receive instructions sent by the interface module.

[0047] Here, the tool plug-in 40 can be installed on a high-performance server or a student host. The tool plug-in 40 can be a unified interface framework, which integrates the test tools needed in teaching into the resource module according to a unified instruction input and result output method. The interface framework completes the customization of various tools and uses the JSON (JavaScript Object Notation, a lightweight data exchange format) data exchange format to achieve interaction with the resource module. The interface framework sends various drive and call commands to the tool, collects execution steps and results, and can effectively achieve flexible expansion of teaching tools.

[0048] It should be noted that the testing tools involved in the present disclosure include open source testing tools and non-open source testing tools. For non-open source testing tools, it supports calling with command line, API (Application Programming Interface) and graphical interface modes. For open source testing tools, on the basis of the support method of non-open source testing tools, source code extension is added, and the plug-in source code is integrated with the testing tool source code to form a tool plug-in suitable for this device.

[0049] Optionally, the service module 10 is deployed on a high-performance server. The service module 10 is configured to: share teaching course schedules, provide teaching suggestions and experimental materials, store student operation processes and experimental data, and monitor tool operation status and resource usage in each host.

[0050] The teaching data stored in the service module 10 may include but is not limited to at least one of the following: experimental course identification, experimental course name, experimental course content, experimental tool type, experimental tool execution command and command parameter type. The format of the teaching data stored in the service module 10 is: {"key", "value"}. The key (keyword) can represent: experimental course identification, experimental course name, experimental course content, experimental tool type, experimental tool execution command or command parameter type. The value (attribute value) can represent the specific data content.

[0051] Optionally, the interface module 20 is deployed on a high-performance server. The interface module 20 is configured to: receive operation instructions issued by the service module, share teaching handouts, experimental materials and experimental environment addresses, collect status data sent by the resource module, feed back the status data sent by the resource module to the service module, allocate resources required in teaching activities, forward teaching handouts, experimental materials and experimental environment addresses to the resource module, issue teaching experiment operation tasks, and collect experimental operation data.

[0052] Here, the interface module 20 is further configured to: send instructions to the service module 10, distribute them to the hosts where the resource modules 30 are located, and monitor the data uploaded by the resource modules 30 in real time. Students obtain the test tools and experimental instructions that need to be run through the interface module 20 according to the experimental content, submit the experimental results after completing the experiment, and obtain the student's experimental process operation records at the same time. When the student's operation makes a mistake, the error location is fed back and the correct operation prompt is given. After obtaining the experimental results, the interface module 20 uploads them to the service module 10.

[0053] The interface module 20 is further configured to: adopt a resource module scheduling strategy based on load prediction, and the resource module scheduling strategy may include: a local control strategy and a global control strategy.

[0054] Here, the local control strategy can be: the first step is to determine the CPU resource utilization rate of the node device based on the amount of CPU (Central Processing Unit) resources requested by each experimental tool and the total amount of CPU resources. Among them, the amount of CPU resources requested by the experimental tool can be: the amount of CPU resources requested by the experimental tool running on the node device. The node device can be a student host or a high-performance server. In practice, the CPU resource utilization rate of the node device can be determined based on the amount of CPU (Central Processing Unit) resources requested by each experimental tool and the total amount of CPU resources by the following formula: .

[0055] in, Indicates time. express CPU resource usage of the node device at a certain moment. Indicates a sequence number. Indicates the total number of experimental tools running on the node device. Indicates the total amount of CPU resources on the node device. Indicates Experimental tools in The amount of CPU resources requested at any given moment.

[0056] The second step is to determine the local resource energy consumption rate of the node device based on the energy consumption rate of the node device when idle, the maximum resource energy consumption value of the node device, and the CPU resource utilization rate of the node device. In practice, the local resource energy consumption rate of the node device can be determined based on the energy consumption rate of the node device when idle, the maximum resource energy consumption value of the node device, and the CPU resource utilization rate of the node device using the following formula: .

[0057] in, express The local resource energy consumption rate of the node device at this moment. Indicates the energy consumption rate of the node device when it is idle. Indicates the maximum energy consumption value of the node device resources.

[0058] The third step is to determine the local resource energy consumption value of the target node device within the first preset time period. The first preset time period may be a time period between a first moment and a second moment. For example, the first moment may be a moment five minutes before the current moment. The second moment may be the current moment. In practice, the local resource energy consumption value of the target node device within the first preset time period may be determined by the following formula: .

[0059] in, Represents the local resource energy consumption value of the target node device within the first preset time period. Indicates the first moment. Indicates the second moment.

[0060] The fourth step is to predict the predicted energy consumption rate of the local resources of the node device within the second preset time period based on the local resource energy consumption value of the target node device. The second time period may be the time period between the second moment and the third moment. For example, the third moment may be the moment five minutes after the current moment. In practice, the predicted energy consumption change value of the local resources of the node device within the second preset time period may be predicted based on the local resource energy consumption value of the target node device by the following formula: .

[0061] in, Indicates the predicted energy consumption change value of the local resources of the node device within the second preset time period. It is the degree of bias of the predicted value to the adjacent continuous monitoring values. for The sequence number of the time interval. For time interval series The change value of resource energy consumption.

[0062] Therefore, ARIMA time series analysis can be used to predict The resource energy consumption variation range in the time interval.

[0063] The fifth step is to dynamically allocate resources such as CPU, memory, and network bandwidth, and dynamically schedule resources to achieve local resource load balancing. Thus, local resources including CPU, memory, and network bandwidth can be dynamically allocated to node devices that need local resources as represented by the predicted energy consumption change value of the local resources of the node devices within the second preset time period.

[0064] The global control strategy may be: first, obtain the resource scheduling command and scheduling data transmitted by the local control strategy. Then, according to the remaining resources of each node device, as well as the predicted energy consumption change value of the local resources of the node device and the energy consumption value of the local resources of the target node device, determine whether the remaining resources of each node device meet the needs of resource scheduling (that is, determine the size between the remaining resources of each node device and the sum of the predicted energy consumption change value of the local resources of the node device and the energy consumption value of the local resources of the target node device). Afterwards, in response to determining that the remaining resources of the node device meet the resource scheduling requirements (that is, the remaining resources of each node device are greater than or equal to the sum of the predicted energy consumption change value of the local resources of the node device and the energy consumption value of the local resources of the target node device), schedule resources for the current node device. In response to determining that the fertility resources of the node device do not meet the resource scheduling requirements (that is, the remaining resources of each node device are less than the sum of the predicted energy consumption change value of the local resources of the node device and the energy consumption value of the local resources of the target node device), migrate the node device with the smallest amount of resources, and release the resources of the node device for subsequent resource scheduling.

[0065] Optionally, the communication interface between the interface module 20 and the resource module 30 is a REST API (Representational State Transfer Application Programming Interface), which locates services and allocates access priorities through a gateway.

[0066] Optionally, the resource module 30 is deployed on the student host and the high-performance server. The resource module 30 integrates the tool plug-in 40. The resource module 30 is configured to: manage test tool resources, process computing resources and disk storage resources, generate test tool driver scripts, start test tools, perform test experiment operations, collect operation step information and experimental results, receive instructions issued by the interface module, and manage the tools used for teaching in a unified manner in the form of plug-ins (for example, test tool parameter configuration, test tool start test, test tool stop test, upload test results, etc.), and reasonably allocate resources through the central deployment plus edge computing mode.

[0067] Here, the resource module 30 is further configured to: parse the experimental task, determine the experimental tool, query and match in the database according to the tool plug-in information where the experimental tool is located, generate the command line parameters, API interface variables and interface operation icons required for the execution of the specific tool, and the specific operation steps required to operate the experimental tool.

[0068] Optionally, the resource module 30 is further configured to: deploy the test tools that meet the preset resource conditions in the high-performance server, and deploy the test tools that do not meet the preset resource conditions in the student host. For example, the preset resource condition may be that the resource usage rate is greater than 80%.

[0069] Here, the resource module 30 can manage one or more tool plug-ins 40. For example, in response to receiving the task unified interface data, the resource module 30 parses the specific experimental content and calls different tool plug-ins 40. Here, the tool plug-in 40 can be parsed into corresponding execution commands (for example, the execution command can be: tool execution command, API interface and graphical driver) according to different experimental tools to complete the execution of the experimental tool.

[0070] Optionally, the resource module 30 is configured to send the device identification to the interface module, and keep the device online through regular heartbeat data interaction, wherein the heartbeat data includes: local performance indicators and tool operation status.

[0071] The tool plug-in 40 is configured to: generate a driving experiment tool, monitor the running status, record the student's operation steps, and complete the collection and processing of experimental data.

[0072] Here, the tool plug-in 40 starts the experiment tool in a non-interface manner or an interface manner.

[0073] Among them, when the tool plug-in 40 starts the experimental tool without an interface, it is completed through command execution and API call. When receiving the experimental content issued by the resource module, it parses and obtains the tool name, and obtains the tool installation path, execution file name and parameter template in the local database according to the tool name. In practice, when the student host operates the interface-free experimental tool through the tool plug-in 40, fill in the tool call parameters according to the experimental handouts, and start the experiment after completing the tool configuration. The tool plug-in 40 receives the parameters to drive the tool to complete the experiment, and the data returned by the tool execution process is fed back in real time during the tool execution process. If the student fills in the parameters incorrectly, the wrong parameters will be prompted during the experiment, and the correct parameters will be prompted to enter.

[0074] Among them, when the tool plug-in 40 starts the experimental tool through the interface, the tool plug-in 40 starts the tool, opens the graphical interface of the experimental tool, and records the operation steps in real time during the students' graphical interface operation. According to the screenshot of the operation steps and the comparison with the experimental handouts, the student operation process is recorded. When the student makes an error in the key operation steps, the error location will be prompted, and the correct operation method will be prompted. In practice, the tool plug-in 40 splits the experimental execution results, obtains multiple basic experimental test data, labels multiple experimental data, and obtains unified interface information including experimental task identification, experimental result content, and whether the execution is correct.

[0075] Further references Figure 4 , shows a schematic diagram of the structure of some embodiments of the tool plug-in of the test tool teaching integrated device according to the present disclosure. Figure 4 As shown, the tool plug-in 40 includes: a tool driving module 401 and a test processing module 402 .

[0076] In some embodiments, the tool driver module 401 includes: a tool driver with an interface and a tool driver without an interface. The tool driver with an interface is configured to: simulate operation through icon driving and UI identification. The tool driver without an interface is configured to: simulate operation through command execution and interface driving.

[0077] Optionally, the tool driving module 401 is used to drive the experimental tool to complete the experiment.

[0078] The tool driver module 401 is configured to: obtain tool instructions, search for corresponding tool information from the driver module information library, combine the startup script according to the driver module information library information, complete the startup of the experimental tool, and feedback the tool running process ID. When the resource module sends down the stop operation, the script driver module parses the tool running process ID and closes the experimental tool. If the closure is successful, there is no tool process in the process, and feedback is a success ID. Here, the description information of the driver module information library is as follows: .

[0079] Among them, ID is the ID (Identity document) of the deployed test driver module, which is the unique identifier of each driver module. ToolName is the tool name information. ToolPath is the specific path where the tool is installed. ExeFile is the name of the executable file when the tool is started. IsGUI is whether the tool is started through the interface. Command is the template for executing commands.

[0080] Optionally, the test processing module 402 is configured to obtain test results through tool interfaces, result files, and interface screenshots, perform data conversion on the test results to generate unified result interface information, and send the unified result interface information to the interface module.

[0081] The test processing module 402 is configured to process the running results of the experimental tool, which may include: JSON format result report, XML (Extensible Markup Language) format result report, command line printing, log output, and tool screenshots.

[0082] Optionally, the tool plug-in 40 further includes: a status monitoring module 403 and an operation recording module 404 .

[0083] In some embodiments, the above-mentioned status monitoring module 403 is configured to: monitor the tool operation status information, identify the normal operation status information, store the normal operation status information in the operation log, and send feedback data to the resource module at regular intervals. Among them, the tool operation status information includes: tool process information, execution log, command line print output information. Normal operation status information includes but is not limited to: whether the tool has been started, whether the execution is normal, and whether the tool is occupied. Feedback data includes: process identification, tool name, operation status, execution log file path, task identification. The above-mentioned tool driver module 401 is configured to: parse the tool name, query the tool path, execution file, interface startup status, command template, startup script, and feedback tool operation process identification. The above-mentioned operation recording module 404 is configured to: record student experimental operation step information. For example, the operation recording module 404 has two recording modes, including: command parameter recording and experimental course identification.

[0084] Here, the operation modes of the test processing module 402 include automatic processing and manual processing. In the automatic processing mode, when the status monitoring module 403 detects that the experimental tool has been executed or manually stopped, the collected tool operation results are stored in the local database and shared directory, and the tool operation process identifier is sent to the resource module; in the manual processing mode, the student stores the experimental data in the shared directory according to the format specified by the teacher, and the experimental data file is named: Experiment Name_Student ID_Completion Time. The test processing module 402 regularly scans the directory processing results.

[0085] Optionally, the status monitoring module 403 is configured to: monitor the running status of the tool, including the tool process, execution log, command line printout, etc., and determine whether the tool has been started, whether the execution is normal, whether the tool is occupied, etc. according to the tool running status. When the tool is executed normally, the normal running status is recorded and stored in the running log. When the resource module starts the tool plug-in, the status monitoring module 401 starts running and regularly feeds back to the resource module. The feedback data includes: process ID, tool name, running status, execution log file path, task ID.

[0086] Optionally, the operation recording module 404 is further configured to: The first step is to query the correct input parameters from the database based on the experimental course identifier. In practice, the operation record module can use the experimental course identifier as an identifier to query the correct input parameters from the database. Among them, the database can be used to store the experimental course identifier and the correct input parameters. The experimental course identifier can uniquely identify an experimental course. The correct input parameters can characterize the correct operating steps of the student host under the experimental course. Here, the database can be generated by the following steps: Before the start of the experimental course, the teacher records the standard experimental operation steps, takes video screenshots of the key steps of the experiment, and then uses a convolutional neural network to extract the correct operation step features, and stores the correct operation step features to form a vectorized experimental operation library.

[0087] In the second step, in response to receiving the student input parameters input by the student host, the student input parameters and the correct input parameters are compared to generate parameter comparison results. The parameter comparison results can indicate whether the student operation is correct. Thus, the operation recording module can compare the parameters input by the student to determine whether the student operation is correct.

[0088] In the third step, in response to determining that the parameter comparison result satisfies the preset abnormal parameter condition, the preset abnormal information and the correct input parameter are sent to the student host to perform an alarm process on the student host. The preset abnormal parameter condition may be: the parameter comparison result indicates that the student operation is incorrect. The preset abnormal information may be information indicating "parameter input error".

[0089] The fourth step is to determine the student input parameters and the comparison results of the above parameters as the student operation step information.

[0090] Therefore, the students' experimental operation steps can be recorded through the recording mode of command parameter recording.

[0091] Optionally, the operation recording module 404 is further configured to: The first step is to query the correct input parameters from the database based on the experimental course identifier. In practice, the operation record module can query the correct input parameters from the database using the experimental course identifier as an identifier. The database can be used to store the experimental course identifier and the correct input parameters. The experimental course identifier can uniquely identify an experimental course. The correct input parameters can represent the correct operation steps of the student host under the experimental course.

[0092] The second step is to send the graphic page to the student host to receive the input operation information of the student host on the graphic page. The input operation information may include but is not limited to: mouse click, drag and drop, and file import.

[0093] The third step is to dispatch a camera to capture the student processing video of the student host, wherein the student processing video may be a video representing the execution action of the student host on the graphic page.

[0094] The fourth step is to perform image recognition processing on the student processing video to generate student processing information. In practice, the operation recording module can input the student processing video into the image recognition model to obtain the student processing information. Here, the image recognition model can be a pre-trained model that takes the student processing video as input and takes the student processing information as output. The image recognition model can refer to Figure 5 The structure diagram of the image recognition model of the test tool teaching integrated device according to the present application is shown in FIG. Figure 5 As shown, the image recognition model can include: convolution layer 1, pooling layer 1, convolution layer 2, pooling layer 2, convolution layer 3, pooling layer 3, convolution layer 4, pooling layer 4, convolution layer 5, pooling layer 5, convolution layer 6-7, fusion P5, fusion P4, fusion P3, 8x deconvolution, 16x deconvolution, and 32x deconvolution.

[0095] like Figure 5 As shown in the figure, the image recognition model uses transposed convolution to upsample the student-processed feature map, gradually restore the original image size, and obtain a dense pixel prediction image of the same size as the teacher's standard experimental operation. In the image recognition model, upsampling was performed three times, namely 8x deconvolution, 16x deconvolution, and 32x deconvolution.

[0096] In addition, the image recognition model introduces an attention mechanism. In the upsampled student processing feature map, given a retrieval task related to the classroom standard trial operation, the attention is calculated based on the student processing feature map: .

[0097] in, is the soft attention distribution, i.e., select The probability of an index position. Attention scoring function based on the dot product model.

[0098] like Figure 5 As shown in the figure, the image recognition model adds a skip structure. In addition to upsampling the feature map output by the last layer of the network, fusion P5 is upsampled by 2 times and feature fused with pooling layer 4 to obtain fusion 4. Fusion P4 is then upsampled by 2 times and feature fused with pooling layer 3 to obtain fusion P3.

[0099] Therefore, the image recognition model can be an improved convolutional neural network model built using the VGG16 (Visual Geometry Group Network16, 16-layer visual geometry group network) architecture. First, convolutional layers 6-7 are used to replace the fully connected layers of the convolutional neural network, so that the image recognition model can intercept student processing videos from different perspectives, ensuring that student processing operation images of any size can be received. Secondly, transposed convolution is used to upsample the student processing feature map to gradually restore the original image size so as to obtain a dense pixel prediction image of the same size as the teacher's standard experimental operation. After that, the attention mechanism is introduced to calculate the attention, select the focus position, determine the importance of different features or regions, and generate a higher resolution feature representation. Then, weight sharing is realized to reduce the number of parameters. Through the weights learned by VGG16 in the full convolutional network, the surface features of the classroom standard experimental operation and the coordinate position of the student processing operation are extracted, and the network feature extraction capability is improved. Next, a skip-level structure is added. In addition to upsampling the feature map output by the last layer of the network, fusion P5 is upsampled by 2 times and feature fused with pooling layer 4 to obtain fusion 4. Fusion P4 is then upsampled by 2 times and feature fused with pooling layer 3 to obtain fusion P3. Shallow image detail information is fused in the student processing video to ensure that the obtained student processing operation segmentation map retains more local detail information.

[0100] In the fifth step, in response to receiving the input operation information sent by the student host, the correct input parameters, the input operation information, and the student processing information are compared to generate a parameter comparison result. The parameter comparison result can characterize whether the student operation is correct. Here, the student input operation information can be generated by the following steps: during the student's experimental operation, the operation video is recorded and compared with the experimental operation library, the student operation video is framed by second, and each frame image is compared with the experimental operation library one by one to obtain the student operation input operation information.

[0101] Step 6: In response to determining that the parameter comparison result satisfies the preset abnormal parameter condition, the preset abnormal information and the correct input parameter are sent to the student host to perform an alarm process on the student host. The preset abnormal parameter condition may be: the parameter comparison result indicates that the student operation is incorrect. The preset abnormal information may be information indicating "parameter input error".

[0102] Step 7: Determine the above input operation information, the above student processing information and the above parameter comparison result as the student operation step information.

[0103] Therefore, the recording mode of the graphical interface operation record can be used to record the students' experimental operation steps.

[0104] The present invention adopts a loosely coupled approach to manage test tools, breaking away from the traditional security testing platform model where each tool needs to be developed in a unified and integrated manner. It has greater flexibility and scalability, and is very suitable for customized and large-scale security testing. The implementation principle is clear and simple, and it is easy to deploy and implement quickly, with high practical value.

[0105] The above-mentioned embodiments of the present disclosure have the following beneficial effects: the present disclosure relates to a test tool teaching integrated device, and relates to the field of test teaching and training, wherein the test tool teaching integrated device includes: a service module, an interface module, a resource module and a tool plug-in. The resource module is installed on the student host and the test server, receives the service module instructions, and is used to manage the test tool resources, process computing resources, disk storage resources, etc.; the interface module is used to receive the teaching experiment operation tasks, provide teaching handouts and experimental materials according to the experimental course arrangement, generate the test tool driving script, and collect and analyze the test tool results; the service module sends the tool driving script to the student host or the test server according to the experimental course according to the feedback information of the resource module, and starts the test tool, executes the test experiment content, and monitors the tool operation and resource occupancy; the tool plug-in module is used to integrate different types of test tools, and has unified input and output. Therefore, the center plus edge computing method is used to complete the integration of the test tool and the environment, and the functional encapsulation is completed by customizing the encapsulation of various tools and environments, so that the remote call of the tool can be realized, and the execution results can be collected, which can effectively realize the flexible expansion of the platform to the tool, and can support various network security testing tasks in a distributed environment. Therefore, the rational use of teaching resources can be achieved through the unified scheduling of experimental tools by the test tool teaching integrated device.

[0106] The above descriptions are only some preferred embodiments of the present disclosure and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present disclosure is not limited to the technical solutions formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above inventive concept. For example, the above features are replaced with (but not limited to) technical features with similar functions disclosed in the embodiments of the present disclosure.

Claims

1. A test tool teaching integrated device, characterized in that: The test tool teaching integrated device includes: a service module, an interface module, a resource module and a tool plug-in, wherein: The service module is configured to: store teaching data, generate control instructions, send them to the interface module, and process data information sent by the interface module; The interface module is configured to: coordinate the communication between different resource modules and service modules; The resource module is configured to: register an address in the interface module, upload status data, and receive instructions issued by the interface module; The service module is deployed on a high-performance server; the service module is configured to: share teaching course arrangements, provide teaching suggestions and experimental materials, store student operation processes and experimental data, and monitor the tool operation status and resource usage in each host; The interface module is deployed on a high-performance server; the interface module is configured to: receive operation instructions issued by the service module, share teaching handouts, experimental materials and experimental environment addresses, collect status data sent by the resource module, feed back the status data sent by the resource module to the service module, allocate resources required in teaching activities, forward teaching handouts, experimental materials and experimental environment addresses to the resource module, issue teaching experiment operation tasks, and collect experimental operation data; Among them, the resource module is deployed on the student host and high-performance server; the resource module integrates the tool plug-in; the resource module is configured to: manage test tool resources, process computing resources and disk storage resources, generate test tool driver scripts, start test tools, execute test experiment operations, collect operation step information and experimental results, receive instructions issued by the interface module, and uniformly manage the tools used for teaching in the form of plug-ins, and reasonably allocate resources through central deployment and edge computing mode.

2. The test tool teaching integrated device according to claim 1, characterized in that: The communication interface between the interface module and the resource module is a REST API application program interface, which locates services and allocates access priorities through the gateway.

3. The test tool teaching integrated device according to claim 2, characterized in that: The resource module is configured to send the device identification to the interface module, and keep the device online through regular heartbeat data interaction, wherein the heartbeat data includes local performance indicators and tool operation status.

4. The test tool teaching integrated device according to claim 3, characterized in that: The tool plug-in includes: a tool driving module and a test processing module, wherein: The tool driving module includes: a tool driving with an interface and a tool driving without an interface; The interface tool driver is configured to: simulate operation through icon driving and interface UI identification; The non-interface tool driver is configured to: execute through commands and simulate the operation of the interface driver.

5. The test tool teaching integrated device according to claim 4, characterized in that: The test processing module is configured to: obtain test results through tool interfaces, result files, and interface screenshots, perform data conversion processing on the test results to generate result unified interface information, and send the result unified interface information to the interface module.

6. The test tool teaching integrated device according to claim 4, characterized in that: The tool plug-in also includes: a status monitoring module and an operation recording module, wherein: The status monitoring module is configured to: monitor the tool operation status information, identify the normal operation status information, store the normal operation status information in the operation log, and regularly send feedback data to the resource module, wherein the tool operation status information includes: tool process information, execution log, command line print output information, and the feedback data includes: process identifier, tool name, operation status, execution log file path, and task identifier; The tool driver module is configured to: parse the tool name, query the tool path, execution file, interface startup status, command template, startup script, and feedback the tool running process identifier; The operation recording module is configured to record the student's experimental operation step information.

7. The test tool teaching integrated device according to claim 6, characterized in that: The operation recording module is further configured to: Based on the experimental course identifier, query the database for correct input parameters; In response to receiving the student input parameters input by the student host, performing a comparison process on the student input parameters and the correct input parameters to generate a parameter comparison result; In response to determining that the parameter comparison result meets the preset abnormal parameter condition, the preset abnormal information and the correct input parameter are sent to the student host to perform alarm processing on the student host; The student input parameters and the parameter comparison results are determined as student operation step information.

8. The test tool teaching integrated device according to claim 7, characterized in that: The operation recording module is further configured to: Based on the experimental course identifier, query the database for correct input parameters; Sending a graphic page to the student host to receive input operation information of the student host on the graphic page; The camera is dispatched to capture the student processing video of the student host; Performing image recognition processing on the student processing video to generate student processing information; In response to receiving the input operation information sent by the student host, performing comparison processing on the correct input parameter, the input operation information, and the student processing information to generate a parameter comparison result; In response to determining that the parameter comparison result meets the preset abnormal parameter condition, the preset abnormal information and the correct input parameter are sent to the student host to perform alarm processing on the student host; The input operation information, the student processing information and the parameter comparison result are determined as student operation step information.

9. The test tool teaching integrated device according to claim 5, characterized in that: The resource module is further configured to: Deploy the test tools that meet the preset resource conditions on high-performance servers; Deploy test tools that do not meet the preset resource conditions on student hosts.

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