Method, device, electronic device and computer program product for scheduling password evaluation tool

Abstract

The present invention discloses a scheduling method, device, electronic device and computer program product for password evaluation tools. The method comprises: obtaining an application scenario topology map of the object to be tested, wherein the application scenario topology map includes at least: pre-identified evaluation points; based on the evaluation points in the application scenario topology map, selecting a key feature set for evaluating the evaluation points from a material library; based on the key feature set, using multiple password evaluation tools in a preset password evaluation tool set, constructing a random forest for evaluating the evaluation points, the random forest includes: multiple randomly established decision trees, each decision tree includes: some or all of the password evaluation tools in the preset password evaluation tool set; in the random forest, scheduling the password evaluation tools according to the decision trees with the largest number of the same number. The present invention solves the technical problem of low scheduling efficiency of password evaluation tools caused by relying on manual scheduling of password evaluation tools.

Description

Technical Field

[0001] The present invention relates to the field of computers, and in particular to a scheduling method, device, electronic device and computer program product for a password assessment tool. Background Art

[0002] Cryptographic technology enables security functions such as encryption, identity authentication, and signature verification, ensuring the confidentiality and integrity of transmitted data, the authenticity of entities, and the non-repudiation of critical operations. Conducting cryptographic assessments can evaluate the compliance, correctness, and effectiveness of cryptographic applications, helping to identify security issues promptly.

[0003] In the process of conducting security assessments of cryptographic applications in electronic information systems, the assessment tools used can be divided into two categories based on the scope of application: dedicated assessment tools and general assessment tools. Dedicated assessment tools are mainly used to detect and analyze part or all of the compliance, correctness, and effectiveness of the cryptographic applications of the tested system. General assessment tools are not limited to specific areas of application and are universal.

[0004] During the password evaluation process, evaluators need to conduct extensive standard compliance testing and cryptographic algorithm calculations. Using professional evaluation tools can significantly improve evaluators' work efficiency and the reliability of evaluation results. The evaluation process often involves multiple evaluation implementations, and each implementation conclusion may require multiple evaluation tools to collaborate and analyze the tested data. However, because existing tools are not designed with interoperability in mind, evaluators are required to manually complete batch tasks such as scheduling between tools, importing and exporting data, and converting formats. This not only increases the workload, but also inevitably leads to errors in manual processing, ultimately affecting the accuracy of evaluation conclusions. Furthermore, the combination of evaluation solutions still relies on human experience, which is inefficient.

[0005] Currently, no effective solution has been proposed to the problem of low scheduling efficiency of password assessment tools caused by reliance on manual scheduling of password assessment tools. Summary of the Invention

[0006] Embodiments of the present invention provide a scheduling method, device, electronic device and computer program product for a password assessment tool, so as to at least solve the technical problem of low scheduling efficiency of the password assessment tool due to reliance on manual scheduling of the password assessment tool.

[0007] According to one aspect of an embodiment of the present invention, a scheduling method for a password evaluation tool is also provided, comprising: obtaining an application scenario topology diagram of a tested object, wherein the application scenario topology diagram at least comprises: the connection relationship between the components in the tested object, and evaluation points pre-identified in the application scenario topology diagram; selecting a key feature set for evaluating the evaluation point from a material library according to the evaluation point in the application scenario topology diagram, wherein the material library at least comprises: the tested data uploaded by the tested object, the key feature set comprising: a plurality of feature indicators determined based on the tested data; using a preset password test tool according to the key feature set. A plurality of password evaluation tools in the preset password evaluation tool set are used to construct a random forest for evaluating the evaluation points, wherein the preset password evaluation tool set includes: the password evaluation tools corresponding to the plurality of characteristic indicators respectively, and the random forest includes: a plurality of randomly established decision trees, the nodes in the decision trees correspond one-to-one to the password evaluation tools, and each of the decision trees includes: part or all of the password evaluation tools in the preset password evaluation tool set; in the random forest, the password evaluation tools are scheduled according to the decision trees with the largest number of the same number, wherein the same number is the number of the same decision trees with the same node distribution.

[0008] Optionally, based on the key feature set, multiple password evaluation tools recorded in a preset password evaluation tool set are used to construct a random forest for evaluating the evaluation points, including: performing replacement sampling on multiple feature indicators in the key feature set to obtain multiple sub-sample sets, wherein the number of feature indicators in the sub-sample sets is the same as the number of feature indicators in the key feature set; in each of the sub-sample sets, randomly selecting a preset number of feature indicators to construct the decision tree, wherein the preset number of features is the square root of the number of feature indicators in the sub-sample set; and determining the random forest based on the decision tree constructed based on multiple sub-sample sets.

[0009] Optionally, in each of the sub-sample sets, a preset number of feature indicators are randomly selected, and constructing the decision tree includes: in each of the sub-sample sets, randomly selecting the feature indicators that meet the preset number to obtain a set to be trained; using the set to be trained as the root node of the decision tree, and using the splitting point with the smallest Gini index to split the root node into two child nodes, wherein the splitting point is used to split the set to be trained into a first training subset and a second training subset, and the child nodes include: a first child node corresponding to the first training subset, and a second child node corresponding to the second training subset; detecting whether the number of the feature indicators in the set to be trained corresponding to each child node reaches a preset number threshold, wherein the set to be trained is the first training subset or the second training subset; when the number of the feature indicators in the set to be trained corresponding to the child node reaches the preset number threshold, using the child node as a leaf node of the decision tree to complete the construction of the decision tree.

[0010] Optionally, the method further includes: when the number of the characteristic indicators in the to-be-trained set corresponding to the child node does not reach the preset number threshold, using the child node as the root node.

[0011] Optionally, taking the set to be trained as the root node of the decision tree, and using the splitting point with the smallest Gini index to split the root node into two child nodes includes: determining the feature categories to which multiple feature indicators in the set to be trained belong, wherein the feature indicators of each feature category are evaluated using the password evaluation tool of the corresponding tool category; traversing each feature category in the set to be trained, determining the splitting point corresponding to each feature category, and calculating the Gini index after dividing the set to be trained according to each splitting point; selecting the splitting point with the smallest Gini index, splitting the set to be trained into a first training subset and a second training subset, and obtaining the two child nodes of the root node.

[0012] Optionally, in the random forest, scheduling the password evaluation tool according to the decision trees with the largest number includes: counting the same number of each decision tree with the same node distribution among the multiple decision trees in the random forest; selecting any one of the decision trees of the same type with the largest number as the target decision tree; and calling the password evaluation tool corresponding to each node in the target decision tree in sequence from the root node to the leaf node to evaluate the characteristic indicators corresponding to the node.

[0013] Optionally, in the random forest, after scheduling the password evaluation tools according to the decision trees with the same largest number, the method further includes: using the scheduled password evaluation tools to evaluate the tested objects according to the evaluation points to obtain the evaluation results corresponding to each of the password evaluation tools; reporting each of the evaluation results to the material library; calling a report generation tool to use the evaluation results reported to the material library as task materials to generate an evaluation report.

[0014] According to another aspect of an embodiment of the present invention, a scheduling device for a password evaluation tool is provided, comprising: an acquisition module for acquiring an application scenario topology map of a measured object, wherein the application scenario topology map includes at least: connection relationships between components in the measured object and evaluation points pre-identified in the application scenario topology map; a selection module for selecting a key feature set for evaluating the evaluation point from a material library based on the evaluation point in the application scenario topology map, wherein the material library includes at least: measured data uploaded by the measured object, and the key feature set includes: a plurality of feature indicators determined based on the measured data; a construction module for constructing a random forest for evaluating the evaluation point based on the key feature set using a plurality of password evaluation tools in a preset password evaluation tool set, wherein the preset password evaluation tool set includes: the password evaluation tools corresponding to the plurality of feature indicators, respectively, and the random forest includes: a plurality of randomly established decision trees, wherein the nodes in the decision trees correspond one-to-one to the password evaluation tools, and each decision tree includes: some or all of the password evaluation tools in the preset password evaluation tool set;

[0015] A scheduling module is used to schedule the password evaluation tool in the random forest according to the decision trees with the largest number of the same number, wherein the same number is the number of the same decision trees with the same node distribution.

[0016] According to another aspect of an embodiment of the present invention, an electronic device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to execute the scheduling method of the above-mentioned password assessment tool through the computer program.

[0017] According to another aspect of an embodiment of the present invention, a computer program product is provided, comprising computer instructions, which, when executed by a processor, implement the steps of the scheduling method of the above-mentioned password assessment tool.

[0018] In an embodiment of the present invention, according to the evaluation points identified in the application scenario topology diagram of the object to be tested, a key feature set relied on for evaluating the object to be tested based on the evaluation points can be selected from the material library, and then a random forest for calling the password evaluation tool can be automatically constructed based on the key feature set, and the optimal decision tree can be selected from the random forest to schedule the password evaluation tool, so that the scheduling of the password evaluation tool can get rid of the dependence on manual labor, thereby achieving the technical effect of improving the scheduling efficiency of the password evaluation tool, and further solving the technical problem of low scheduling efficiency of the password evaluation tool caused by relying on manual labor for scheduling the password evaluation tool. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0020] Figure 1 is a flow chart of a scheduling method for a password assessment tool according to an embodiment of the present invention;

[0021] Figure 2 is a schematic diagram of a classification of password assessment tools according to an embodiment of the present invention;

[0022] Figure 3 A schematic diagram of an adaptive scheduling scheme for a password assessment tool according to an embodiment of the present invention;

[0023] Figure 4 A schematic diagram of a unified interface model of a password assessment tool according to an embodiment of the present invention;

[0024] Figure 5 A schematic diagram of a generated evaluation strategy solution according to an embodiment of the present invention;

[0025] Figure 6 is a schematic diagram of a scheduling device of a password assessment tool according to an embodiment of the present invention;

[0026] Figure 7 It is a structural block diagram of a computer terminal according to an embodiment of the present invention. DETAILED DESCRIPTION

[0027] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0028] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0029] According to an embodiment of the present invention, an embodiment of a scheduling method for a password assessment tool is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0030] Figure 1 is a flow chart of a scheduling method for a password assessment tool according to an embodiment of the present invention. Figure 1 As shown, the method includes the following steps:

[0031] Step S102: obtaining an application scenario topology diagram of the object under test, wherein the application scenario topology diagram includes at least: connection relationships between components in the object under test, and evaluation points pre-marked in the application scenario topology diagram;

[0032] Step S104: selecting a key feature set for evaluating the evaluation point from a material library based on the evaluation point in the application scenario topology diagram, wherein the material library at least includes: the test data uploaded by the test object; and the key feature set includes: a plurality of feature indicators determined based on the test data;

[0033] Step S106: Based on the key feature set, a random forest is constructed using multiple password evaluation tools from a preset password evaluation tool set to evaluate the evaluation point. The preset password evaluation tool set includes password evaluation tools corresponding to multiple feature indicators, and the random forest includes multiple randomly established decision trees, where nodes in the decision trees correspond one-to-one to the password evaluation tools, and each decision tree includes some or all of the password evaluation tools from the preset password evaluation tool set.

[0034] Step S108: In the random forest, the password evaluation tools are scheduled according to the decision trees with the largest number of identical numbers, where the identical number refers to the number of decision trees of the same type with the same node distribution.

[0035] In an embodiment of the present invention, according to the evaluation points identified in the application scenario topology diagram of the object to be tested, a key feature set relied on for evaluating the object to be tested based on the evaluation points can be selected from the material library, and then a random forest for calling the password evaluation tool can be automatically constructed based on the key feature set, and the optimal decision tree can be selected from the random forest to schedule the password evaluation tool, so that the scheduling of the password evaluation tool can get rid of the dependence on manual labor, thereby achieving the technical effect of improving the scheduling efficiency of the password evaluation tool, and further solving the technical problem of low scheduling efficiency of the password evaluation tool caused by relying on manual labor for scheduling the password evaluation tool.

[0036] In the above step S102, the object under test includes multiple components, and the object under test relies on multiple components to jointly process the passwords relied upon for encryption or verification. The process of using passwords between the components in the object under test can be represented by an application scenario topology diagram.

[0037] In the above step S102, the evaluation point may instruct the subject to use the password in the link and transmit the password used or generated in the link to the password evaluation tool for evaluation.

[0038] In the above step S104, the material library includes: multiple materials, which can be data generated by the password assessment tool, data uploaded by the user, or data reported by the tested object in a historical application environment.

[0039] In the above step S104, the measured data may be the material relied upon for evaluation based on the evaluation points.

[0040] In the above step S106, Random Forest is an ensemble learning method that improves the accuracy and stability of classification or regression tasks by constructing multiple decision trees and combining the prediction results of these trees.

[0041] In step S106, a decision tree is a tree-structured prediction model that recursively splits a dataset to gradually construct classification or regression rules. Each node represents a test of a feature, each branch represents a value or range for that feature, and each leaf node represents a category or value. The goal of building a decision tree is to maximize the purity or information gain of the child nodes by selecting the optimal split point.

[0042] In the above step S106, the preset password evaluation tool set includes: a plurality of pre-configured password evaluation tools, each password evaluation tool is used to evaluate a corresponding characteristic indicator.

[0043] In the above step S108, the scheduling of the password assessment tool can be achieved by pre-setting a configuration file for each password assessment tool, wherein the configuration file is used to describe the access address of the password assessment tool, and the configuration file is maintained by the scheduling platform.

[0044] Optionally, the scheduling platform is used to at least execute the above steps S102, S104, S106 and S108.

[0045] As an optional embodiment, based on a key feature set, multiple password evaluation tools recorded in a preset password evaluation tool set are used to construct a random forest for evaluating evaluation points, including: performing replacement sampling on multiple feature indicators in the key feature set to obtain multiple sub-sample sets, wherein the number of feature indicators in the sub-sample set is the same as the number of feature indicators in the key feature set; in each sub-sample set, randomly selecting a preset number of feature indicators to construct a decision tree, wherein the preset number of features is the square root of the number of feature indicators in the sub-sample set; and determining the random forest based on the decision trees constructed based on the multiple sub-sample sets.

[0046] In the above-mentioned embodiment of the present application, by performing sampling with replacement on multiple feature indicators in the key feature set, multiple randomly determined sub-sample sets can be obtained, and then a decision tree is constructed based on each sub-sample set, and multiple randomly determined decision trees can be obtained, thereby realizing the generation of a random forest.

[0047] As an optional embodiment, in each sub-sample set, feature indicators of a preset number of features are randomly selected, and constructing a decision tree includes: in each sub-sample set, feature indicators that meet the preset number are randomly selected to obtain a set to be trained; the set to be trained is used as the root node of the decision tree, and the root node is split into two child nodes using a splitting point with the smallest Gini index, wherein the splitting point is used to split the set to be trained into a first training subset and a second training subset, and the child nodes include: a first child node corresponding to the first training subset, and a second child node corresponding to the second training subset; detecting whether the number of feature indicators in the set to be trained corresponding to each child node reaches a preset number threshold, wherein the set to be trained is the first training subset or the second training subset; when the number of feature indicators in the set to be trained corresponding to the child node reaches the preset number threshold, the child node is used as a leaf node of the decision tree to complete the construction of the decision tree.

[0048] In the above embodiment of the present application, the nodes in the decision tree correspond to the characteristic indicators in the set to be trained and also correspond to the password evaluation tool. Therefore, according to each node in the decision tree, the password evaluation tool corresponding to the node can be selected for evaluation based on the set to be trained corresponding to the node. The purer the type of the set to be trained corresponding to each node, the better the evaluation effect. Therefore, the Gini index is used to determine the splitting point of each node, and a node is split into two child nodes. The type of the set to be trained corresponding to each child node can be purer. By recursively splitting each node, multiple child nodes with purer types can be obtained. Then, when the number of characteristic indicators in the set to be trained corresponding to the child node reaches a preset number threshold, it means that the recursive stopping condition is met. Then, the last split child node can be used as a leaf node, and the node before the first split can be used as the root node to complete the construction of the decision tree.

[0049] Optionally, the preset quantity threshold may be 2.

[0050] As an optional embodiment, the method further includes: when the number of feature indicators in the to-be-trained set corresponding to the child node does not reach a preset number threshold, using the child node as a root node.

[0051] In the above embodiment of the present application, when the number of characteristic indicators in the set to be trained corresponding to the child node does not reach the preset number threshold, the child node can be used as the root node of the branch, and further splitting can be performed using the splitting point with the smallest Gini index to obtain a new child node, until the number of characteristic indicators in the set to be trained corresponding to the child node reaches the preset number threshold.

[0052] As an optional embodiment, the set to be trained is used as the root node of the decision tree, and the root node is split into two child nodes using the splitting point with the smallest Gini index, including: determining the feature categories to which multiple feature indicators in the set to be trained belong, wherein the feature indicators of each feature category are evaluated using a password evaluation tool of the corresponding tool category; traversing each feature category in the set to be trained, determining the splitting point corresponding to each feature category, and calculating the Gini index after the set to be trained is divided according to each splitting point; selecting the splitting point with the smallest Gini index, splitting the set to be trained into a first training subset and a second training subset, and obtaining two child nodes of the root node.

[0053] In the above embodiment of the present application, there are feature indicators of multiple feature categories in the set to be trained, and feature indicators of different feature categories need to be evaluated using different password evaluation tools. The purpose of splitting the root node into two child nodes is to classify the set to be trained into a first training subset and a second training subset with purer feature categories. Therefore, the splitting point can be determined based on the feature category to which the feature indicator belongs. The splitting point can be determined by calculating the Gini index. The smaller the Gini index, the purer the training subset split from the set to be trained.

[0054] Optionally, the calculation principle of the Gini index is: Among them, p k It represents the probability of the kth feature category in the set, where K is the total number of feature categories. Gini(D) represents the Gini index of set D. The smaller the Gini index, the purer the set.

[0055] As an optional embodiment, in a random forest, scheduling password evaluation tools according to the decision trees with the same largest number includes: counting the same number of decision trees of each type with the same node distribution in multiple decision trees of the random forest; selecting any one of the decision trees of the same type with the same largest number as the target decision tree; and calling the password evaluation tool corresponding to each node in the target decision tree in sequence from the root node to the leaf node to evaluate the characteristic indicators corresponding to the node.

[0056] In the above embodiment of the present application, among the multiple decision trees in the random forest, there may be decision trees with the same node distribution. Therefore, the same decision trees can be regarded as the same type, and statistics are performed on each decision tree in the random forest to determine the same number of each decision tree, and then the type of decision trees with the largest number of the same number is determined. Since the node distribution of multiple decision trees of the same type is the same, any one of the decision trees with the largest number of the same number can be selected as the target decision tree. The distribution of each node in the target decision tree can represent the calling order of the password evaluation tool corresponding to each node.

[0057] As an optional embodiment, in a random forest, after scheduling the password evaluation tools according to the decision trees with the same largest number, the method also includes: using the scheduled password evaluation tools to evaluate the objects under test according to the evaluation points to obtain the evaluation results corresponding to each password evaluation tool; reporting each evaluation result to the material library; calling the report generation tool to use the evaluation results reported to the material library as task materials to generate an evaluation report.

[0058] In the above embodiment of the present application, the password evaluation tool can upload the evaluation results to the material library after completing the evaluation. Furthermore, when the object being tested needs to use multiple password evaluation tools, the material library can receive evaluation results reported by multiple password evaluation tools respectively, and then use the evaluation report to organize the multiple evaluation results in the material library and generate an evaluation report as the final result.

[0059] Optionally, when the tested object needs to use one password assessment tool and the password assessment tool only reports one assessment result to the material library, the report generation tool can also organize the assessment results and generate an assessment report.

[0060] Optionally, the report generation tool organizes the evaluation results to at least include: filling the evaluation results into the evaluation report according to a preset format.

[0061] The present invention also provides a preferred embodiment, which provides an adaptive scheduling method for a password evaluation tool in a power information system.

[0062] Figure 2 is a schematic diagram of a classification of password assessment tools according to an embodiment of the present invention, such as Figure 2 As shown in Figure 1, password testing tools can be categorized into: algorithm and randomness testing tools, password security protocol testing tools, and password application testing tools. Automating the testing of various password testing tools can improve the efficiency and quality of testing. However, in existing password testing activities, manual data import and export operations are still required between multiple password testing tools. Currently, no adaptive scheduling solutions for password testing tools have been proposed.

[0063] Figure 3 A schematic diagram of an adaptive scheduling scheme for a password assessment tool according to an embodiment of the present invention is shown in FIG. Figure 3 As shown in the figure, in the adaptive scheduling scheme, by performing feature analysis on the application scenarios and test purposes and selecting the appropriate password evaluation tool combination scheme through the random forest decision-making process, the efficiency of password evaluation is improved. At the same time, the data analysis and processing between the password evaluation tools are completed automatically, which saves the evaluators the time and energy of manually collecting, formatting, importing and exporting, and organizing and marking the input and output data of the password evaluation tools during the evaluation process, effectively improving the intelligence level and automation level of the password evaluation tool automatic scheduling platform.

[0064] It should be noted that before the evaluation begins, the evaluator is required to upload a topological diagram of the application scenario being tested. The evaluator can then identify evaluation points within the diagram and adaptively select an evaluation plan using an intelligent matching algorithm. After the evaluation is complete, the password evaluation tool uploads the evaluation data and results to the resource library, which can be used to generate evaluation reports and maintain the application scenario knowledge base.

[0065] It's important to note that password assessment tools need to expose their interface access addresses for other tools to access. This application uses configuration files to describe the access addresses of password assessment tools, which are maintained through the scheduling platform. Each password assessment tool corresponds to a corresponding configuration file. To study the types of external interfaces required by password assessment tools, we categorize password assessment tools into three categories based on their position and function in the scheduling process: collection tools, analysis tools, and correlation tools.

[0066] Optionally, collection tools can be divided into two categories according to the collection method: interactive evaluation tools and monitoring evaluation tools, which collect communication data by interacting with or monitoring the object under test, such as the WireShark tool.

[0067] Optionally, the analysis tool analyzes and verifies the tested data according to the specification standards and finally obtains the evaluation results, such as the digital certificate format compliance detection tool.

[0068] Optionally, the correlation tool collects the evaluation results of multiple password evaluation tools and finally compiles the evaluation report according to the template. Therefore, in addition to the basic data input and output interface requirements, the password evaluation tool also needs to include an interface for receiving scheduling information.

[0069] In order to capture communication data and communicate with the object under test, the collection tool also needs to be configured with information such as the IP address of the object under test; some analysis tools need to be configured with parameter information, such as digital certificate format compliance. In order to verify whether the source of the digital certificate is trustworthy, the password evaluation tool needs to be configured with the parameters of the issued superior certificate, etc.; there are dependencies between some analysis tools, such as the evaluation results of the confidentiality analysis tool need to rely on the evaluation results of the random number analysis tool for randomness verification of the ciphertext, etc. Therefore, in order to meet the interface description requirements of the above three types of evaluation tools, this application proposes a unified interface model for evaluation tools.

[0070] Figure 4 A schematic diagram of a unified interface model of a password assessment tool according to an embodiment of the present invention is shown as follows: Figure 4 As shown, it at least includes: an input interface and an output interface, a service provision interface and a dependent service interface, a task scheduling interface and a personnel operation interface (ie, a human-computer interaction interface).

[0071] Optionally, the input interface is used to receive data to be processed by the password assessment tool. This data may be manually entered by the assessor or generated by other password assessment tools. The output interface is used to send assessment results to the scheduling platform or send processed data to other password assessment tools. Data can be transferred between two password assessment tools via the input and output interfaces.

[0072] Optionally, a service interface is provided to receive service access requests and parameters from other password assessment tools and return the processed results. A dependency service interface is used to send service access requests and parameters to other password assessment tools and obtain return values. Two password assessment tools can call each other using the service interface and dependency service interface, for example, a confidentiality analysis tool calling the randomness verification service provided by a random number analysis tool.

[0073] Optionally, the task scheduling interface is used to receive the evaluation instruction sent by the scheduling platform. The password evaluation tool obtains scheduling information from the evaluation instruction and adjusts the task running status.

[0074] Optionally, a personnel operation interface is used to display the operation interface provided by the password assessment tool. The assessor can configure the required information and operating parameters of the password assessment tool on the operation interface, such as the server IP address for collecting data, the certificate issued by the superior, etc.

[0075] In the above embodiments of this application, each evaluation tool added to the scheduling platform needs to define a specific configuration file according to the tool's unified interface model to facilitate interaction with other password evaluation tools. Each interface type in the configuration file corresponds to a set of data types, representing the data structure information passed to or from this interface.

[0076] It should be noted that each piece of data generated by the password assessment tool or uploaded by the user is called a resource. The scheduling platform can use the resource library to store and manage these resources. If the test data requires manual input, the assessor can upload the test data as a resource to the resource library in advance to store and manage it, making it easier to select when starting the assessment task. If the assessor needs to obtain specific output data from the assessment tool, they can upload the output data to the resource library to save it for later review.

[0077] Optionally, the resource library records historical evaluation processes and expert-based evaluation plans, covering evaluation strategies for different application scenarios. Organizing, categorizing, and analyzing the characteristics of collected data to form a structured evaluation scenario knowledge base is the foundation for implementing intelligent matching algorithms. This knowledge base collects and organizes evaluation data and best practices for different application scenarios, including evaluation tool usage, access points, dependencies, and execution order.

[0078] As an optional example, when the object under test is a platform, key features can be extracted based on the application scenario topology diagram of the platform under test, where the extracted key features include at least: component type, component quantity, component characteristics, component dependencies, data flow path, overall topology structure, etc.

[0079] Optionally, the component types include an application server, a database, an authentication server, an encryption module, etc., and a One-Hot encoding method is used to encode the authentication server as [1,0,0,0], and the application server as [0,1,0,0], etc.

[0080] Optionally, to prevent order of magnitude differences from affecting model training, the number of components is normalized.

[0081] Optionally, component characteristics include operating system, software version, operating environment, etc., and are encoded using One-Hot.

[0082] Optionally, component dependencies are converted to feature vectors using an adjacency matrix.

[0083] Optionally, the data flow path is encoded by a path vector of a node flow identifier.

[0084] Optionally, the application scenario topology graph uses node degree and circle diameter to express the overall topology graph structure.

[0085] Optionally, based on the characteristics of the above application scenario topology diagram, combined with the test objectives and evaluation criteria, a complete application scenario characteristic indicator set B = {B1, B2, ..., B m}, m is the number of indicators, where test objectives include performance testing, security testing, compliance testing, etc.; evaluation standards include ISO / IEC 27001, NIST SP 800-53, etc., all of which use One-Hot encoding.

[0086] As an optional example, based on the records of the material library and the analysis of application scenario characteristics, an application scenario knowledge base is constructed as a training set, and a test solution decision tree is constructed based on the training set. The specific steps include the following:

[0087] Step S21: Initialization: Starting from the root node, the entire training set is used as the sample set of the initial node.

[0088] Step S22: Select the optimal feature and split point. For the sample set of the current node, calculate the splitting criterion for each feature based on the Gini index, select the optimal feature and its corresponding optimal split point, and split the sample set of the current node into two subsets. The optimal split point calculation process is as follows: Among them, p kIt represents the probability of the kth category in the data set, K is the total number of categories, and Gini(D) represents the Gini index of the data set D. The smaller the Gini index, the purer the data set.

[0089] Step S23: Create child nodes. Split the current node into two child nodes based on the optimal feature and the split point, with each child node corresponding to a subset.

[0090] Step S24, recursive splitting.

[0091] For each child node, repeat steps S22 and S23 until the minimum number of leaf nodes is 2, which satisfies the stopping condition, thus avoiding the generation of leaf nodes that are too small and improving the generalization ability of the model.

[0092] Step S25: Generate a leaf node. When the stopping condition is met, the current node is set as a leaf node and assigned a category label, which is the corresponding optimal test solution.

[0093] It should be noted that the core idea of ​​random forest is to construct a set of diverse decision trees by introducing randomness, and by integrating the prediction results of these trees, it can reduce the overfitting problem of a single decision tree and improve the generalization ability of the model. The basic construction steps are as follows:

[0094] Step S31: Bootstrap sampling. Sampling with replacement is performed from the original training set. The sampling ratio is 100%. However, since it is sampling with replacement, the generated sub-sample sets are subsets of the original training set. Each sub-sample set is used to train a decision tree.

[0095] Step S32, construct a decision tree. When constructing each decision tree, randomly select The features are split to increase the differences between trees.

[0096] Step S33: Integrate the decision tree and decide the final matching evaluation plan by majority voting, including the evaluation tools used, the order of tool scheduling, etc.

[0097] Figure 5 A schematic diagram of a generated evaluation strategy solution according to an embodiment of the present invention is shown as follows: Figure 5As shown, the platform under test captures data packets through a protocol capture tool, and the protocol analysis tool analyzes the captured data packets to obtain certificate data and signature data. The certificate data is then evaluated using a digital certificate format compliance evaluation tool, and the generated evaluation results are sent to the material library; the signature data is evaluated using a digital signature function evaluation tool, and the generated evaluation results are sent to the material library. The simulation system tool can call a service through a digital signature verification evaluation tool to generate a simulated signature, and send the simulated signature to the platform under test, and then receive the processed signature data returned by the platform under test. The simulation system tool can then verify the signature data returned by the platform under test and send the verification results to the digital signature verification evaluation tool, which will then evaluate the verification results and send the results to the material library. The report generation tool can extract the evaluation results returned by the digital signature verification evaluation tool, the evaluation results of the digital certificate format compliance evaluation tool, and the evaluation results of the digital signature function evaluation tool from the material library to generate an evaluation report, and then send the evaluation report to the material library for management.

[0098] Optionally, the present application designs a unified interface model to provide a standardized description of various password assessment tools. This model can be used to define the input, output, and operation methods of various password assessment tools, so that different password assessment tools can be called and managed through a unified interface.

[0099] Optionally, this application uses a unified description of the evaluation tools, which can provide detailed descriptions and definitions of all evaluation tools based on a unified interface model. In this way, the differences between different password evaluation tools can be eliminated, allowing them to work together on the same platform, facilitating centralized management and scheduling.

[0100] Optionally, this application establishes an adaptive scheduling strategy matching algorithm, which can effectively improve the intelligence and automation level of the password assessment tool automated scheduling platform. By building an evaluation scenario knowledge base, proposing key features, and introducing an intelligent matching algorithm, intelligent scheduling and optimization of password assessment tools are achieved.

[0101] The above-mentioned embodiments of the present application significantly reduce the time spent by evaluators on designing evaluation plans and processing data by adaptively generating evaluation strategies; the automated processing of evaluation strategies not only saves time, but also reduces the workload of evaluators; the automated method can ensure the accuracy and consistency of data processing, effectively reduce the deviation of evaluation results caused by human errors, and avoid errors introduced by manual data processing; through automated processing, the data processing flow of the evaluation tool is standardized and unified, which is convenient for centralized management and maintenance, and improves the efficiency and reliability of the entire evaluation process; on the basis of saving time and avoiding errors, the overall efficiency of the evaluation work is improved, making the evaluation results more timely and reliable, and facilitating quick decision-making and adjustments; because the automated system reduces human intervention, it ensures the stability and reliability of evaluation data processing, making the output results of the evaluation tool more credible.

[0102] According to an embodiment of the present invention, an embodiment of a scheduling device of a password evaluation tool is also provided. It should be noted that the scheduling device of the password evaluation tool can be used to execute the scheduling method of the password evaluation tool in the embodiment of the present invention, and the scheduling method of the password evaluation tool in the embodiment of the present invention can be executed in the scheduling device of the password evaluation tool.

[0103] Figure 6 is a schematic diagram of a scheduling device of a password assessment tool according to an embodiment of the present invention, such as Figure 6 As shown, the device may include: an acquisition module 62, for acquiring an application scenario topology map of the object under test, wherein the application scenario topology map at least includes: the connection relationship between the components in the object under test, and the evaluation points pre-identified in the application scenario topology map; a selection module 64, for selecting a key feature set for evaluating the evaluation points from a material library based on the evaluation points in the application scenario topology map, wherein the material library at least includes: the test data uploaded by the object under test, and the key feature set includes: a plurality of feature indicators determined based on the test data; a construction module 66, for selecting a key feature set based on the key feature set using a preset password Multiple password evaluation tools in the evaluation tool set are used to construct a random forest for evaluating evaluation points, wherein the preset password evaluation tool set includes: password evaluation tools corresponding to multiple characteristic indicators, the random forest includes: multiple randomly established decision trees, the nodes in the decision trees correspond one-to-one to the password evaluation tools, and each decision tree includes: some or all of the password evaluation tools in the preset password evaluation tool set; a scheduling module 68 is used to schedule the password evaluation tools in the random forest according to the decision trees with the largest number of the same number, wherein the same number is the number of decision trees of the same type with the same node distribution.

[0104] It should be noted that the acquisition module 62 in this embodiment can be used to execute step S102 in the embodiment of the present application, the selection module 64 in this embodiment can be used to execute step S104 in the embodiment of the present application, the construction module 66 in this embodiment can be used to execute step S106 in the embodiment of the present application, and the scheduling module 68 in this embodiment can be used to execute step S108 in the embodiment of the present application. The examples and application scenarios implemented by the above modules and corresponding steps are the same, but are not limited to the contents disclosed in the above embodiments.

[0105] In an embodiment of the present invention, according to the evaluation points identified in the application scenario topology diagram of the object to be tested, a key feature set relied on for evaluating the object to be tested based on the evaluation points can be selected from the material library, and then a random forest for calling the password evaluation tool can be automatically constructed based on the key feature set, and the optimal decision tree can be selected from the random forest to schedule the password evaluation tool, so that the scheduling of the password evaluation tool can get rid of the dependence on manual labor, thereby achieving the technical effect of improving the scheduling efficiency of the password evaluation tool, and further solving the technical problem of low scheduling efficiency of the password evaluation tool caused by relying on manual labor for scheduling the password evaluation tool.

[0106] As an optional embodiment, the construction module includes: a sampling unit, used to perform replacement sampling on multiple feature indicators in the key feature set to obtain multiple sub-sample sets, wherein the number of feature indicators in the sub-sample sets is the same as the number of feature indicators in the key feature set; a construction unit, used to randomly select a preset number of feature indicators in each sub-sample set to construct a decision tree, wherein the preset number of features is the square root of the number of feature indicators in the sub-sample set; and a determination unit, used to determine a random forest based on the decision trees constructed based on multiple sub-sample sets.

[0107] As an optional embodiment, the construction unit includes: a selection subunit, used to randomly select feature indicators that meet a preset number in each sub-sample set to obtain a set to be trained; a splitting subunit, used to use the set to be trained as the root node of the decision tree, and use the splitting point with the smallest Gini index to split the root node into two child nodes, wherein the splitting point is used to split the set to be trained into a first training subset and a second training subset, and the child nodes include: a first child node corresponding to the first training subset, and a second child node corresponding to the second training subset; a detection subunit, used to detect whether the number of feature indicators in the set to be trained corresponding to each child node reaches a preset number threshold, wherein the set to be trained is the first training subset or the second training subset; a first determination subunit, used to use the child node as a leaf node of the decision tree when the number of feature indicators in the set to be trained corresponding to the child node reaches a preset number threshold, thereby completing the construction of the decision tree.

[0108] As an optional embodiment, the device further includes: a second determining subunit, configured to use the child node as a root node when the number of characteristic indicators in the to-be-trained set corresponding to the child node does not reach a preset number threshold.

[0109] As an optional embodiment, the splitting subunit includes: a third determination subunit, used to determine the feature categories to which multiple feature indicators in the set to be trained belong, wherein the feature indicators of each feature category are evaluated using a password evaluation tool of the corresponding tool category; a traversal subunit, used to traverse each feature category in the set to be trained, determine the splitting point corresponding to each feature category, and calculate the Gini index after the set to be trained is divided according to each splitting point; a selection subunit, used to select the splitting point with the smallest Gini index, split the set to be trained into a first training subset and a second training subset, and obtain two child nodes of the root node.

[0110] As an optional embodiment, the scheduling module includes: a statistical unit, used to count the same number of each decision tree with the same node distribution in multiple decision trees of the random forest; a selection unit, used to select any one of the decision trees of the same type with the largest number of the same number as the target decision tree; and a calling unit, which calls the password evaluation tool corresponding to each node in the target decision tree in order from the root node to the leaf node to evaluate the characteristic indicators corresponding to the node.

[0111] As an optional embodiment, the device also includes: an evaluation sub-module, which is used to schedule the password evaluation tools in the random forest according to the decision trees with the same largest number, and then use the scheduled password evaluation tools to evaluate the objects under test according to the evaluation points to obtain the evaluation results corresponding to each password evaluation tool; a reporting sub-module, which is used to report each evaluation result to the material library; and a generation sub-module, which is used to call the report generation tool to use the evaluation results reported to the material library as task materials to generate an evaluation report.

[0112] An embodiment of the present invention may provide an electronic device, which may be a computer terminal, and the computer terminal may be any computer terminal device in a computer terminal group. Optionally, in this embodiment, the computer terminal may also be replaced by a terminal device such as a mobile terminal.

[0113] Optionally, in this embodiment, the computer terminal may be located in at least one network device among a plurality of network devices of a computer network.

[0114] In this embodiment, the computer terminal can execute the program code of the following steps in the scheduling method of the password evaluation tool: obtaining an application scenario topology map of the object under test, wherein the application scenario topology map at least includes: the connection relationship between the components in the object under test, and the evaluation points pre-identified in the application scenario topology map; according to the evaluation points in the application scenario topology map, selecting a key feature set for evaluating the evaluation points from a material library, wherein the material library at least includes: the test data uploaded by the object under test, and the key feature set includes: a plurality of feature indicators determined based on the test data; according to the key feature set, A random forest for evaluating evaluation points is constructed using multiple password evaluation tools from a preset password evaluation tool set, wherein the preset password evaluation tool set includes: password evaluation tools corresponding to multiple characteristic indicators, and the random forest includes: multiple randomly established decision trees, wherein the nodes in the decision trees correspond one-to-one to the password evaluation tools, and each decision tree includes: some or all of the password evaluation tools in the preset password evaluation tool set; in the random forest, the password evaluation tools are scheduled according to the decision trees with the largest number of the same number, wherein the same number refers to the number of decision trees of the same type with the same node distribution.

[0115] Figure 7 is a structural block diagram of a computer terminal according to an embodiment of the present invention, such as Figure 7 As shown, the computer terminal 70 may include: one or more (only one is shown in the figure) processors 72 and a memory 74.

[0116] The memory can be used to store software programs and modules, such as the program instructions / modules corresponding to the scheduling method and device of the password assessment tool in the embodiments of the present invention. The processor executes the software programs and modules stored in the memory to perform various functional applications and data processing, thereby implementing the scheduling method of the password assessment tool described above. The memory can include high-speed random access memory and can also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory can further include memory remotely located relative to the processor, and these remote memories can be connected to the terminal 70 via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0117] The processor can call information and applications stored in the memory through a transmission device to perform the following steps: obtaining an application scenario topology map of the object under test, wherein the application scenario topology map at least includes: connection relationships between components in the object under test, and evaluation points pre-identified in the application scenario topology map; selecting a key feature set for evaluating the evaluation points from a material library based on the evaluation points in the application scenario topology map, wherein the material library at least includes: test data uploaded by the object under test, and the key feature set includes: multiple feature indicators determined based on the test data; based on the key feature set, using multiple password evaluation tools in a preset password evaluation tool set, constructing a random forest for evaluating the evaluation points, wherein the preset password evaluation tool set includes: password evaluation tools corresponding to multiple feature indicators, respectively, the random forest includes: multiple randomly established decision trees, nodes in the decision trees correspond one-to-one to the password evaluation tools, and each decision tree includes: some or all of the password evaluation tools in the preset password evaluation tool set; in the random forest, scheduling the password evaluation tools according to the decision trees with the largest number of the same number, wherein the same number refers to the number of decision trees of the same type with the same node distribution.

[0118] Optionally, the processor may also execute the program code of the following steps: performing sampling with replacement on multiple feature indicators in the key feature set to obtain multiple sub-sample sets, wherein the number of feature indicators in the sub-sample sets is the same as the number of feature indicators in the key feature set; in each sub-sample set, randomly selecting a preset number of feature indicators to construct a decision tree, wherein the preset number of features is the square root of the number of feature indicators in the sub-sample set; and determining a random forest based on the decision trees constructed based on the multiple sub-sample sets.

[0119] Optionally, the processor may also execute the following program code: in each subsample set, randomly select characteristic indicators that meet a preset number to obtain a set to be trained; use the set to be trained as the root node of the decision tree, and use the splitting point with the smallest Gini index to split the root node into two child nodes, wherein the splitting point is used to split the set to be trained into a first training subset and a second training subset, and the child nodes include: a first child node corresponding to the first training subset, and a second child node corresponding to the second training subset; detect whether the number of characteristic indicators in the set to be trained corresponding to each child node reaches a preset number threshold, wherein the set to be trained is the first training subset or the second training subset; when the number of characteristic indicators in the set to be trained corresponding to the child node reaches the preset number threshold, use the child node as a leaf node of the decision tree to complete the construction of the decision tree.

[0120] Optionally, the processor may further execute the program code of the following steps: when the number of feature indicators in the to-be-trained set corresponding to the child node does not reach a preset number threshold, use the child node as a root node.

[0121] Optionally, the processor may also execute the program code of the following steps: determining the feature categories to which multiple feature indicators in the set to be trained belong, wherein the feature indicators of each feature category are evaluated using a password evaluation tool of the corresponding tool category; traversing each feature category in the set to be trained, determining the splitting point corresponding to each feature category, and calculating the Gini index after the set to be trained is divided according to each splitting point; selecting the splitting point with the smallest Gini index, splitting the set to be trained into a first training subset and a second training subset, and obtaining two child nodes of the root node.

[0122] Optionally, the processor may also execute the program code of the following steps: among multiple decision trees in a random forest, counting the same number of each decision tree with the same node distribution; among the decision trees of the same type with the largest number of the same number, selecting any one of the decision trees as the target decision tree; and calling the password evaluation tool corresponding to each node in the target decision tree in sequence from the root node to the leaf node to evaluate the characteristic indicators corresponding to the node.

[0123] Optionally, the above-mentioned processor can also execute the program code of the following steps: use the scheduled password evaluation tool to evaluate the object under test according to the evaluation points, and obtain the evaluation results corresponding to each password evaluation tool; report each evaluation result to the material library; call the report generation tool to use the evaluation results reported to the material library as task materials to generate an evaluation report.

[0124] In an embodiment of the present invention, according to the evaluation points identified in the application scenario topology diagram of the object to be tested, a key feature set relied on for evaluating the object to be tested based on the evaluation points can be selected from the material library, and then a random forest for calling the password evaluation tool can be automatically constructed based on the key feature set, and the optimal decision tree can be selected from the random forest to schedule the password evaluation tool, so that the scheduling of the password evaluation tool can get rid of the dependence on manual labor, thereby achieving the technical effect of improving the scheduling efficiency of the password evaluation tool, and further solving the technical problem of low scheduling efficiency of the password evaluation tool caused by relying on manual labor for scheduling the password evaluation tool.

[0125] It can be understood by those skilled in the art that Figure 7 The structure shown is for illustration only, and the computer terminal may also be a smart phone (such as an Android phone, an iOS phone, etc.), a tablet computer, a handheld computer, a mobile Internet device (MID), a PAD, or other terminal devices. Figure 7 It does not limit the structure of the above electronic device. For example, the computer terminal 70 may also include Figure 7 More or fewer components (such as network interfaces, display devices, etc.) shown in, or with Figure 7 Different configurations shown.

[0126] A person skilled in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing the hardware related to the terminal device through a computer program. The computer program can be stored in a non-volatile medium. The non-volatile storage medium may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0127] The embodiment of the present invention further provides a non-volatile storage medium. Optionally, in this embodiment, the non-volatile storage medium can be used to store the program code executed by the scheduling method of the password assessment tool provided in the embodiment.

[0128] Optionally, in this embodiment, the non-volatile storage medium may be located in any computer terminal in a computer terminal group in a computer network, or in any mobile terminal in a mobile terminal group.

[0129] Optionally, in this embodiment, the non-volatile storage medium is configured to store program codes for executing the following steps: obtaining an application scenario topology diagram of the object under test, wherein the application scenario topology diagram at least includes: the connection relationship between the components in the object under test, and the evaluation points pre-identified in the application scenario topology diagram; selecting a key feature set for evaluating the evaluation points from a material library according to the evaluation points in the application scenario topology diagram, wherein the material library at least includes: the test data uploaded by the object under test, the key feature set includes: a plurality of feature indicators determined based on the test data; based on the key feature set, A random forest for evaluating evaluation points is constructed using multiple password evaluation tools from a preset password evaluation tool set, wherein the preset password evaluation tool set includes: password evaluation tools corresponding to multiple characteristic indicators, and the random forest includes: multiple randomly established decision trees, and the nodes in the decision trees correspond one-to-one to the password evaluation tools, and each decision tree includes: some or all of the password evaluation tools in the preset password evaluation tool set; in the random forest, the password evaluation tools are scheduled according to the decision trees with the largest number of the same number, wherein the same number refers to the number of decision trees of the same type with the same node distribution.

[0130] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for executing the following steps: performing sampling with replacement on multiple feature indicators in the key feature set to obtain multiple sub-sample sets, wherein the number of feature indicators in the sub-sample sets is the same as the number of feature indicators in the key feature set; in each sub-sample set, randomly selecting feature indicators with a preset number of features to construct a decision tree, wherein the preset number of features is the square root of the number of feature indicators in the sub-sample set; and determining a random forest based on the decision trees constructed based on the multiple sub-sample sets.

[0131] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for executing the following steps: in each sub-sample set, randomly selecting feature indicators that meet a preset number to obtain a set to be trained; using the set to be trained as the root node of the decision tree, and using the splitting point with the smallest Gini index to split the root node into two child nodes, wherein the splitting point is used to split the set to be trained into a first training subset and a second training subset, and the child nodes include: a first child node corresponding to the first training subset, and a second child node corresponding to the second training subset; detecting whether the number of feature indicators in the set to be trained corresponding to each child node reaches a preset number threshold, wherein the set to be trained is the first training subset or the second training subset; when the number of feature indicators in the set to be trained corresponding to the child node reaches the preset number threshold, using the child node as a leaf node of the decision tree to complete the construction of the decision tree.

[0132] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for executing the following steps: when the number of feature indicators in the to-be-trained set corresponding to the child node does not reach a preset number threshold, use the child node as a root node.

[0133] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for executing the following steps: determining the feature categories to which multiple feature indicators in the set to be trained belong, wherein the feature indicators of each feature category are evaluated using a password evaluation tool of the corresponding tool category; traversing each feature category in the set to be trained, determining the splitting point corresponding to each feature category, and calculating the Gini index after the set to be trained is divided according to each splitting point; selecting the splitting point with the smallest Gini index, splitting the set to be trained into a first training subset and a second training subset, and obtaining two child nodes of the root node.

[0134] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for executing the following steps: among multiple decision trees in a random forest, counting the same number of each decision tree with the same node distribution; among the decision trees of the same type with the largest number, selecting any one of them as the target decision tree; and calling the password evaluation tool corresponding to each node in the target decision tree in sequence from the root node to the leaf node to evaluate the characteristic indicators corresponding to the node.

[0135] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for executing the following steps: using the scheduled password evaluation tool to evaluate the object under test according to the evaluation points, and obtaining the evaluation results corresponding to each password evaluation tool; reporting each evaluation result to the material library; calling the report generation tool to use the evaluation results reported to the material library as task materials to generate an evaluation report.

[0136] An embodiment of the present invention further provides a computer program product, including a computer program. Optionally, in this embodiment, when the computer program is executed by a processor, the steps of the scheduling method of the password assessment tool provided in the above embodiment are implemented.

[0137] The serial numbers of the above embodiments of the present invention are for description only and do not represent the advantages or disadvantages of the embodiments.

[0138] In the above embodiments of the present invention, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0139] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only exemplary. For example, the division of the units can be a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0140] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0141] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0142] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a non-volatile storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a non-volatile storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned non-volatile storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program code.

[0143] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A method for scheduling a password assessment tool, characterized in that: include: Obtaining an application scenario topology diagram of the object under test, wherein the application scenario topology diagram at least includes: connection relationships between components in the object under test, and evaluation points pre-marked in the application scenario topology diagram; According to the evaluation point in the application scenario topology diagram, a key feature set for evaluating the evaluation point is selected from a material library, wherein the material library at least includes: the measured data uploaded by the measured object, and the key feature set includes: a plurality of feature indicators determined based on the measured data; Based on the key feature set, a random forest is constructed using multiple password evaluation tools from a preset password evaluation tool set to evaluate the evaluation point, wherein the preset password evaluation tool set includes: the password evaluation tools corresponding to the multiple feature indicators, and the random forest includes: a plurality of randomly established decision trees, wherein the nodes in the decision trees correspond one-to-one to the password evaluation tools, and each decision tree includes: some or all of the password evaluation tools in the preset password evaluation tool set; In the random forest, the password evaluation tool is scheduled according to the decision trees with the largest number of the same number, wherein the same number is the number of the same decision trees with the same node distribution.

2. The method according to claim 1, characterized in that Based on the key feature set, using multiple password evaluation tools recorded in a preset password evaluation tool set, constructing a random forest for evaluating the evaluation points includes: Performing sampling with replacement on the plurality of feature indicators in the key feature set to obtain a plurality of subsample sets, wherein the number of the feature indicators in the subsample sets is the same as the number of the feature indicators in the key feature set; In each of the subsample sets, randomly selecting a preset number of feature indicators to construct the decision tree, wherein the preset number of features is the square root of the number of feature indicators in the subsample set; The random forest is determined according to the decision trees constructed based on the plurality of subsample sets.

3. The method according to claim 2, characterized in that Randomly selecting a preset number of feature indicators in each subsample set, and constructing the decision tree includes: In each of the subsample sets, randomly selecting the feature indicators that meet a preset number to obtain a set to be trained; The to-be-trained set is used as the root node of the decision tree, and the root node is split into two child nodes using a splitting point with a minimum Gini index, wherein the splitting point is used to split the to-be-trained set into a first training subset and a second training subset, and the child nodes include: a first child node corresponding to the first training subset, and a second child node corresponding to the second training subset; Detecting whether the number of the characteristic indicators in the to-be-trained set corresponding to each of the sub-nodes reaches a preset threshold, wherein the to-be-trained set is the first training subset or the second training subset; When the number of the characteristic indicators in the to-be-trained set corresponding to the child node reaches the preset number threshold, the child node is used as a leaf node of the decision tree to complete the construction of the decision tree.

4. The method according to claim 3, characterized in that The method further comprises: When the number of the characteristic indicators in the to-be-trained set corresponding to the child node does not reach the preset number threshold, the child node is used as the root node.

5. The method according to claim 3, characterized in that Taking the to-be-trained set as the root node of the decision tree and splitting the root node into two child nodes using the splitting point with the smallest Gini index includes: Determining the feature categories to which the plurality of feature indicators in the to-be-trained set belong, wherein the feature indicators of each feature category are evaluated using the password evaluation tool of the corresponding tool category; Traversing each of the feature categories in the to-be-trained set, determining a splitting point corresponding to each of the feature categories, and calculating a Gini index after segmenting the to-be-trained set according to each of the splitting points; The split point with the smallest Gini index is selected, and the to-be-trained set is split into a first training subset and a second training subset, to obtain the two child nodes of the root node.

6. The method according to claim 1, characterized in that Scheduling the password assessment tool according to the decision trees having the largest number of the same number in the random forest includes: Among the multiple decision trees in the random forest, counting the same number of each type of decision trees with the same node distribution; Among the decision trees of the same type with the largest number of the same number, select any one of the decision trees as the target decision tree; In order from the root node to the leaf node, the password evaluation tool corresponding to each node in the target decision tree is called in turn to evaluate the characteristic indicator corresponding to the node.

7. The method according to claim 1, characterized in that After scheduling the password assessment tools according to the decision trees with the largest number of the same number in the random forest, the method further includes: Using the scheduled password evaluation tool, evaluating the tested object according to the evaluation points, and obtaining the evaluation result corresponding to each password evaluation tool; Reporting each of the evaluation results to the material library; The report generation tool is called to use the evaluation results reported to the material library as task materials to generate an evaluation report.

8. A scheduling device for a password assessment tool, characterized in that: include: An acquisition module is used to obtain an application scenario topology map of the object under test, wherein the application scenario topology map at least includes: a connection relationship between components in the object under test, and evaluation points pre-marked in the application scenario topology map; A selection module is configured to select a key feature set for evaluating the evaluation point from a material library according to the evaluation point in the application scenario topology diagram, wherein the material library at least includes: the measured data uploaded by the measured object, and the key feature set includes: a plurality of feature indicators determined based on the measured data; a construction module, configured to construct a random forest for evaluating the evaluation point based on the key feature set using multiple password evaluation tools in a preset password evaluation tool set, wherein the preset password evaluation tool set includes: the password evaluation tools corresponding to the multiple feature indicators, the random forest includes: a plurality of randomly established decision trees, the nodes in the decision trees correspond one-to-one to the password evaluation tools, and each decision tree includes: some or all of the password evaluation tools in the preset password evaluation tool set; A scheduling module is used to schedule the password evaluation tool in the random forest according to the decision trees with the largest number of the same number, wherein the same number is the number of the same decision trees with the same node distribution.

9. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to execute the scheduling method of the password assessment tool according to any one of claims 1 to 7 through the computer program.

10. A computer program product comprising computer instructions, characterized in that When the computer instructions are executed by a processor, the steps of the scheduling method of the password assessment tool described in any one of claims 1 to 7 are implemented.

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