Network security vulnerability test training scene generation system and method

Abstract

The invention discloses a network security vulnerability test training scene generation system and method, and relates to the field of network security and training teaching, and the system comprises a dynamic environment generation module, a flow collection and behavior perception module, an AI intelligent evaluation engine, an intelligent tutoring module, and a central control and management platform. Dynamic evolution of a practical training environment is realized through a knowledge graph and reinforcement learning, a complete behavior sequence is constructed through multi-source data fusion, an AI engine evaluates student operation from multi-dimensional fine granularity, an intelligent tutoring module provides real-time prompt, a redisk report and a personalized learning path, and a central platform realizes global management and control. The problems that an existing system is static, evaluation is one-sided and personalized tutoring is lacked are solved, the practical training combat performance and the teaching efficiency are improved, and the labor cost is reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of network security technology and practical teaching, and particularly relates to a network security vulnerability test practical training scene generation system and method. BACKGROUND

[0002] The network security vulnerability test practical training system is a core tool for cultivating and evaluating network security attack and defense talents, and its purpose is to provide a simulated environment for students to conduct vulnerability mining, penetration testing and defense drills close to actual combat. However, the existing practical training systems on the market have obvious limitations, resulting in a large gap between the training effect and the real network attack and defense scene. For example, most systems rely on pre-set static vulnerability scenes. The practical training environment is usually manually built by administrators and contains fixed vulnerability types and attack paths. Students often follow standardized problem-solving steps to complete the drills, and cannot simulate the complex characteristics of the target system dynamically adjusting the defense strategy and the attack path adapting to changes in the real network. In addition, the intelligence level of the system is insufficient, and the construction of new scenes, scoring and analysis after the drills are highly dependent on the experience and manual operation of security administrators, which is low in efficiency and difficult to scale. SUMMARY

[0003] The technical problem to be solved by the present application is to provide a network security vulnerability test practical training scene generation system and method to solve the above problems in the prior art, to realize the dynamic generation and evolution of the practical training environment, the deep behavior analysis of the student operation, and the intelligent real-time guidance and evaluation, thereby significantly improving the practicality of the training and the teaching effect.

[0004] The technical solution of the present application to solve the above technical problems is: a network security vulnerability test practical training scene generation system, comprising a dynamic environment generation module, a traffic collection and behavior perception module, an AI intelligent evaluation engine, an intelligent tutoring module, and a central control and management platform, which realize the dynamic generation of the practical training scene, the comprehensive perception of the student behavior, the intelligent evaluation and personalized tutoring through the collaborative operation of the modules, wherein: The dynamic environment generation module comprises a scene script generator, a basic framework layer and a reinforcement learning intelligent agent, the scene script generator is based on knowledge graph technology, extracts associated elements of vulnerabilities, services and defense measures according to the training target set by an administrator, generates an initial scene script containing multiple hosts and multiple vulnerabilities, the basic framework layer adopts containerization technology, virtualization technology and software-defined network technology, receives the initial scene script and quickly deploys to form a running training environment, realizes flexible customization and real-time adjustment of network topology, can simulate complex scenarios of enterprise intranet and cloud network, and the reinforcement learning intelligent agent learns by interacting with the running training environment, receives environment state feedback and reward signals generated by student operations, evolves actions based on learning strategies, including activating WAF, opening new ports, strengthening defense or providing clues, and realizes real-time adaptive adjustment of the training environment; The traffic collection and behavior perception module comprises a multi-source data collector and a data fusion and association engine, the multi-source data collector collects original data in the training process from three aspects of network traffic, host logs and operation commands; The AI intelligent evaluation engine comprises an attack chain semantic analysis unit, a multi-dimensional behavior portrait unit and a vulnerability exploitation depth analysis unit, the three units run in parallel to realize deep analysis and quantitative scoring of student operations; The intelligent tutoring module comprises a real-time prompting engine, a review report generator and a learning path recommender, and provides personalized teaching support based on the evaluation results of the AI intelligent evaluation engine; The central control and management platform comprises a visual control interface, a resource and user management unit and an AI model management interface, and provides unified control functions for administrators.

[0005] The present application further limits the technical solutions: Preferably, the data fusion and association engine cleans, standardizes and synchronizes the time stamp of the multi-source original data, associates isolated events into complete behavior sequences with students as the main line through session reorganization and behavior chain reconstruction technology, and generates clear structure standardized behavior data flow.

[0006] Preferably, the attack chain semantic analysis unit adopts a natural language processing (NLP) model to perform word embedding and semantic understanding on student operation commands and attack payloads, and identify semantic equivalence of attack chain stages and operation intentions: The multi-dimensional behavior portrait unit adopts a machine learning model to establish a behavior portrait for students from the dimensions of efficiency, concealment, standardization and innovation, analyze the number of steps and time to achieve key goals, evaluate operations such as log cleaning and encrypted channel use, and detect non-standard creative attack methods; The exploit depth analysis unit adopts a graph neural network (GNN), models the exploit process as a graph structure, and evaluates the understanding degree and application flexibility of the student on the principle of the vulnerability by analyzing the structure and path of the graph. Preferably, the real-time prompting engine is built-in with a rule engine and a knowledge graph, and when it is detected that the student meets the preset "dilemma" rule, the relevant content is retrieved from the knowledge graph to push the prompts in the form of heuristic questions or keywords. The review report generator adopts a templating and natural language generation (NLG) technology to convert the multi-dimensional evaluation results into a human-readable report containing scores, operation backtracking, root cause analysis and knowledge point hyperlinks. The learning path recommender constructs a dynamic skill model based on the historical evaluation data of the student, matches the curriculum resource library through collaborative filtering and content recommendation algorithms, and recommends personalized theoretical courses or practical training projects.

[0007] Preferably, the visual management interface displays the real-time topology and state monitoring data of the training environment in a Web graphical manner, and provides one-key environment deployment, pause, snapshot and recovery functions. The resource and user management unit is responsible for managing the permissions of student and teacher accounts, monitoring and managing the allocation and use of underlying computing, storage and network resources. The AI model management interface supports administrators to upload vulnerability data and attack scripts, configure and update AI evaluation model parameters, and realize continuous evolution of the system.

[0008] The present application provides a network security vulnerability test training scene generation method, comprising the following steps: (1) Dynamic environment generation: the administrator sets the training target, the scene script generator generates the initial scene script based on the knowledge graph technology, the basic architecture layer deploys the initial training environment through containerization, virtualization and SDN technology, and the reinforcement learning agent executes evolution actions to realize real-time adaptive adjustment of the environment according to the environment state feedback and student operation reward signals; (2) Traffic collection and behavior perception: the multi-source data collector comprehensively captures network traffic, host logs and operation command raw data, and the data fusion and association engine processes the raw data to generate standardized behavior data streams; (3) AI intelligent evaluation: the AI intelligent evaluation engine realizes fine-grained evaluation and quantitative scoring of student operations through attack link semantic analysis, multi-dimensional behavior profiling and exploit depth analysis; (4) Intelligent tutoring: the intelligent tutoring module pushes heuristic prompts through the real-time prompting engine, generates detailed review reports through the review report generator, and recommends personalized learning paths through the learning path recommender; (5) System control: the administrator manages users, monitors the state of the practical training environment, and configures AI model parameters through the central control and management platform to realize global control of the system.

[0009] The beneficial effects of the present application are: (1) The present application generates a dynamic environment through reinforcement learning, so that each practical training scene is no longer a fixed static template, but the practical training environment can adjust the difficulty and evolution path in real time according to the operation level of the student, simulate the dynamic confrontation characteristics in real network attack and defense, greatly train the on-the-spot response and complex problem solving ability of the student, and improve the practicality and challenge of the training; (2) The AI intelligent evaluation engine uses multi-modal data analysis and NLP, graph neural network and other artificial intelligence technologies to realize fine-grained and multi-dimensional analysis of the student's operation process, not only can judge right and wrong, but also can evaluate operation efficiency, concealment, standardization and technical understanding depth, provide accurate ability portrait, and provide deep analysis such as attack link stage identification and vulnerability exploitation flexibility evaluation, clearly understand the root cause of the skill short board of the student, and get rid of the low efficiency and one-sided problem of traditional dependence on artificial evaluation; (3) The present application system can not only evaluate, but also guide, wherein the intelligent guidance module plays the role of "AI coach", provides real-time heuristic prompts and detailed automated review reports, and recommends personalized learning paths according to the student's short board, provides customized teaching feedback and growth path for each student, and greatly improves the teaching efficiency.

[0010] (4) The environment deployment, evaluation and guidance process of the whole system of the present application is highly automated, which greatly reduces the burden of teachers, so that teachers can be liberated from tedious affairs, and more energy can be invested in course design, technical research and key problem guidance, saving a lot of human cost of teachers. BRIEF DESCRIPTION OF DRAWINGS

[0011] Figure 1 is a system architecture schematic diagram of the present application; Figure 2 is a dynamic environment generation module workflow diagram; Figure 3 is a traffic collection and behavior perception module workflow diagram; Figure 4 is an AI intelligent evaluation engine workflow diagram; Figure 5 is an intelligent guidance module workflow; Figure 6 is a central control and management platform architecture schematic diagram; Figure 7 is a dynamic environment generation module running result schematic diagram; Figure 8This is a schematic diagram showing the operating results of the traffic acquisition and behavior perception module. Figure 9 This is a schematic diagram illustrating the results of the AI-powered intelligent evaluation engine. Figure 10 This is a schematic diagram showing the running results of the intelligent tutoring module; Figure 11 A schematic diagram of the operation results of the central control and management platform. Detailed Implementation Example

[0012] like Figures 1-6 As shown, this embodiment provides a network security vulnerability testing training scenario generation system, including a dynamic environment generation module, a traffic acquisition and behavior perception module, an AI intelligent evaluation engine, an intelligent tutoring module, and a central control and management platform. Through the collaborative operation of these modules, the system achieves dynamic generation of training scenarios, comprehensive perception of student behavior, intelligent evaluation, and personalized tutoring. The dynamic environment generation module includes a scenario script generator, an infrastructure layer, and a reinforcement learning agent. The scenario script generator, based on knowledge graph technology, automatically extracts related elements of vulnerabilities, services, and defense measures according to the training objectives set by the administrator, generating an initial scenario script containing multiple hosts and various vulnerabilities. The infrastructure layer adopts containerization, virtualization and software-defined networking technologies. It receives the initial scenario script and quickly deploys it to form a running training environment, realizing flexible customization and real-time adjustment of network topology, and can simulate complex scenarios such as enterprise intranet and cloud network. The reinforcement learning agent learns by interacting with the training environment, receiving feedback from the environment and reward signals generated by the trainees' operations, and executes evolutionary actions based on the learning strategy, including activating the WAF, opening new ports, strengthening defenses, or providing clues, so as to achieve real-time adaptive adjustment of the training environment. The traffic acquisition and behavior perception module includes a multi-source data collector and a data fusion and correlation engine. The multi-source data collector captures raw data from three levels: network traffic, host logs, and operation commands. The data fusion and correlation engine cleans, standardizes, and timestamps the raw data from multiple sources. Through session reorganization and behavior chain reconstruction technologies, it links isolated events into a complete behavior sequence with the learner as the main thread, generating a well-structured and standardized behavior data stream. The AI-powered intelligent assessment engine comprises an attack chain semantic analysis unit, a multi-dimensional behavioral profiling unit, and a vulnerability exploitation in-depth analysis unit. These three units operate in parallel to achieve in-depth analysis and quantitative scoring of student actions. The attack link semantic analysis unit adopts a natural language processing (NLP) model to perform word embedding and semantic understanding on the operation commands and attack payloads of the trainee, identify the semantic equivalence of the attack chain stage and operation intention, and the like: The multi-dimensional behavior profiling unit adopts a machine learning model to establish a behavior profile for the trainee from the dimensions of efficiency, concealment, standardization, and innovation, analyze the number of steps and time required to achieve the key target of the attack, evaluate the operation of log cleaning and encrypted channel use, and detect non-standard creative attack methods; The vulnerability exploit deep analysis unit adopts a graph neural network (GNN) to model the vulnerability exploit process as a graph structure, analyze the structure and path of the graph, and evaluate the understanding degree and application flexibility of the trainee on the vulnerability principle; The intelligent tutoring module includes a real-time prompting engine, a review report generator, and a learning path recommender, and provides personalized teaching support based on the evaluation results of the AI intelligent evaluation engine, wherein: The real-time prompting engine is internally provided with a rule engine and a knowledge graph, detects the trainee in accordance with a preset “dilemma” rule, retrieves relevant content from the knowledge graph, and pushes the prompts in the form of heuristic questions or keywords; The review report generator adopts a templating and natural language generation (NLG) technology to convert the multi-dimensional evaluation results into a human-readable report containing scores, operation backtracking, root cause analysis, and knowledge point hyperlinks; The learning path recommender constructs a dynamic skill model based on the historical evaluation data of the trainee by using a collaborative filtering and content recommendation algorithm, matches the dynamic skill model with a curriculum resource library, and recommends personalized theoretical courses or practical training projects; The center control and management platform includes a visual control interface, a resource and user management unit, and an AI model management interface, and provides unified control functions for administrators, wherein: The visual control interface displays the real-time topology and state monitoring data of the training environment in a Web graphical manner, provides one-key environment deployment, pause, snapshot, and recovery functions; The resource and user management unit is responsible for managing the account permissions of the trainees and teachers, and monitoring and managing the allocation and use of underlying computing, storage, and network resources; The AI model management interface supports administrators to upload vulnerability data and attack scripts, configure and update the parameters of the AI evaluation model, and realize continuous evolution of the system.

[0013] The embodiment provides a network security vulnerability test training scene generation method, including the following steps: (1) Dynamic environment generation: the administrator sets the training target, the scene script generator generates the initial scene script based on the knowledge graph technology, the basic architecture layer deploys the initial training environment through containerization, virtualization and SDN technology, and the reinforcement learning agent executes evolutionary actions to realize real-time adaptive adjustment of the environment according to the environmental state feedback and the operation reward signal of the student; (2) Traffic collection and behavior perception: the multi-source data collector comprehensively captures network traffic, host logs, and operation command raw data, and the data fusion and association engine processes the raw data to generate standardized behavior data streams; (3) AI intelligent evaluation: the AI intelligent evaluation engine realizes fine-grained evaluation and quantitative scoring of student operations through attack link semantic analysis, multi-dimensional behavior profiling, and vulnerability exploitation depth analysis; (4) Intelligent tutoring: the intelligent tutoring module pushes heuristic prompts through the real-time prompting engine according to the evaluation results, generates detailed review reports through the review report generator, and recommends personalized learning paths through the learning path recommender; (5) System control: the administrator manages users, monitors the training environment state, and configures AI model parameters through the central control and management platform to realize global control of the system.

[0014] In addition to the above embodiments, the present application can also have other implementation manners. Any technical solution formed by equivalent substitution or equivalent transformation falls within the protection scope required by the present application.

Claims

1. A network security vulnerability testing training scenario generation system, characterized in that: It includes a dynamic environment generation module, a traffic acquisition and behavior perception module, an AI intelligent assessment engine, an intelligent tutoring module, and a central control and management platform. Through the collaborative operation of these modules, it achieves dynamic generation of training scenarios, comprehensive perception of student behavior, intelligent assessment, and personalized tutoring. The dynamic environment generation module includes a scenario script generator, an infrastructure layer, and a reinforcement learning agent. The scenario script generator, based on knowledge graph technology, automatically extracts related elements of vulnerabilities, services, and defense measures according to the training objectives set by the administrator, generating an initial scenario script containing multiple hosts and various vulnerabilities. The infrastructure layer adopts containerization, virtualization, and software-defined networking technologies, receives the initial scenario script, and quickly deploys it to form a running training environment, enabling flexible customization and real-time adjustment of network topology. It can simulate complex scenarios such as enterprise intranets and cloud networks. The reinforcement learning agent learns through interaction with the running training environment, receives feedback on the environment status and reward signals generated by the trainee's operations, and executes evolutionary actions based on the learning strategy, including activating WAF, opening new ports, strengthening defenses, or providing clues, to achieve real-time adaptive adjustment of the training environment. The traffic acquisition and behavior perception module includes a multi-source data collector and a data fusion and correlation engine. The multi-source data collector comprehensively captures raw data from three levels: network traffic, host logs, and operation commands during the training process. The AI ​​intelligent assessment engine includes an attack chain semantic analysis unit, a multi-dimensional behavior profiling unit, and a vulnerability exploitation in-depth analysis unit. The three units run in parallel to achieve in-depth analysis and quantitative scoring of the trainees' operations. The intelligent tutoring module includes a real-time prompt engine, a review report generator, and a learning path recommender, providing personalized teaching support based on the assessment results of the AI ​​intelligent assessment engine. The central control and management platform includes a visual management interface, resource and user management units, and an AI model management interface, providing administrators with unified management functions.

2. The network security vulnerability testing training scenario generation system according to claim 1, characterized in that: The data fusion and association engine cleans, standardizes, and timestamps the multi-source raw data. Through session reorganization and behavior chain reconstruction technologies, it associates isolated events into a complete behavior sequence with the learner as the main thread, generating a well-structured and standardized behavior data stream.

3. The network security vulnerability testing training scenario generation system according to claim 1, characterized in that: The attack chain semantic analysis unit uses a natural language processing (NLP) model to perform word embedding and semantic understanding on the student's operation commands and attack payload, and identifies the semantic equivalence of attack chain stages and operation intentions: The multi-dimensional behavioral profiling unit uses a machine learning model to create behavioral profiles for trainees from dimensions such as efficiency, concealment, standardization, and innovation. It analyzes the number of steps and time required for an attack to achieve its key objectives, evaluates operations such as log clearing and encrypted channel usage, and detects non-standard creative attack methods. The vulnerability exploitation deep analysis unit uses graph neural networks (GNNs) to model the vulnerability exploitation process as a graph structure. By analyzing the structure and path of the graph, it assesses the trainees' understanding of vulnerability principles and their application flexibility.

4. The network security vulnerability testing training scenario generation system according to claim 1, characterized in that: The real-time prompting engine has a built-in rule engine and knowledge graph. When it detects that a student meets the preset "dilemma" rule, it retrieves relevant content from the knowledge graph and pushes prompts in the form of heuristic questions or keywords. The debriefing report generator uses templated and natural language generation (NLG) technologies to transform multidimensional evaluation results into human-readable reports that include scores, operation backtracking, root cause analysis, and knowledge point hyperlinks. The learning path recommender uses students' historical assessment data to build a dynamic skill model through collaborative filtering and content recommendation algorithms, matches it with the course resource library, and recommends personalized theoretical courses or practical training projects.

5. The network security vulnerability testing training scenario generation system according to claim 1, characterized in that: The visual management interface displays the real-time topology map and status monitoring data of the training environment in a web-based graphical manner, and provides one-click environment deployment, pause, snapshot, and recycling functions; The resource and user management unit is responsible for managing student and teacher account permissions, and monitoring and managing the allocation and use of underlying computing, storage, and network resources; The AI ​​model management interface allows administrators to upload vulnerability data and attack scripts, configure and update AI evaluation model parameters, and enable continuous system evolution.

6. A method for generating network security vulnerability testing training scenarios, characterized in that, The system for generating network security vulnerability testing training scenarios according to any one of claims 1-5 includes the following steps: (1) Dynamic environment generation: The administrator sets the training objectives, the scenario script generator generates the initial scenario script based on knowledge graph technology, the infrastructure layer deploys the initial training environment through containerization, virtualization and SDN technology, and the reinforcement learning agent executes evolutionary actions to achieve real-time adaptive adjustment of the environment based on the feedback of the environment status and the reward signal of the student's operation. (2) Traffic collection and behavior perception: The multi-source data collector captures network traffic, host logs and operation command raw data from all directions. The data fusion and correlation engine processes the raw data to generate standardized behavior data streams. (3) AI Intelligent Assessment: The AI ​​intelligent assessment engine achieves fine-grained assessment and quantitative scoring of trainees' operations through attack link semantic analysis, multi-dimensional behavioral profiling and vulnerability exploitation in-depth analysis; (4) Intelligent tutoring: Based on the assessment results, the intelligent tutoring module pushes heuristic prompts through the real-time prompt engine, generates detailed review reports through the review report generator, and recommends personalized learning paths through the learning path recommender; (5) System control: Administrators manage users, monitor the status of the training environment, and configure AI model parameters through the central control and management platform to achieve global system control.

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