Intrusion prevention system used in meta-universe environment

Abstract

The invention discloses an intrusion prevention system used in a meta universe environment, which relates to the technical field of information security and comprises a virtual identity verification module, a virtual asset protection module, a virtual scene integrity verification module, a behavior analysis and threat detection module, a real-time response and recovery module and a security policy management module. According to the invention, by setting a multi-layer defense mechanism, through virtual identity verification, virtual asset protection, scene integrity verification, behavior analysis and other multi-layer defense mechanisms, the system can comprehensively cover various security threats in the universe, it is ensured that a user is protected in each link in the virtual world, and AI and ML technologies are introduced at the same time, so that the security of the user is improved. The system can dynamically adapt to complex virtual interaction scenes and detect and respond to threats in real time, meanwhile, the system can continuously optimize the protection model through the self-adaptive learning function so as to cope with new attack means, and high real-time performance and self-adaptability are achieved.

Description

Technical Field

[0001] The present invention relates to the field of information security technology, and in particular to an intrusion defense system used in a metaverse environment. Background Art

[0002] With the rapid development of information technology, the concept of the metaverse has gradually moved from science fiction to reality. The metaverse refers to a highly immersive virtual world built by virtual reality (VR), augmented reality (AR) and Internet technology. In this world, users can use virtual avatars to engage in a variety of activities such as socializing, entertainment, trading, and work. They can even own virtual property, build virtual scenes, and interact in these scenes. However, the rapid development of the metaverse has also brought unprecedented security challenges, especially in protecting user privacy, virtual assets, and the integrity of virtual scenes. In the traditional network security system, the intrusion prevention system (IPS) is mainly used to detect and prevent malicious activities in the network. These systems protect physical and digital assets from attacks by analyzing network traffic, monitoring system behavior, and detecting known attack patterns. However, the unique characteristics of the metaverse make traditional IPS systems appear powerless in the face of emerging threats.

[0003] First, the metaverse is a highly interactive three-dimensional virtual space where users conduct various activities through virtual avatars. The security of virtual avatars and virtual items is directly related to the user's experience and interests. However, the protection mechanism of these elements in the metaverse is still imperfect. Secondly, traditional IPS systems lack special protection mechanisms for virtual assets, and it is difficult to prevent the theft, tampering and illegal trading of these assets. Thirdly, the virtual scenes in the metaverse are extremely complex and diverse. These scenes can be created and edited by multiple users. Tampering with the scenes may not only destroy the user experience, but also cause chaos in the virtual world. Finally, existing IPS systems usually focus on network-level security, while the protection of user privacy is relatively insufficient. Therefore, an intrusion prevention system for the metaverse environment is proposed to deal with these complex and diverse threats. Summary of the invention

[0004] The purpose of the present invention is to provide an intrusion defense system for use in a metaverse environment to solve the above-mentioned problems and provide a comprehensive solution to security challenges in virtual space. By introducing multi-level defense mechanisms and advanced technical means, the system of the present invention can not only cope with current security threats, but also has the potential and scalability to cope with new types of attacks in the future, providing a solid security guarantee for the development of the metaverse.

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is: An intrusion prevention system for use in a metaverse environment, comprising a virtual identity authentication module, a virtual asset protection module, a virtual scene integrity verification module, a behavior analysis and threat detection module, a real-time response and recovery module, and a security policy management module; The virtual identity authentication module is used to continuously monitor the user's behavior, identify and prevent the virtual identity from being stolen or tampered with, and once abnormal behavior is detected, immediately issue an alarm and take protective measures; The virtual asset protection module is used to protect the user's virtual assets. Combined with blockchain technology, the system ensures the transparency and immutability of each virtual asset transaction, while monitoring and preventing unauthorized asset transfer and copying in real time; The virtual scene integrity verification module ensures that the virtual scene has not been illegally tampered with or injected with malicious code through technical means such as hash verification of 3D models and scene consistency detection. Once an abnormality is found, the system will automatically roll back the scene or prevent unsafe scene loading; The behavior analysis and threat detection module is based on artificial intelligence and machine learning technology, which can dynamically analyze the behavior patterns of users in the metaverse, identify abnormal activities and generate real-time security alerts. According to the threat classification results, the system will take corresponding measures; The real-time response and recovery module is used to coordinate and manage the security incident response and recovery operations of all modules in the system, integrate and optimize the independent response functions of each module, achieve rapid and unified response of the whole system, and ensure effective recovery in emergency situations; The security policy management module is used by administrators to set different security levels, access control rules and protection measures, and synchronize these policies between different platforms.

[0006] A further improvement of the technical solution of the present invention is that the specific implementation of the virtual identity authentication module includes: Implementation of multi-factor authentication: The system ensures the uniqueness and security of user identity by integrating biometric technology and behavioral feature analysis; Implementation of anomaly detection and response: When users log in or use the system, the system continuously monitors their behavior patterns and detects anomalies in real time; Implementation of continuous behavior monitoring: The system continuously analyzes user operation behaviors through the built-in user behavior analysis engine based on machine learning algorithms.

[0007] A further improvement of the technical solution of the present invention is that the implementation steps of the multi-factor authentication are divided into initial registration, real-time authentication and behavioral feature analysis, and the implementation steps of the anomaly detection and response are divided into behavioral baseline analysis, anomaly detection and response mechanism.

[0008] A further improvement of the technical solution of the present invention is that the specific implementation of the virtual asset protection module includes: The application of blockchain technology, including transaction records, smart contract execution, and distributed ledgers; Real-time asset monitoring, including: asset activity monitoring, abnormal activity detection, response and freezing mechanisms; Asset recovery mechanism, the asset recovery mechanism includes: backup mechanism, log recording and backtracking, and asset recovery process.

[0009] A further improvement of the technical solution of the present invention is that the specific implementation of the virtual scene integrity verification module includes: Hash verification mechanism, the contents of which include: hash value calculation, hash value comparison, anomaly detection and alarm; Scenario data consistency detection, which includes: multi-user collaborative monitoring, authority management and data locking, and real-time consistency detection; Automatic rollback and scene recovery, the automatic rollback and scene recovery content includes: rollback mechanism triggering, scene backup and recovery, and log playback function.

[0010] A further improvement of the technical solution of the present invention is that the specific implementation of the behavior analysis and threat detection module includes: AI-based behavior analysis implementation, the AI-based behavior analysis implementation includes: data collection, model training, and real-time inference; Threat classification and response, including: threat classification, response mechanism; Adaptive learning, wherein the adaptive learning content includes: model update, threat intelligence integration, and automatic rule adjustment.

[0011] A further improvement of the technical solution of the present invention is that the specific implementation of the real-time response and recovery module includes: Central coordination and management, including: event aggregation and analysis, system-wide response coordination, and multi-level response strategy execution; Automated recovery management, including: unified recovery mechanism, global rollback and recovery; Event log and audit, the event log and audit content includes: unified log management, security event audit and reporting, compliance and traceability.

[0012] A further improvement of the technical solution of the present invention is that the specific implementation of the security policy management module includes: Policy customization and configuration implementation: The system provides administrators with a policy configuration platform based on a Web or desktop application; Cross-platform policy synchronization: The system has designed a policy synchronization mechanism to ensure that policies remain consistent across multiple platforms; Policy update and management: The system provides policy update and historical version management functions.

[0013] Due to the adoption of the above technical solution, the present invention has the following technical advances compared with the prior art: 1. The present invention provides an intrusion defense system for use in a metaverse environment. By setting up a multi-layer defense mechanism, such as virtual identity authentication, virtual asset protection, scene integrity verification, and behavior analysis, the system can comprehensively cover various security threats in the metaverse, ensuring that users are protected at every stage in the virtual world. By introducing AI and ML technologies, the system can dynamically adapt to complex virtual interactive scenes, detect and respond to threats in real time, and the adaptive learning function enables the system to continuously optimize the protection model to cope with newly emerging attack methods, with strong real-time and adaptability.

[0014] 2. The present invention provides an intrusion prevention system for a metaverse environment. The system supports customized security policy management, allowing administrators to flexibly adjust security policies according to specific application scenarios and user needs, thereby achieving effective security management of different types of metaverse platforms and achieving the purpose of customized security policies. Finally, by integrating blockchain technology, the system can ensure the transparency and immutability of virtual asset transactions and enhance users' trust in the security of virtual assets.

[0015] 3. The present invention provides an intrusion defense system for use in a metaverse environment, which is not only suitable for individual users, but can also be widely used in virtual reality platforms, augmented reality applications, online games, virtual conference systems and other digital platforms involving virtual space. With the further development of metaverse technology, the system provided by the present invention has broad market application prospects and will become an indispensable security technology in the future virtual world. Through the collaborative work of various modules in the system, it can effectively respond to the diverse security threats in the metaverse and provide users with a safe and reliable virtual environment, thereby promoting the healthy development of the metaverse. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a schematic diagram of the intrusion prevention system module of the present invention; Figure 2 A schematic diagram of the implementation content of the virtual identity authentication module of the present invention; Figure 3 A schematic diagram of the implementation content of the virtual asset protection module of the present invention; Figure 4 A schematic diagram of the implementation content of the virtual scene integrity verification module of the present invention; Figure 5 A schematic diagram of the implementation content of the behavior analysis and threat module of the present invention; Figure 6 A schematic diagram of the implementation content of the real-time response and recovery module of the present invention; Figure 7 This is a schematic diagram of the implementation content of the security policy management module of the present invention. DETAILED DESCRIPTION

[0017] The present invention is further described in detail below in conjunction with embodiments: Example like Figure 1-7 As shown, the present invention provides an intrusion prevention system for use in a metaverse environment, including a virtual identity authentication module, a virtual asset protection module, a virtual scene integrity verification module, a behavior analysis and threat detection module, a real-time response and recovery module, and a security policy management module; The virtual identity authentication module is used to continuously monitor the user's behavior, identify and prevent the virtual identity from being stolen or tampered with. Once abnormal behavior is detected, an alarm will be immediately issued and protective measures will be taken. Through multi-factor authentication mechanisms (such as biometrics, behavioral feature analysis, etc.), the user's virtual identity security is ensured. This module continuously monitors the user's behavior, identifies and prevents the virtual identity from being stolen or tampered with. Once abnormal behavior is detected, such as multi-location login, abnormal transactions, etc., the system will immediately issue an alarm and take protective measures; The virtual asset protection module is used to protect the user's virtual assets. By combining blockchain technology, the system ensures the transparency and immutability of each virtual asset transaction, and monitors and blocks unauthorized asset transfers and copying in real time. This module focuses on protecting the user's virtual assets, including virtual currency, virtual land, digital artworks, etc. By combining blockchain technology, the system ensures the transparency and immutability of each virtual asset transaction, and monitors and blocks unauthorized asset transfers and copying in real time. The virtual scene integrity verification module ensures that the virtual scene has not been illegally tampered with or injected with malicious code through technical means such as hash verification of 3D models and scene consistency detection. Once an abnormality is found, the system will automatically roll back the scene or prevent unsafe scene loading. Virtual scenes in the metaverse are usually created and edited by multiple users in collaboration, which makes the security and integrity of the scene particularly important. This module ensures that the virtual scene has not been illegally tampered with or injected with malicious code through technical means such as hash verification of 3D models and scene consistency detection. Once an abnormality is found, the system will automatically roll back the scene or prevent unsafe scene loading; The behavior analysis and threat detection module is based on artificial intelligence and machine learning technology. It can dynamically analyze the behavior patterns of users in the metaverse, identify abnormal activities and generate real-time security alerts. Based on artificial intelligence (AI) and machine learning (ML) technology, the system can dynamically analyze the behavior patterns of users in the metaverse, identify abnormal activities and generate real-time security alerts. Through adaptive learning, the module continuously optimizes the threat detection model to respond to new attack methods and actively defends against threats before they cause damage; The real-time response and recovery module is used to coordinate and manage the security incident response and recovery operations of all modules in the system, integrate and optimize the independent response functions of each module, achieve rapid and unified response of the entire system, and ensure effective recovery in emergency situations. This module is responsible for coordinating and managing the security incident response and recovery operations of all modules in the system. By integrating and optimizing the independent response functions of each module, a rapid and unified response of the entire system is achieved, and effective recovery in emergency situations is ensured. After detecting a security threat, the module can immediately take response measures, including isolating suspicious users, blocking malicious code, rolling back virtual asset transactions, correcting virtual scenes, etc. Through the rapid response mechanism, the system can control threats to a minimum in the shortest time, ensuring the stability of the virtual environment and undisturbed user experience; The security policy management module allows administrators to set different security levels, access control rules and protective measures, and synchronize these policies between different platforms. In order to adapt to different types of Metaverse application scenarios, this module allows administrators to customize security policies according to specific needs. Administrators can set different security levels, access control rules and protective measures, and synchronize these policies between different platforms to ensure cross-platform security consistency.

[0018] The following is a detailed description of the specific implementation of each module: The specific implementation of the virtual identity authentication module includes: Implementation of multi-factor authentication: The system ensures the uniqueness and security of user identity by integrating biometric technology (such as fingerprint, iris, and facial recognition) and behavioral feature analysis (such as user typing speed and mouse movement trajectory); The implementation steps of multi-factor authentication are divided into initial registration, real-time authentication and behavioral feature analysis. The implementation steps of anomaly detection and response are divided into behavioral baseline analysis, anomaly detection and response mechanism, specifically: 1. Initial registration: When a user registers for the first time, the system will collect the user's biometric data (such as facial images) and perform feature extraction through a convolutional neural network (CNN) to generate an encrypted feature vector and store it in the database; 2. Real-time authentication: Every time a user logs in, the system collects the current biometric features in real time and compares them with the stored feature vectors. The comparison algorithm uses an improved Euclidean distance measurement method to ensure a high-precision matching rate; 3. Behavioral feature analysis: In addition to biometric features, the system also uses HMM (Hidden Markov Model) to model user behavioral characteristics, such as operating habits, and detect user behavioral deviations.

[0019] Implementation of anomaly detection and response: When users log in or use the system, the system continuously monitors their behavior patterns and detects anomalies in real time, specifically: 1. Behavior baseline analysis: The system establishes a normal behavior baseline model for each user, including normal login time, operation mode, virtual asset operation, etc. The baseline model is dynamically updated through machine learning algorithms (such as SVM); 2. Anomaly detection: When a user's behavior deviates from the baseline, the system immediately identifies the abnormal behavior. For example, the system may detect abnormal geographic location logins, overly frequent transaction requests, abnormal changes in operating habits, etc. 3. Response mechanism: Once an anomaly is detected, the system triggers a security response mechanism, including: (1) Secondary authentication: requiring users to provide additional verification information, such as a secondary password or answers to security questions; (2) Restrict operations: temporarily freeze the user account or restrict certain high-risk operations until the user completes verification; (3) Alarm notification: The system sends an instant notification to the administrator, containing details of the detected anomaly, to facilitate further investigation and processing by the administrator.

[0020] Implementation of continuous behavior monitoring: The system continuously analyzes user operation behaviors through the built-in user behavior analysis engine based on machine learning algorithms (such as SVM, Support Vector Machine). The behavior data is processed in real time through stream processing platforms such as Kafka and compared with the normal behavior baseline. The process includes data collection, feature extraction, model training and real-time inference.

[0021] The specific implementation methods of the virtual asset protection module include: The application of blockchain technology includes: transaction records, smart contract execution, and distributed ledgers; Transaction records: When a user conducts a virtual asset transaction in the Metaverse, the system will first generate a transaction request, which includes detailed information about the transaction, such as asset type, transaction amount, and the identities of both parties to the transaction. The system uses the SHA-256 hash algorithm to encrypt the transaction request to generate a transaction hash value, and stores this transaction hash value together with the transaction information in a new block of the blockchain. Each block will contain the hash value of the previous block, forming an unalterable chain, thereby ensuring the authenticity and security of the transaction; Smart contract execution: The smart contract embedded in the system will automatically execute the predetermined transaction rules. The smart contract is implemented by code and runs on the blockchain platform. When the transaction request meets the conditions of the smart contract, the smart contract will automatically complete the transaction process and record it on the blockchain. The execution of the smart contract ensures the automation of the transaction and the absence of intermediary interference, and the record cannot be tampered with; Distributed ledger: The system maintains a distributed ledger on multiple blockchain nodes to ensure that each node holds a complete copy of transaction records. The distributed ledger uses consensus algorithms (such as PoW, PoS) to ensure data consistency among all nodes. Even if individual nodes are attacked or fail, the overall system still maintains data integrity and reliability.

[0022] Real-time asset monitoring, including: asset activity monitoring, abnormal activity detection, response and freezing mechanisms; Asset activity monitoring: The system monitors all virtual asset activities in real time, including asset transfers, purchases, sales, donations, etc. The system uses an event-driven architecture, and whenever asset activity occurs, the event triggers the monitoring module to collect and analyze data; Abnormal activity detection: The system uses a dual detection mechanism based on rules and machine learning to identify abnormal activities. The rule detection is based on predefined thresholds, such as a single transaction amount exceeding a certain limit, frequent transactions in a short period of time, etc. The machine learning model analyzes historical data to identify abnormal behavior patterns, such as sudden large-scale asset transfers, abnormal cross-border transaction frequencies, etc. Once an anomaly is detected, the system will issue an alarm; Response and freezing mechanism: When the system detects abnormal activity, the response mechanism is immediately executed. The system will first notify the user and administrator, and then automatically freeze the relevant virtual assets according to the situation. The freezing operation is achieved by recording the change of asset status on the blockchain. The freeze can only be lifted after confirmation by the administrator to ensure that the assets are not further transferred or traded.

[0023] Asset recovery mechanism. The asset recovery mechanism includes: backup mechanism, log recording and backtracking, and asset recovery process.

[0024] Backup mechanism: The system regularly backs up the status and transaction records of virtual assets. The backup data is stored in a distributed file system (such as IPFS). The backup frequency is determined by the system configuration. The main purpose of the backup is to quickly restore to a known safe state after a security incident occurs. Log recording and backtracking: Each transaction is recorded in detail in the transaction log, which includes the transaction initiation time, the identity information of both parties, the transaction amount, the transaction result, etc. When asset theft or abnormal transactions occur, the system will analyze the transaction path through the log backtracking function to locate the time point and transaction details when the assets were illegally transferred; Asset recovery process: After confirming that the asset has been stolen or transferred abnormally, the system starts the asset recovery process. First, the asset holding status is restored to the last safe state through backup data and log records. During the recovery process, the system will generate new transaction records and record the recovery operation on the blockchain to ensure the transparency and traceability of all recovery operations. After the recovery is completed, the system will notify the user and administrator to confirm the safe recovery of the asset.

[0025] The specific implementation methods of the virtual scene integrity verification module include: Hash verification mechanism, which includes: hash value calculation, hash value comparison, anomaly detection and alarm; Hash value calculation: The system regularly calculates the hash values ​​of all 3D models and their associated data (such as textures, animation files, physical properties, etc.) in the virtual scene. Specifically, the system uses secure hash algorithms such as SHA-256 to generate the hash value of each data file. The hash value generation process includes reading the file content byte by byte and inputting the read content into the hash algorithm to generate a unique hash value; Hash value comparison: After the hash values ​​are generated, the system will compare them with the original hash values ​​stored in the database to detect whether the scene data has changed. The comparison process is automatically performed in the background, and the system will compare periodically or at specific trigger events (such as scene loading, user editing is completed); Anomaly detection and alerts: If the system detects an inconsistency during the hash value comparison process (i.e., the hash values ​​do not match), this indicates that the scene data may have been illegally tampered with or damaged. The system will immediately trigger the anomaly detection mechanism, generate an alert, and notify the administrator. The alert content includes detailed information such as the name of the tampered data file, the corresponding hash value, and the comparison time.

[0026] Scenario data consistency detection, which includes: multi-user collaborative monitoring, permission management and data locking, and real-time consistency detection; Multi-user collaborative monitoring: In a virtual scene edited by multiple users, the system monitors the editing activities of all users in real time to ensure the consistency of scene data. The system uses a distributed consistency algorithm (such as Paxos or Raft) to synchronize data to other users' sessions when each user submits a modification, to ensure that the scene content seen by all users is consistent; Permission management and data locking: To prevent unauthorized modification or malicious code injection, the system limits the user's editing permissions through a permission management mechanism. Before each editing operation, the system verifies the user's permissions to ensure that only authorized users can modify specific scenes. In addition, when the user edits a scene, the system locks the relevant data to prevent other users from modifying the same data at the same time, thereby avoiding data conflicts; Real-time consistency check: After receiving a modification request from a user, the system will verify the consistency of the data through a consistency algorithm. If a consistency problem is detected, such as unauthorized modification or data conflict, the system will reject the modification request and notify the user and administrator.

[0027] Automatic rollback and scene recovery, automatic rollback and scene recovery content includes: rollback mechanism triggering, scene backup and recovery, and log playback function.

[0028] Rollback mechanism triggering: When the system detects that the scene data has been illegally tampered with or a consistency problem occurs, the system automatically triggers the rollback mechanism. The rollback mechanism restores the scene to the last known safe state by tracing back the historical version of the scene data. Scene backup and recovery: The system regularly backs up virtual scenes, including all scene-related data such as 3D models, textures, animation data, and physical properties. The backup data is timestamped and stored in a distributed file system (such as IPFS or a custom distributed storage solution). When the rollback mechanism is triggered, the system will restore to the most recent backup version based on the timestamp. Log playback function: In order to ensure the transparency and traceability of the scene recovery process, the system integrates a log playback function. The log playback records the detailed information of all scene modifications, including modification time, modification user, modification content, etc. During the scene recovery process, the system will play back the logs one by one to ensure that all recovery operations are consistent with the log records. Administrators can review the legitimacy of each modification and confirm the correctness of the recovery operation by viewing the log playback.

[0029] The specific implementation methods of the behavior analysis and threat detection module include: AI-based behavioral analysis implementation includes: data collection, model training, and real-time inference; Data collection: During the user interaction process, the system continuously collects the user's operational behavior data. These behavioral data include but are not limited to login time, transaction frequency, virtual asset transfer and interaction mode, scene switching frequency, etc. Data collection is achieved by deploying collection modules at various entrances of the system (such as login interface, transaction interface, scene transition point). The collected data is transmitted in real time to a dedicated time series database. The database uses a storage solution that supports high concurrency and fast writing, such as InfluxDB or TimescaleDB, to ensure the integrity and timeliness of the data; Model training: Using the collected historical behavior data, the system performs model training on a deep learning framework (such as TensorFlow or PyTorch). Specifically, the system uses a recurrent neural network (RNN) model because RNN is good at processing time series data and can capture the temporal dependencies of user behavior, including: (1) Data preprocessing: Clean, normalize and batch process the collected historical behavior data to ensure the consistency and validity of data input; (2) Model architecture design: The architecture of the RNN model includes an input layer (receiving user behavior data), a hidden layer (processing time series data through LSTM or GRU units), and an output layer (generating behavior pattern predictions). The model optimizes parameters through back propagation and gradient descent algorithms; (3) Model training and validation: After multiple rounds of training data iteration, the model will generate the normal behavior pattern of each user. The training process uses training sets and validation sets to monitor model performance and prevent overfitting. After training, the model will be saved to the system's model library for real-time inference.

[0030] Real-time inference: During the operation of the system, whenever a user performs an operation, the real-time collected behavior data will be input into the trained RNN model for inference. The system uses the model to predict whether the user's current behavior deviates from the normal mode and generates a behavior deviation score. If the deviation score exceeds the preset threshold, the system will mark the operation as abnormal and trigger an alarm mechanism, including: (1) Data stream processing: Real-time data is processed through a data stream processing framework (such as Apache Kafka or Apache Flink) to ensure low-latency transmission to the model inference engine; (2) Inference engine execution: The system loads the trained RNN model into the inference engine, inputs real-time behavior data into the model, and outputs prediction results and behavior deviation scores. The scores are used to assess the degree of abnormality of the operation behavior.

[0031] Threat classification and response, threat classification and response content includes: threat classification, response mechanism; Threat classification: After detecting abnormal behavior, the system first uses the support vector machine (SVM) model to perform preliminary classification of threats. Classification is based on the behavioral deviation score and other contextual information (such as the user's historical behavior patterns, geographic location, and device used) to classify threats into three risk levels: low, medium, and high. The SVM model learns how to classify newly detected abnormal behaviors into different risk levels by training historical threat data; Response mechanism: Based on the threat classification results, the system will take corresponding response measures, specifically: (1) Low-risk threat: The system will issue a warning to the user and record the behavior for further analysis, but will not take immediate restrictive measures; (2) Medium-risk threats: The system will restrict certain high-risk operations of users, such as large-scale virtual asset transfers or access to sensitive scenarios, and notify the administrator for manual review; (3) High-risk threats: For high-risk threats, the system will immediately lock the user account, suspend all operations, and send an emergency alert to the administrator. The locking operation is achieved by modifying the user's account status field to ensure that the user can no longer access or operate system resources.

[0032] Adaptive learning: Adaptive learning content includes: model update, threat intelligence integration, and automatic rule adjustment.

[0033] Model update: The system regularly updates and optimizes RNN and SVM models through adaptive learning functions to maintain the ability to detect emerging threats. The system automatically extracts the latest behavior data from the database regularly, retrains the model, and generates new behavior patterns. The frequency of model updates can be configured by the system administrator to balance the accuracy of the model and system performance; Threat intelligence integration: The system integrates the latest threat intelligence sources, such as global threat databases and public intelligence from the cybersecurity community, and uses this intelligence to update threat classification rules and detection standards. Intelligence data is imported into the system through an API interface and combined with internal behavioral data to enhance the threat identification capabilities of the model; Automatic rule adjustment: The system automatically adjusts detection rules and response strategies based on model update results and threat intelligence analysis. For example, when a new threat is detected, the system can dynamically adjust the scoring threshold for abnormal behavior or update response measures to ensure that the system can effectively respond to the ever-changing security environment.

[0034] The specific implementation methods of the real-time response and recovery module include: Central coordination and management, including: event summary and analysis, system-wide response coordination, and multi-level response strategy execution; Event aggregation and analysis: The real-time response and recovery module collects security event information from various modules (such as virtual identity authentication module, virtual asset protection module, behavior analysis and threat detection module, etc.). Through the centralized event aggregation platform, the system can uniformly analyze and prioritize all reported events. The analysis process includes the evaluation of the source, type, impact scope and potential threats of the event to determine the response strategy for the entire system; System-wide response coordination: When the system detects a high-risk event, the real-time response and recovery module coordinates all related modules to perform consistent response operations. For example, when a large-scale virtual asset theft threat is detected, this module will not only trigger the freezing operation of the virtual asset protection module, but also coordinate the virtual identity authentication module to lock the relevant user accounts and notify the behavior analysis module to strengthen monitoring; Multi-level response strategy execution: The real-time response and recovery module executes different levels of response operations according to the pre-set multi-level response strategy. Low-risk events may only require a single module to handle, while medium- and high-risk events will trigger the coordinated operation of multiple modules. Each response strategy contains clear operation steps and execution order to ensure that the system can respond quickly and efficiently when dealing with complex threats.

[0035] Automated recovery management, which includes: unified recovery mechanism, global rollback and recovery; Unified recovery mechanism: When a module starts a recovery operation, the real-time response and recovery module intervenes and manages the entire recovery process. This module is responsible for coordinating the recovery steps of different modules to ensure data consistency and system integrity. For example, when the virtual scene integrity verification module triggers a scene rollback, the real-time response and recovery module will synchronously check the data status of the virtual asset protection module to ensure the consistency of the scene and assets after recovery; Global rollback and recovery: In extreme cases, the system may need to perform a global rollback operation to restore the entire Metaverse system to a known safe state. The real-time response and recovery module performs a global rollback operation through a unified rollback management system to restore all system states including user data, virtual scenes, and virtual assets. The rollback process includes extracting data from the distributed backup system, synchronously restoring the data state of all modules, and verifying the consistency of the restored system.

[0036] Event log and audit, event log and audit content includes: unified log management, security incident audit and reporting, compliance and traceability.

[0037] Unified log management: The real-time response and recovery module collects and manages the response and recovery logs of all modules. The system stores, indexes, and retrieves these logs in a unified manner through a central log management platform (such as ELK Stack). Unified log management helps administrators fully understand the security status of the system and provides complete event records for post-audit; Security event audit and reporting: The system generates a detailed security event audit report after each response or recovery operation. The report includes details of the triggering event, the response and recovery operations performed by each module, the final system status, and any abnormal conditions that occurred. Administrators can use the audit report to evaluate the security performance of the system and optimize future response strategies; Compliance and traceability: To ensure that the system complies with relevant security standards and laws and regulations, the real-time response and recovery module supports automatic generation of compliance reports and provides event traceability when necessary. Through the traceability function, administrators can view the detailed processing process of each event and confirm the legality and compliance of all operations.

[0038] The specific implementation methods of the security policy management module include: Policy customization and configuration implementation: The system provides administrators with a policy configuration platform based on a Web or desktop application, specifically: 1. Policy definition: Administrators define access control, threat response, data protection and other policies through the interface. After input, the system will store the policies in the database in JSON or XML format; 2. Rule engine execution: The system’s rule engine parses the stored policy files and applies them to the entire metaverse environment, using Drools or a custom rule engine to implement rule parsing and execution.

[0039] Cross-platform policy synchronization: The system has designed a policy synchronization mechanism to ensure that policies are consistent across multiple platforms. The specific implementation includes: 1. Version control: The policy file is version controlled using Git or similar tools, and all changes are recorded and traceable; 2. Synchronization mechanism: Use a message queue-based system (such as Kafka) to synchronize policy changes to all platform instances in real time to ensure policy consistency across the entire system.

[0040] Policy update and management: The system provides policy update and historical version management functions, including: 1. Regular updates: The system automatically checks for policy updates, and administrators can set the policy update frequency and method (automatic or manual); 2. Version rollback: When problems occur during policy updates, administrators can easily restore the policy to the previous version through the interface provided by the system.

[0041] In summary, the present invention sets up a multi-layer defense mechanism. Through multi-level defense mechanisms such as virtual identity authentication, virtual asset protection, scene integrity verification and behavior analysis, the system can comprehensively cover various security threats in the metaverse, ensuring that users are protected in every link of the virtual world. At the same time, by introducing AI and ML technologies, the system can dynamically adapt to complex virtual interactive scenes, detect and respond to threats in real time. At the same time, the adaptive learning function enables the system to continuously optimize the protection model to cope with emerging attack methods. It has strong real-time and adaptability. It is not only suitable for individual users, but also can be widely used in virtual reality platforms, augmented reality applications, online games, virtual conference systems and other digital platforms involving virtual space. With the further development of metaverse technology, the system provided by the present invention has broad market application prospects and will become an indispensable security technology in the future virtual world. Through the collaborative work of various modules in the system, it can effectively cope with the diverse security threats in the metaverse and provide users with a safe and reliable virtual environment, thereby promoting the healthy development of the metaverse.

[0042] The above generally describes the present invention in detail, but it is obvious to a person skilled in the art that some modifications or improvements can be made to the present invention. Therefore, modifications or improvements that do not depart from the spirit of the present invention are within the scope of protection of the present invention.

Claims

1. An intrusion prevention system for use in a metaverse environment, characterized in that: It includes virtual identity authentication module, virtual asset protection module, virtual scene integrity verification module, behavior analysis and threat detection module, real-time response and recovery module and security policy management module; The virtual identity authentication module is used to continuously monitor the user's behavior, identify and prevent the virtual identity from being stolen or tampered with, and once abnormal behavior is detected, immediately issue an alarm and take protective measures; The virtual asset protection module is used to protect the user's virtual assets. Combined with blockchain technology, the system ensures the transparency and immutability of each virtual asset transaction, while monitoring and preventing unauthorized asset transfer and copying in real time; The virtual scene integrity verification module ensures that the virtual scene has not been illegally tampered with or injected with malicious code through technical means such as hash verification of 3D models and scene consistency detection. Once an abnormality is found, the system will automatically roll back the scene or prevent unsafe scene loading; The behavior analysis and threat detection module is based on artificial intelligence and machine learning technology, and can dynamically analyze the user's behavior patterns in the metaverse, identify abnormal activities and generate real-time security alerts; The real-time response and recovery module is used to coordinate and manage the security incident response and recovery operations of all modules in the system, integrate and optimize the independent response functions of each module, achieve rapid and unified response of the whole system, and ensure effective recovery in emergency situations; The security policy management module is used by administrators to set different security levels, access control rules and protection measures, and synchronize these policies between different platforms.

2. The intrusion prevention system for use in a metaverse environment according to claim 1, characterized in that: The specific implementation of the virtual identity authentication module includes: Implementation of multi-factor authentication: The system ensures the uniqueness and security of user identity by integrating biometric technology and behavioral feature analysis; Implementation of anomaly detection and response: When users log in or use the system, the system continuously monitors their behavior patterns and detects anomalies in real time; Implementation of continuous behavior monitoring: The system continuously analyzes user operation behaviors through the built-in user behavior analysis engine based on machine learning algorithms.

3. The intrusion prevention system for use in a metaverse environment according to claim 2, characterized in that: The implementation steps of the multi-factor authentication are divided into initial registration, real-time authentication and behavior feature analysis, and the implementation steps of the anomaly detection and response are divided into behavior baseline analysis, anomaly detection and response mechanism.

4. The intrusion prevention system for use in a metaverse environment according to claim 1, characterized in that: The specific implementation of the virtual asset protection module includes: The application of blockchain technology, including transaction records, smart contract execution, and distributed ledgers; Real-time asset monitoring, including: asset activity monitoring, abnormal activity detection, response and freezing mechanisms; Asset recovery mechanism, the asset recovery mechanism includes: backup mechanism, log recording and backtracking, and asset recovery process.

5. The intrusion prevention system for use in a metaverse environment according to claim 1, characterized in that: The specific implementation of the virtual scene integrity verification module includes: Hash verification mechanism, the contents of which include: hash value calculation, hash value comparison, anomaly detection and alarm; Scenario data consistency detection, which includes: multi-user collaborative monitoring, authority management and data locking, and real-time consistency detection; Automatic rollback and scene recovery, the automatic rollback and scene recovery content includes: rollback mechanism triggering, scene backup and recovery, and log playback function.

6. The intrusion prevention system for use in a metaverse environment according to claim 1, characterized in that: The specific implementation of the behavior analysis and threat detection module includes: AI-based behavior analysis implementation, the AI-based behavior analysis implementation includes: data collection, model training, and real-time inference; Threat classification and response, including: threat classification, response mechanism; Adaptive learning, wherein the adaptive learning content includes: model update, threat intelligence integration, and automatic rule adjustment.

7. The intrusion prevention system for use in a metaverse environment according to claim 1, characterized in that: The specific implementation of the real-time response and recovery module includes: Central coordination and management, including: event aggregation and analysis, system-wide response coordination, and multi-level response strategy execution; Automated recovery management, including: unified recovery mechanism, global rollback and recovery; Event log and audit, the event log and audit content includes: unified log management, security event audit and reporting, compliance and traceability.

8. The intrusion prevention system for use in a metaverse environment according to claim 1, characterized in that: The specific implementation of the security policy management module includes: Policy customization and configuration implementation: The system provides administrators with a policy configuration platform based on a Web or desktop application; Cross-platform policy synchronization: The system has designed a policy synchronization mechanism to ensure that policies remain consistent across multiple platforms; Policy update and management: The system provides policy update and historical version management functions.

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