Method based on 5G mobile communication network security

Abstract

The invention relates to the technical field of network security, and discloses a 5G mobile communication network security-based method, which comprises the following steps of: 1, performing risk assessment, and constructing a risk matrix and defining a risk level by identifying interconnection and intercommunication potential threats between a 5G network and other networks; 2, designing a security architecture, and ensuring that each slice has an independent security mechanism by utilizing a network slicing technology so as to realize data isolation; step 3, data encryption is implemented, and an end-to-end encryption protocol is adopted to ensure data transmission security between the user equipment and the network service; and 4, introducing dynamic identity verification, and verifying the user in combination with a biological recognition technology. By implementing a unified cross-network security protocol, the data transmission security between the 5G network and other networks is ensured, the risk of data leakage and hostile attack is effectively reduced, and the overall security protection capability in different network environments is enhanced.

Description

Technical Field

[0001] The present invention relates to the field of network security technology, and specifically to a method based on 5G mobile communication network security. Background Art

[0002] With the development of 5G mobile communication technology, network security issues have become increasingly prominent. Traditional network security measures are difficult to meet the complexity and diversity of the 5G environment, especially in terms of cross-network data transmission, IoT device access and compliance requirements. More effective solutions are urgently needed.

[0003] In this context, existing technologies such as CN117879975B propose a security method based on 5G mobile communication networks, but there are still defects. First, the technology fails to fully implement a unified cross-network security protocol, resulting in data transmission between 5G networks and other networks facing the risk of data leakage and malicious attacks. Therefore, enhancing the overall security protection capabilities in different network environments has become an urgent problem to be solved.

[0004] Secondly, existing technologies provide security management measures for IoT devices, but have not established a dedicated framework to ensure that access devices meet the security requirements of identity authentication and data encryption, resulting in potential network attacks and security vulnerabilities. At the same time, there is a lack of flexible compliance assessment mechanisms, making it difficult to adapt to the legal and regulatory requirements of different regions around the world, posing legal risks to organizations.

[0005] Furthermore, the existing technologies are insufficient in terms of equipment updates and vulnerability repairs, resulting in the failure to effectively improve the overall level of network security. In particular, in terms of dynamic identity authentication, biometric technology has not been fully integrated, which has affected the security and convenience of user identity authentication, thereby reducing user experience and limiting user access to 5G network services.

[0006] In addition, some real-time monitoring systems have been implemented, but existing technologies are unable to quickly identify and respond to potential security threats, causing network managers to react slowly when security incidents occur and failing to effectively protect the security of user data.

[0007] Finally, CN117879975B provides an audit mechanism, but lacks a comprehensive security audit scoring model, making it difficult to systematically evaluate the security and compliance of an organization. It fails to help the organization identify weak links and take necessary improvement measures, posing challenges to the organization's security management level in a rapidly changing network environment.

[0008] Therefore, based on the deficiencies in the above background technology, the present invention proposes a method based on 5G mobile communication network security, aiming to solve the above problems. Summary of the invention

[0009] In view of the deficiencies in the prior art, the present invention provides a method based on 5G mobile communication network security to solve the problems raised in the above background technology.

[0010] To achieve the above objectives, the present invention is implemented through the following technical solutions: A method based on 5G mobile communication network security, comprising the following steps: Step 1: Conduct risk assessment, identify potential threats to the interconnection between 5G networks and other networks, build a risk matrix, and define risk levels; Step 2: Design a security architecture and use network slicing technology to ensure that each slice has an independent security mechanism to achieve data isolation; Step 3: Implement data encryption and use end-to-end encryption protocol to ensure data transmission security between user devices and network services. The formula is: , Among them, E is the encrypted data, D is the original data, and K is the encryption key; Step 4: Introduce dynamic identity authentication and verify users with biometric technology; Step 5: Deploy a real-time monitoring system to analyze network traffic and identify abnormal activities using artificial intelligence algorithms. The formula is: , in, is the abnormal activity detection result, is the network traffic feature vector, is the threshold value; Step 6: Establish an automated patch management system to regularly update 5G network equipment to eliminate known vulnerabilities; Step 7: Implement the IoT device security management framework to ensure that all connected IoT devices meet the authentication and data encryption requirements; Step 8: Establish a legal and compliance assessment mechanism to ensure that 5G networks comply with relevant laws and regulations in different regions; Step 9: Promote user terminal security certification standards, implement continuous monitoring and management of terminal equipment, and conduct regular security audits to enhance security protection capabilities through evaluation and improvement of security systems.

[0011] Preferably, in step 1, the risk level is divided using a risk matrix formula, the formula is: , Among them, R is the risk level, P is the probability of occurrence, and I is the impact degree.

[0012] Preferably, in step 2, the network slicing technology is modeled and optimized by the following algorithm formula: Slice resource allocation model: , in, is the total resource allocation of the i-th slice, is the unit resource capacity of the j-th resource, is the allocation ratio of slice i to resource j, is the total number of available resource types; Slice Quality Assurance Model: , in, is the quality assurance level of the i-th slice, The amount of resources required to meet the kth quality of service requirement is: is the total number of service quality requirement types, is the total resource allocation for the i-th slice.

[0013] Preferably, in step 2, the network slicing technology is modeled and optimized by the following algorithm formula: Slice resource allocation model: , in, is the total resource allocation of the i-th slice, is the unit resource capacity of the j-th resource, is the allocation ratio of slice i to resource j, is the total number of available resource types; Slice Quality Assurance Model: , in, is the quality assurance level of the i-th slice, The amount of resources required to meet the kth quality of service requirement is: is the total number of service quality requirement types, is the total resource allocation for the i-th slice.

[0014] Preferably, in step 5, the real-time monitoring system uses a deep learning model to perform traffic analysis, and the loss function in the training process is defined as: , in, is the loss value, is the true label, is the predicted value, is the sample size.

[0015] Preferably, in step 6, the automated patch management is modeled and optimized by the following algorithm formula: Patch application priority model: , in, is the application priority of the patch, is the risk level of the patch repair. Score the security of the patch, The duration from the patch release to the current time; Patch application decision model: , in, is the decision result of whether the patch is applied, Set application thresholds, The application priority of the patch.

[0016] Preferably, in step 7, IoT device security management is modeled and implemented by the following algorithm formula: IoT device security scoring model: , in, Provide comprehensive security scores for IoT devices. Score the security of your authentication. Score the security of data encryption. Security scores for auditing and monitoring, , , The weight coefficients corresponding to different safety measures; Risk Assessment and Decision Model: , in, is the equipment risk assessment value, Provide comprehensive security scores for IoT devices. The risk threshold is set.

[0017] Preferably, in step 8, the legal and compliance assessment is modeled and implemented by the following algorithm formula: Compliance Scoring Model: , in, Score overall compliance, Score data protection compliance, Score compliance with security measures, Score compliance with other laws and regulations, , , weight coefficients corresponding to different compliance aspects; Compliance decision model: , in, For compliance decision results, The compliance score threshold is set. Score overall compliance.

[0018] Preferably, in step 9, the user terminal security authentication is modeled and implemented by the following algorithm formula: Endpoint security scoring model: , in, The comprehensive security score of the user terminal. Score the security of the terminal device. Score the user's identity security. Score the session safety, , , The weight coefficients corresponding to different safety measures; Certification decision model: , Among them, A is the authentication decision result, is the comprehensive security score of the user terminal, and T is the set security score threshold.

[0019] Preferably, in step 9, the security audit is modeled and implemented by the following algorithm formula: Security Audit Scoring Model: , in, is the overall score of the safety audit. Score compliance, Score the security incident handling, Score vulnerability management, , , weight coefficients corresponding to different audit aspects; Audit decision model: , in, Decision making for audit results, is the overall score of the safety audit. The audit scoring threshold is set.

[0020] The present invention provides a method for 5G mobile communication network security. It has the following beneficial effects: 1. The present invention ensures the security of data transmission between 5G networks and other networks by implementing a unified cross-network security protocol. The design effectively reduces the risks of data leakage and malicious attacks and enhances the overall security protection capabilities under different network environments.

[0021] 2. The present invention provides a special security management framework to ensure that all access devices can meet the security requirements of identity authentication and data encryption. Its measures can significantly improve the security of IoT devices and prevent potential network attacks and security vulnerabilities. At the same time, it establishes a flexible compliance assessment mechanism to adapt to the legal and regulatory requirements of different regions, helping organizations to better meet compliance requirements and reduce legal risks when operating globally.

[0022] 3. The present invention can ensure that the equipment is updated in a timely manner and that known vulnerabilities are fixed, thereby improving the overall level of network security and reducing the occurrence of security incidents caused by unpatched devices. At the same time, the dynamic identity authentication method is combined with biometric technology to enhance the security and convenience of user identity authentication, improve security, improve user experience, and enable users to access 5G network services more conveniently.

[0023] 4. The present invention deploys a real-time monitoring system based on artificial intelligence to quickly identify and respond to potential security threats, so that network managers can take measures at the first time to prevent the occurrence of security incidents and protect the security of user data.

[0024] 5. The security audit scoring model of the present invention can comprehensively evaluate the security and compliance of an organization, help the organization identify weak links and take necessary improvement measures. Its systematic audit process improves the security management level of the organization and helps maintain security compliance in a rapidly changing network environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0026] In order to make the technical personnel in the technical field understand the scheme of the present invention, the technical scheme in the embodiment of the present invention will be clearly and completely described below in combination with the drawings in the embodiment of the present invention. Obviously, the described embodiment is a partial embodiment of the present invention, not a complete embodiment. Based on the embodiment of the present invention, other embodiments obtained by ordinary technicians in the field without creative work should fall within the scope of protection of the present invention.

[0027] The present invention is described in detail below in conjunction with the accompanying drawings: Example: Please see attached Figure 1 , an embodiment of the present invention provides a method for 5G mobile communication network security, comprising the following steps: Step 1: Conduct risk assessment, identify potential threats to the interconnection between 5G networks and other networks, build a risk matrix, and define risk levels; Step 2: Design a security architecture and use network slicing technology to ensure that each slice has an independent security mechanism to achieve data isolation; Step 3: Implement data encryption and use end-to-end encryption protocol to ensure data transmission security between user devices and network services. The formula is: , Among them, E is the encrypted data, D is the original data, and K is the encryption key; Step 4: Introduce dynamic identity authentication and verify users with biometric technology; Step 5: Deploy a real-time monitoring system to analyze network traffic and identify abnormal activities using artificial intelligence algorithms. The formula is: , in, is the abnormal activity detection result, is the network traffic feature vector, is the threshold value; Step 6: Establish an automated patch management system to regularly update 5G network equipment to eliminate known vulnerabilities; Step 7: Implement the IoT device security management framework to ensure that all connected IoT devices meet the authentication and data encryption requirements; Step 8: Establish a legal and compliance assessment mechanism to ensure that 5G networks comply with relevant laws and regulations in different regions; Step 9: Promote user terminal security certification standards, implement continuous monitoring and management of terminal equipment, and conduct regular security audits to enhance security protection capabilities through evaluation and improvement of security systems.

[0028] By identifying potential threats between 5G networks and other networks, building a risk matrix and defining risk levels, data support can be provided for subsequent security strategy formulation, helping organizations prioritize identifying and responding to the most critical risks and improving the effective use of resources. Use network slicing technology to provide independent security mechanisms for each slice, ensure data isolation, reduce security risks between different services, enhance network flexibility and scalability, and allow for meeting different user needs while maintaining a high level of security; End-to-end encryption protocol is used to protect data transmission between user devices and network services, ensuring the confidentiality and integrity of data during transmission, effectively preventing data from being intercepted or tampered with, and enhancing users' trust in network services; Combine biometric technology for user identity authentication, improve the security and convenience of identity authentication, enhance protection against malicious access, improve user experience, and reduce security risks caused by password management; By analyzing network traffic through artificial intelligence algorithms and identifying abnormal activities in a timely manner, it can quickly respond to potential security threats, improve visibility and control over the network environment, and enable network managers to take preventive measures before problems occur; Regularly perform security updates on 5G network equipment to eliminate known vulnerabilities and ensure that the system is always in a secure state. Automated management reduces the need for manual intervention and reduces security risks caused by delayed patch application. Ensure that connected IoT devices meet authentication and data encryption requirements, thereby improving the security of the IoT environment, effectively preventing network attacks caused by insufficient device security, and protecting user data privacy; Ensure that 5G networks comply with relevant laws and regulations in different regions and reduce legal risks. Compliance assessment helps organizations to promptly identify and fix compliance loopholes, making them safer and more reliable when operating globally. By implementing continuous monitoring and management of terminal devices, potential security threats can be discovered and responded to in a timely manner. Regular security audits provide a systematic evaluation and improvement path to help organizations continuously improve their security protection capabilities and ensure security compliance in a rapidly changing network environment.

[0029] In step 1, the risk matrix formula is used to divide the risk level. The formula is: , Among them, R is the risk level, P is the probability of occurrence, and I is the impact degree.

[0030] The role of the risk matrix formula enables organizations to more clearly understand and compare the severity of different risks. Using the risk level calculated by the formula, organizations can classify and prioritize risks according to their level, which helps to focus on the most critical risks and reduce potential losses when resources are limited. The benefits of the risk matrix formula are that it can quickly identify and assess risks, reduce the time for manual analysis and judgment, improve the efficiency of risk management, promote transparency and sharing of information, ensure that key areas receive adequate attention and protection, and continuously improve the overall safety level and enhance support for risk management.

[0031] In step 2, network slicing technology is modeled and optimized through the following algorithm formula: Slice resource allocation model: , in, is the total resource allocation of the i-th slice, is the unit resource capacity of the j-th resource, is the allocation ratio of slice i to resource j, is the total number of available resource types; Slice Quality Assurance Model: , in, is the quality assurance level of the i-th slice, The amount of resources required to meet the kth quality of service requirement is: is the total number of service quality requirement types, is the total resource allocation for the i-th slice.

[0032] The slice resource allocation model is used to reasonably allocate network resources, ensure that each slice obtains appropriate resources according to its needs, and optimize the configuration so that network resources are used efficiently and waste is avoided; The slice quality assurance model provides users with a consistent service experience by ensuring that resource allocation for each slice meets specific service quality requirements, helping to ensure that the quality requirements of different users and services are met; Improve the overall efficiency of the network through reasonable resource allocation, reduce the risk of idle and overloaded resources, and ensure higher service quality; The quality assurance model ensures that users can obtain the required performance when using network services, improves user satisfaction and thus enhances customer loyalty; Network slicing technology allows slices to be quickly created, adjusted, and deleted based on demand, allowing the network to quickly adapt to changing market demands and business scenarios; Different slices can be optimized for different service quality requirements, support various application scenarios, and promote the diversified application of 5G technology; Through effective resource management, the risk of network failure and performance degradation caused by improper resource allocation can be reduced, and network reliability can be improved.

[0033] In step 4, dynamic identity authentication is modeled and implemented using the following algorithmic formula: Authentication Model: , in, is the authentication result, The identity information entered by the user. The user's biometric information, is the timestamp; Weight evaluation model: , in, For the comprehensive safety score, Score the security of your identity information. Score the security of biometric information, Score the security of the timestamp, , , The weight coefficients corresponding to different information types.

[0034] The authentication model provides a comprehensive authentication result by combining the identity information, biometric information and timestamp entered by the user to ensure that legitimate users can obtain access rights; The weighted assessment model quantifies the importance of different types of information in identity authentication, providing a comprehensive security score that helps improve the security and effectiveness of identity authentication; Through timestamps, the system can dynamically identify and prevent replay attacks, improving the real-time nature and security of identity authentication; By combining multiple authentication methods, dynamic authentication significantly improves system security and reduces the risks of a single authentication method; Using biometrics for verification is usually faster and more convenient. Users do not need to remember complex passwords, which improves their access experience. Allows weight coefficients to be adjusted according to specific circumstances, and dynamic adjustments can be made based on actual security needs, thus enhancing the flexibility of the system; Reduce the risk of identity theft by introducing biometrics and timestamps, ensuring that authentic users can access sensitive information and services; The dynamic authentication system can record authentication requests and results, provide necessary data support for security audits, and help organizations meet compliance requirements.

[0035] In step 5, the real-time monitoring system uses a deep learning model to perform traffic analysis, and the loss function in the training process is defined as: , in, is the loss value, is the true label, is the predicted value, is the sample size.

[0036] The loss function is used to quantify the predictive performance of the model and helps determine the accuracy and effectiveness of the model during the training process. By calculating the loss value, the deep learning model can use the optimization algorithm to adjust the weights and biases to minimize the loss. By monitoring the difference between the predicted value and the actual value of traffic data, abnormal activities or attack behaviors can be identified in time to improve network security. The use of loss functions can help the model gradually learn and improve, thereby improving the accuracy of predicting network traffic status and enhancing the effectiveness of the real-time monitoring system. Accurate traffic analysis and prediction enable network managers to more quickly identify and respond to potential security threats, reducing the time window of loss and impact. Deep learning models can adapt to the ever-changing network environment. Through continuous training and updating of models, they can maintain the ability to identify new attacks and improve the overall network security level. By analyzing loss values ​​and model performance, the network management team can make more reasonable decisions based on data, optimize network resource allocation and security policies, and through accurate anomaly detection, the real-time monitoring system can reduce false alarms and missed alarms, improve the overall reliability of the system, and ensure the security of user data and services.

[0037] In step 6, automated patch management is modeled and optimized using the following algorithmic formula: Patch application priority model: , in, is the application priority of the patch, is the risk level of the patch repair. Score the security of the patch, The duration from the patch release to the current time; Patch application decision model: , in, is the decision result of whether the patch is applied, Set application thresholds, The application priority of the patch.

[0038] The patch application priority model helps organizations prioritize the most critical patches by quantifying the risk level, security, and timeliness of patches. The model ensures that resource allocation and decision-making in the patch management process are data-driven. The patch application decision model provides a clear basis for decision-making based on the priority evaluation results, allowing network administrators to quickly determine whether to apply a patch, improve the efficiency of patch management, and reduce the complexity of decision-making; By prioritizing high-risk and high-security patches, organizations can significantly reduce the risk of security incidents caused by untimely patching and improve overall network security; Automated patch management reduces the need for manual intervention and makes the patch application process more efficient, faster, and consistent through the application of priority and decision models; Through priority assessment, organizations can focus resources and time on the most important patches, avoiding neglect of critical patches due to distraction; Evaluating the security and timeliness of patches helps ensure that patches do not introduce new vulnerabilities or issues, and maintains system stability and reliability; Regular and timely application of patches helps organizations comply with industry standards and laws and regulations, and reduces legal risks caused by compliance issues; Automated patch management provides opportunities for continuous evaluation and improvement, making the patch management process more complete through feedback mechanisms and enhancing overall security management capabilities.

[0039] In step 7, IoT device security management is modeled and implemented using the following algorithmic formula: IoT device security scoring model: , in, Provide comprehensive security scores for IoT devices. Score the security of your authentication. Score the security of data encryption. Security scores for auditing and monitoring, , , The weight coefficients corresponding to different safety measures; Risk Assessment and Decision Model: , in, is the equipment risk assessment value, Provide comprehensive security scores for IoT devices. The risk threshold is set.

[0040] The IoT device security scoring model provides a comprehensive integrated security score by combining multiple security measures such as identity authentication, secure encryption, and audit monitoring, allowing organizations to fully evaluate the security of devices and ensure that all aspects are taken care of; The risk assessment and decision-making model helps organizations identify high-risk devices and decide whether to take additional safety measures by comparing the comprehensive safety score with the set risk threshold, providing data support for risk management; Through a comprehensive scoring model, we ensure that all IoT devices meet the necessary security standards, thereby reducing the risk of device attacks and improving overall security. The assessment results guide organizations to develop targeted security strategies, focus resources on devices with lower security scores, and more effectively improve their security; The comprehensive security score provides a transparent view of device security, allowing organizations to understand the security status of each device and making it easier to conduct audits and compliance checks; The risk assessment model allows organizations to adjust risk management strategies based on real-time security scores, enhance responsiveness to emerging threats, and improve overall network flexibility and adaptability; Regular security assessments and monitoring help organizations comply with industry standards and laws and regulations, and reduce legal risks caused by compliance issues; By continuously updating and evaluating security scores, organizations can identify and fix security vulnerabilities in a timely manner, ensuring that IoT devices remain secure in a rapidly changing network environment.

[0041] In step 8, the legal and compliance assessment is modeled and implemented through the following algorithmic formula: Compliance Scoring Model: , in, Score overall compliance, Score data protection compliance, Score compliance with security measures, Score compliance with other laws and regulations, , , weight coefficients corresponding to different compliance aspects; Compliance decision model: , in, For compliance decision results, The compliance score threshold is set. Score overall compliance.

[0042] The compliance scoring model provides an overall compliance score by quantifying the degree of compliance with data protection, security measures and other laws and regulations, helping organizations comprehensively assess their compliance status; The compliance decision model compares the overall compliance score with the set threshold, providing a clear basis for compliance judgment, so that management can clearly understand whether relevant laws and regulations are complied with; By quantifying compliance scores, organizations can more effectively manage and maintain their compliance, ensure compliance with laws and regulations, and reduce legal risks. The scoring model can clearly identify the organization's deficiencies in data protection, security measures or other compliance aspects, and help develop targeted improvement measures. The comprehensive compliance score makes the compliance status visual, which is easier for internal and external stakeholders of the organization to understand, and improves transparency and trust. Regular compliance assessments and scoring help organizations adjust strategies in a timely manner, adapt to new laws and regulations and industry standards, and maintain continuous compliance. Clear scoring models and decision-making results provide the necessary data support for compliance audits, making the audit process more efficient and transparent.

[0043] In step 9, user terminal security authentication is modeled and implemented using the following algorithm formula: Endpoint security scoring model: , in, The comprehensive security score of the user terminal. Score the security of the terminal device. Score the user's identity security. Score the session safety, , , The weight coefficients corresponding to different safety measures; Certification decision model: , Among them, A is the authentication decision result, is the comprehensive security score of the user terminal, and T is the set security score threshold; In step 9, the security audit is modeled and implemented using the following algorithmic formula: Security Audit Scoring Model: , in, is the overall score of the safety audit. Score compliance, Score the security incident handling, Score vulnerability management, , , weight coefficients corresponding to different audit aspects; Audit decision model: , in, Decision making for audit results, is the overall score of the safety audit. The audit scoring threshold is set.

[0044] The user terminal security scoring model and security audit scoring model provide a comprehensive scoring system, enabling organizations to comprehensively evaluate the security and compliance of terminals and audit processes. The certification and audit decision model provides management with a clear basis for judgment, helping to quickly decide whether a terminal has passed certification and whether the audit results are compliant. Through comprehensive security scoring, we ensure that both user terminals and audit processes meet security standards, thereby reducing security vulnerabilities and attack risks. The scoring model quantifies security and compliance, helping organizations to more effectively identify and solve problems, optimize management processes, improve work efficiency, and provide a clear security and compliance status, so that internal employees and external stakeholders have higher trust in the organization's security management capabilities. Regular security audits and certification processes help ensure that organizations comply with industry standards and laws and regulations and reduce legal risks. By continuously updating the scoring and decision-making models, organizations can adjust security strategies in a timely manner to adapt to the ever-changing threat environment and compliance requirements. Based on the scoring results, organizations can reasonably allocate resources and concentrate on improving terminals and audit links with lower security, thereby improving the overall security management effect.

[0045] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method based on 5G mobile communication network security, characterized in that: The steps include: Step 1: Conduct risk assessment, identify potential threats to the interconnection between 5G networks and other networks, build a risk matrix, and define risk levels; Step 2: Design a security architecture and use network slicing technology to ensure that each slice has an independent security mechanism to achieve data isolation; Step 3: Implement data encryption and use end-to-end encryption protocol to ensure data transmission security between user devices and network services. The formula is: , Among them, E is the encrypted data, D is the original data, and K is the encryption key; Step 4: Introduce dynamic identity authentication and verify users with biometric technology; Step 5: Deploy a real-time monitoring system to analyze network traffic and identify abnormal activities using artificial intelligence algorithms. The formula is: , in, is the abnormal activity detection result, is the network traffic feature vector, is the threshold value; Step 6: Establish an automated patch management system to regularly update 5G network equipment to eliminate known vulnerabilities; Step 7: Implement the IoT device security management framework to ensure that all connected IoT devices meet the authentication and data encryption requirements; Step 8: Establish a legal and compliance assessment mechanism to ensure that 5G networks comply with relevant laws and regulations in different regions; Step 9: Promote user terminal security certification standards, implement continuous monitoring and management of terminal equipment, and conduct regular security audits to enhance security protection capabilities through evaluation and improvement of security systems.

2. A method based on 5G mobile communication network security according to claim 1, characterized in that: In step 1, the risk level is divided using the risk matrix formula, which is: ， Among them, R is the risk level, P is the probability of occurrence, and I is the impact degree.

3. A method based on 5G mobile communication network security according to claim 1, characterized in that: In step 2, the network slicing technology is modeled and optimized through the following algorithm formula: Slice resource allocation model: , in, is the total resource allocation of the i-th slice, is the unit resource capacity of the j-th resource, is the allocation ratio of slice i to resource j, is the total number of available resource types; Slice Quality Assurance Model: , in, is the quality assurance level of the i-th slice, The amount of resources required to meet the kth quality of service requirement is: is the total number of service quality requirement types, is the total resource allocation for the i-th slice.

4. A method based on 5G mobile communication network security according to claim 1, characterized in that: In step 4, dynamic identity authentication is modeled and implemented through the following algorithm formula: Authentication Model: , in, is the authentication result, The identity information entered by the user. The user's biometric information, is the timestamp; Weight evaluation model: , in, For the comprehensive safety score, Score the security of your identity information. Score the security of biometric information, Score the security of the timestamp, , , The weight coefficients corresponding to different information types.

5. A method based on 5G mobile communication network security according to claim 1, characterized in that: In step 5, the real-time monitoring system uses a deep learning model to perform traffic analysis, and the loss function in the training process is defined as: ， in, is the loss value, is the true label, is the predicted value, is the sample size.

6. A method based on 5G mobile communication network security according to claim 1, characterized in that: In step 6, the automated patch management is modeled and optimized using the following algorithm formula: Patch application priority model: , in, is the application priority of the patch, is the risk level of the patch repair. Score the security of the patch, The duration from the patch release to the current time; Patch application decision model: , in, is the decision result of whether the patch is applied, Set application thresholds, The application priority of the patch.

7. A method based on 5G mobile communication network security according to claim 1, characterized in that: In step 7, IoT device security management is modeled and implemented through the following algorithm formula: IoT device security scoring model: ， in, Provide comprehensive security scores for IoT devices. Score the security of your authentication. Score the security of data encryption. Security scores for auditing and monitoring, , , The weight coefficients corresponding to different safety measures; Risk Assessment and Decision Model: , in, is the equipment risk assessment value, Provide comprehensive security scores for IoT devices. The risk threshold is set.

8. A method based on 5G mobile communication network security according to claim 1, characterized in that: In step 8, the legal and compliance assessment is modeled and implemented through the following algorithmic formula: Compliance Scoring Model: ， in, Score overall compliance, Score data protection compliance, Score compliance with security measures, Score compliance with other laws and regulations, , , weight coefficients corresponding to different compliance aspects; Compliance decision model: , in, For compliance decision results, The compliance score threshold is set. Score overall compliance.

9. A method based on 5G mobile communication network security according to claim 1, characterized in that: In step 9, the user terminal security authentication is modeled and implemented by the following algorithm formula: Endpoint security scoring model: ， in, The comprehensive security score of the user terminal. Score the security of the terminal device. Score the user's identity security. Score the session safety, , , The weight coefficients corresponding to different safety measures; Certification decision model: , Among them, A is the authentication decision result, is the comprehensive security score of the user terminal, and T is the set security score threshold.

10. A method based on 5G mobile communication network security according to claim 1, characterized in that: In step 9, the security audit is modeled and implemented through the following algorithm formula: Security Audit Scoring Model: ， in, is the overall score of the safety audit. Score compliance, Score the security incident handling, Score vulnerability management, , , weight coefficients corresponding to different audit aspects; Audit decision model: , in, Decision making for audit results, is the overall score of the safety audit. The audit scoring threshold is set.

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