A method for optimizing the security performance of VLC relay system

Abstract

The present invention relates to the field of visible light communication technology, and in particular to a method for optimizing the security performance of a VLC relay system. The method comprises the following steps: performing risk identification on a target VLC relay system, thereby obtaining a system potential attack risk model; performing a penetration combat simulation on the target VLC relay system through a simulation test based on the system potential attack risk model, thereby obtaining system security vulnerability analysis data; performing a security protection algorithm evaluation on the target VLC relay system according to the system security vulnerability analysis data, thereby obtaining a system security protection scheme design report; performing cascade multi-dimensional security coding on the target VLC relay system according to the system security protection scheme design report, thereby obtaining cascade multi-dimensional security coding. The present invention can improve the security performance of a VLC relay system, reduce the risk of information leakage and data tampering, and ensure the confidentiality, integrity and reliability of communications.

Description

Technical Field

[0001] The present invention relates to the technical field of visible light communication, and in particular to a method for optimizing the security performance of a VLC relay system. Background Art

[0002] With the development of wireless communication technology, VLC, as an emerging communication technology, uses visible light band for data transmission and has broad application prospects. VLC relay system plays a key role in VLC communication. Relay equipment can expand the communication range, enhance signal coverage and improve communication quality. However, the current VLC relay system has some challenges in terms of security performance.

[0003] The security issues of traditional VLC relay systems mainly include the following aspects: Unauthorized third parties can steal communication content by receiving optical signals, resulting in information leakage risks. Attackers can intercept and replay data packets sent by relay devices, resulting in repeated transmission of data or unauthorized access. Due to the mobility of relay devices, attackers can forge relay devices and tamper with data transmission, destroying the confidentiality and integrity of communications. In multi-hop relay systems, relay links may become targets of attackers. By attacking relay nodes on the link, attackers can interfere with or interrupt the entire communication link. Summary of the invention

[0004] Based on this, it is necessary for the present invention to provide a method for optimizing the security performance of a VLC relay system to solve at least one of the above technical problems.

[0005] To achieve the above object, a VLC relay system security performance optimization method comprises the following steps:

[0006] Step S1: Identify the risks of the target VLC relay system to obtain a potential attack risk model of the system;

[0007] Step S2: Based on the system potential attack risk model, a penetration combat simulation is performed on the target VLC relay system through simulation testing to obtain system security vulnerability analysis data; based on the system security vulnerability analysis data, a security protection algorithm is evaluated on the target VLC relay system to obtain a system security protection solution design report;

[0008] Step S3: performing cascade multi-dimensional security coding on the target VLC relay system according to the system security protection scheme design report, thereby obtaining the cascade multi-dimensional security coding;

[0009] Step S4: Perform retinal identification and digital watermark extraction on the user to obtain the user's biometric data; use the biometric data to perform user-personalized double encryption on the cascaded multi-dimensional security code to obtain specific confidential communication encryption parameters;

[0010] Step S5: construct a distributed network attack and defense simulation platform; based on specific confidential communication encryption parameters, use middleman spoofing signals of different strengths to simulate attack tests on the target VLC relay system to obtain anti-tampering performance data; perform security performance evaluation on specific confidential communication encryption parameters according to the anti-tampering performance data to obtain an anti-attack optimization solution; dynamically update the key of specific confidential communication encryption parameters according to the anti-attack optimization solution.

[0011] The present invention can fully understand the potential attack risks of the system, including security vulnerabilities that may lead to information leakage, data tampering and communication interruption, by identifying the risks of the target VLC relay system. Obtaining the system potential attack risk model helps to clarify the security threats that may exist in the system and provide guidance for subsequent security protection measures. Through simulation testing and penetration combat simulation, actual attack scenarios can be simulated, and security vulnerability analysis data of the system under different attack situations can be obtained. This helps to discover the weaknesses and security vulnerabilities of the system and provide a basis for subsequent security protection schemes. Based on the system security vulnerability analysis data, the target VLC relay system is evaluated for security protection algorithms to determine a security protection scheme suitable for the system. This helps to select appropriate technologies and methods to improve the security performance of the system. The security performance of the target VLC relay system can be enhanced by the application of cascaded multidimensional security coding. Cascaded multidimensional security coding can provide multiple protection levels, encrypt and protect communication data, and prevent information leakage and data tampering. The application of cascaded multidimensional security coding can increase the difficulty for attackers to obtain communication content, improve the confidentiality and integrity of the system, and thus protect the security of communication. By performing retinal recognition and digital watermark extraction on users, biometric data of users can be obtained. This personalized biometric data can be used for user authentication and communication encryption to increase the security of the system. Using the user's biometric data to perform personalized double encryption on the cascaded multi-dimensional security code can provide higher confidentiality for the communication data. In this way, even if the attacker obtains the encrypted data, it is difficult to decrypt and destroy the integrity of the data. Building a distributed network attack and defense simulation platform can simulate actual attack scenarios and evaluate the security performance of the target VLC relay system. This helps to discover the weaknesses and vulnerabilities of the system under different attack situations and provide a basis for improving defense strategies. By simulating attack tests and using middleman spoofing signals of different strengths, the tamper-proof performance of the target VLC relay system can be evaluated. This helps to determine the system's anti-attack capabilities and provide directions for improving defense strategies. By evaluating the security performance of specific confidential communication encryption parameters based on tamper-proof performance data, the effectiveness and security of the encryption parameters can be determined. This helps to optimize the system's security protection plan and improve the confidentiality and integrity of communication data. The security of the system can be enhanced by dynamically updating the keys of specific confidential communication encryption parameters. Dynamically updating the keys can prevent key leakage and cracking and maintain the confidentiality and integrity of communications. In summary, the present invention can improve the security performance of the VLC relay system, reduce the risks of information leakage and data tampering, and ensure the confidentiality, integrity and reliability of communications. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments thereof made with reference to the following drawings:

[0013] Figure 1 A schematic flow chart of the steps of a method for optimizing the security performance of a VLC relay system according to an embodiment is shown.

[0014] Figure 2 A detailed flow chart of step S4 of an embodiment is shown.

[0015] Figure 3 A detailed flow chart of step S5 of an embodiment is shown. DETAILED DESCRIPTION

[0016] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0017] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0018] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0019] To achieve this, please refer to Figures 1 to 3 The present invention provides a method for optimizing the security performance of a VLC relay system, the method comprising the following steps:

[0020] Step S1: Identify the risks of the target VLC relay system to obtain a potential attack risk model of the system;

[0021] Specifically, for example, a comprehensive system survey can be conducted on the target VLC relay system to understand the system's structure, functions, communication protocols, and security mechanisms. Collect and analyze threat intelligence related to optical communications and network security, including known attack techniques, vulnerability information, malicious code, etc. A threat intelligence platform or tool can be used to obtain relevant information. Use vulnerability scanning tools (such as Nessus, OpenVAS, etc.) to scan the target VLC relay system for vulnerabilities and identify known vulnerabilities and weaknesses that may exist in the system. Based on the system structure and vulnerability assessment results, use threat modeling techniques (such as STRIDE model, attack tree, etc.) to model the system for threats and identify potential attack risks that the system may face. Based on the results of threat modeling, evaluate the probability and impact of each potential attack risk to form a system potential attack risk model. Risk matrix or quantitative risk assessment methods can be used to quantify risks.

[0022] Step S2: Based on the system potential attack risk model, a penetration combat simulation is performed on the target VLC relay system through simulation testing to obtain system security vulnerability analysis data; based on the system security vulnerability analysis data, a security protection algorithm is evaluated on the target VLC relay system to obtain a system security protection solution design report;

[0023] Specifically, for example, a penetration test plan can be designed based on the system potential attack risk model, including test targets, attack paths, and attack methods. Use penetration testing tools such as Kali Linux, Metasploit, etc. to conduct penetration tests on the target VLC relay system to simulate the behavior of real attackers. Collect security vulnerability data during the penetration test, including the type of vulnerabilities found, vulnerability levels, and possible attack impacts. Analyze security vulnerability data, evaluate the degree of harm and potential impact of the vulnerabilities, and make corresponding repair suggestions. Based on the security vulnerability analysis data, evaluate the effectiveness and defects of the current security protection algorithm of the target VLC relay system, and make improvement suggestions. Write a system security protection plan design report, including security protection strategies, technical measures, and risk management suggestions to improve the security of the target VLC relay system.

[0024] Step S3: performing cascade multi-dimensional security coding on the target VLC relay system according to the system security protection scheme design report, thereby obtaining the cascade multi-dimensional security coding;

[0025] Specifically, for example, multidimensional coding technology can be used to encode data, and the data can be divided and processed according to multiple dimensions. Error correction coding, encryption algorithm and other technologies can be used to enhance the security and reliability of data. Multiple coding schemes are cascaded to form cascaded multidimensional security coding. Each coding scheme can be designed for different security requirements and attack scenarios, thereby improving the security of the system. According to the designed cascaded multidimensional security coding scheme, the target VLC relay system is securely coded. The data in the system is encoded according to different dimensions, and multiple coding schemes are cascaded to generate cascaded multidimensional security coding.

[0026] Step S4: Perform retinal identification and digital watermark extraction on the user to obtain the user's biometric data; use the biometric data to perform user-personalized double encryption on the cascaded multi-dimensional security code to obtain specific confidential communication encryption parameters;

[0027] Specifically, for example, a professional retinal scanning device can be used to scan the user's eyes to obtain retinal images or feature data. Extract the user's biometric information, such as blood vessel distribution, texture, etc., from the retinal image or feature data. Image processing and pattern recognition technologies, such as feature extraction algorithms, machine learning methods, etc., can be used to extract and represent biometrics. According to the results of retinal recognition and digital watermark extraction, the user's biometric data is obtained. The cascaded multi-dimensional security coding is obfuscated and encrypted using biometric data or keys. Symmetric encryption algorithms and asymmetric encryption algorithms can be combined to ensure the security and efficiency of encryption. Generate specific confidential communication encryption parameters based on the user's biometric data. The user's biometric information can be used as input, combined with encryption algorithms and key generation algorithms, to generate personalized encryption parameters related to the user. According to the results of the user's personalized double encryption, specific confidential communication encryption parameters are obtained.

[0028] Step S5: construct a distributed network attack and defense simulation platform; based on specific confidential communication encryption parameters, use middleman spoofing signals of different strengths to simulate attack tests on the target VLC relay system to obtain anti-tampering performance data; perform security performance evaluation on specific confidential communication encryption parameters according to the anti-tampering performance data to obtain an anti-attack optimization solution; dynamically update the key of specific confidential communication encryption parameters according to the anti-attack optimization solution.

[0029] Specifically, for example, a distributed network attack and defense simulation platform for simulating attack and defense tests can be built, specifically for visible light communication (VLC) systems. Virtualization technology and network simulation tools can be used to create a distributed environment, including attack hosts, defense hosts, and network connections. Man-in-the-middle attack technology is used to simulate the attacker's interference, tampering, or eavesdropping on the visible light communication link. Agent tools, deception services, etc. can be used to simulate man-in-the-middle attack scenarios. According to the attack target and test requirements, deception signals of different intensities and types are set, such as optical signal interference, spectrum stealing, etc. Relevant attack tools and technologies can be used, such as light source jammers, spectrum analyzers, etc. During the simulated attack test, the anti-tampering performance of the target VLC system is monitored and relevant data is recorded. The system's anti-attack capability, tampering detection rate, data integrity and other indicators can be evaluated. Based on the obtained anti-tampering performance data, the security performance of specific confidential communication encryption parameters is evaluated. Statistical analysis, machine learning and other methods can be used to analyze and process data and evaluate the security performance of encryption parameters. Based on the results of the security performance evaluation, an anti-attack optimization plan is proposed. According to the system's weaknesses and attack patterns, specific confidential communication encryption parameters can be adjusted to enhance the system's anti-attack capabilities. Methods such as cryptographic algorithm improvement and key management strategy optimization can be used to optimize the defense plan. According to the anti-attack optimization plan, the key of specific confidential communication encryption parameters is dynamically updated. Key negotiation protocols, key update mechanisms, etc. can be used to update keys regularly or based on event triggers to respond to potential security threats.

[0030] The present invention can fully understand the potential attack risks of the system, including security vulnerabilities that may lead to information leakage, data tampering and communication interruption, by identifying the risks of the target VLC relay system. Obtaining the system potential attack risk model helps to clarify the security threats that may exist in the system and provide guidance for subsequent security protection measures. Through simulation testing and penetration combat simulation, actual attack scenarios can be simulated, and security vulnerability analysis data of the system under different attack situations can be obtained. This helps to discover the weaknesses and security vulnerabilities of the system and provide a basis for subsequent security protection schemes. Based on the system security vulnerability analysis data, the target VLC relay system is evaluated for security protection algorithms to determine a security protection scheme suitable for the system. This helps to select appropriate technologies and methods to improve the security performance of the system. The security performance of the target VLC relay system can be enhanced by the application of cascaded multidimensional security coding. Cascaded multidimensional security coding can provide multiple protection levels, encrypt and protect communication data, and prevent information leakage and data tampering. The application of cascaded multidimensional security coding can increase the difficulty for attackers to obtain communication content, improve the confidentiality and integrity of the system, and thus protect the security of communication. By performing retinal recognition and digital watermark extraction on users, biometric data of users can be obtained. This personalized biometric data can be used for user authentication and communication encryption to increase the security of the system. Using the user's biometric data to perform personalized double encryption on the cascaded multi-dimensional security code can provide higher confidentiality for the communication data. In this way, even if the attacker obtains the encrypted data, it is difficult to decrypt and destroy the integrity of the data. Building a distributed network attack and defense simulation platform can simulate actual attack scenarios and evaluate the security performance of the target VLC relay system. This helps to discover the weaknesses and vulnerabilities of the system under different attack situations and provide a basis for improving defense strategies. By simulating attack tests and using middleman spoofing signals of different strengths, the tamper-proof performance of the target VLC relay system can be evaluated. This helps to determine the system's anti-attack capabilities and provide directions for improving defense strategies. By evaluating the security performance of specific confidential communication encryption parameters based on tamper-proof performance data, the effectiveness and security of the encryption parameters can be determined. This helps to optimize the system's security protection plan and improve the confidentiality and integrity of communication data. The security of the system can be enhanced by dynamically updating the keys of specific confidential communication encryption parameters. Dynamically updating the keys can prevent key leakage and cracking and maintain the confidentiality and integrity of communications. In summary, the present invention can improve the security performance of the VLC relay system, reduce the risks of information leakage and data tampering, and ensure the confidentiality, integrity and reliability of communications.

[0031] Preferably, step S1 comprises the following steps:

[0032] Step S11: Acquire communication protocol mode data of the target VLC relay system;

[0033] Specifically, for example, communication data packets of the target VLC relay system can be collected and captured by a network packet capture tool such as Wireshark. The captured communication data packets are analyzed to extract the communication protocol information therein. Protocol analysis tools such as tcpdump, tshark, etc. are used to parse the data packets to obtain information such as protocol type, message format, field definition, etc.

[0034] Step S12: performing network structure topology restoration on the target VLC relay system, thereby obtaining system network topology data; performing precise positioning of node deployment positions on the target VLC relay system according to the system network topology data, thereby obtaining system node geographic distribution data;

[0035] Specifically, for example, network device information of the target VLC relay system can be collected, including switches, routers, relay nodes, etc. Use network scanning tools such as Nmap, Zenmap, etc. to scan the target network and obtain the IP address, MAC address and open port information of the network device. Based on the scanned data, draw a network topology diagram of the target VLC relay system to identify the connection relationship between each network device. According to the network topology diagram and the physical location information of the device, the node deployment location is accurately located, and the location information of the device can be recorded manually by using a GPS positioning device. Integrate the physical location information of the node and the network topology data, establish the node geographic distribution data of the target VLC relay system, and describe the location and relationship of each node in the system.

[0036] Step S13: extracting network features of the target VLC relay system according to the communication protocol mode data, system network topology data and system node geographic distribution data of the target VLC relay system, thereby obtaining network feature data of the VLC relay system;

[0037] Specifically, for example, the communication protocol mode data, network topology data and node geographic distribution data of the target VLC relay system can be integrated. Based on the communication protocol mode data, the characteristics of the communication protocol are extracted, such as the protocol type, message format, field value range, etc. Based on the network topology data, the characteristics of the network topology are extracted, such as the number of nodes, connection relationship, network hierarchy, etc. Based on the node geographic distribution data, the geographical characteristics of the node are extracted, such as the node location coordinates, geographical distance, etc. The extracted communication protocol characteristics, network topology characteristics and node geographic characteristics are integrated into the network feature data of the VLC relay system for subsequent analysis and modeling. Data analysis tools such as Python's data processing library (e.g., Pandas) and feature extraction algorithms (e.g., principal component analysis PCA) can be used to process and extract network feature data.

[0038] Step S14: performing correlation feature matching and collection on a known VLC system vulnerability technology library and an attack technology library based on the VLC relay system network feature data, thereby obtaining candidate vulnerability exploitation solution data;

[0039] Specifically, for example, known VLC system vulnerability technology libraries and attack technology libraries can be collected, which can contain publicly disclosed vulnerability information, known attack methods and tools, etc. Based on the network feature data of the VLC relay system, features related to known vulnerabilities and attacks are extracted, such as protocol version, device model, open ports, etc. The network feature data of the VLC relay system is associated with the vulnerability technology library and the attack technology library using feature matching algorithms (such as string matching, pattern matching, etc.) to find vulnerabilities and attack solutions that match the system features. Vulnerability exploitation solution data related to the matched features is collected, including known vulnerability exploitation codes, attack tools and methods, etc.

[0040] Step S15: Simulate security threat scenarios for the target VLC relay system based on the candidate vulnerability exploitation scenario data, thereby obtaining a potential attack risk model for the system.

[0041] Specifically, for example, some representative vulnerability exploitation scenarios and attack methods can be selected based on the candidate vulnerability exploitation scenario data. A test environment similar to the target VLC relay system is built in a security experimental environment, including the same network topology, device configuration, and software version. Use vulnerability exploitation tools and attack methods to simulate attacks on the target VLC relay system to reproduce the attack scenarios in the candidate vulnerability exploitation scenarios. Monitor and record key information during the attack, including attack success rate, attack path, affected system components, etc. Based on the results of the simulated attack, analyze the potential attack risks of the system, and evaluate the impact of the vulnerability on the system and the possible security consequences. Based on the security threat scenario simulation data obtained from the analysis, a potential attack risk model for the target VLC relay system is established to guide the formulation of subsequent security protection and risk management measures.

[0042] The present invention provides a basis for subsequent security analysis by understanding the communication protocol mode of the target VLC relay system, including data transmission mode, protocol specification, etc. By analyzing the protocol mode data, the types of security vulnerabilities and attack methods that may exist in the system can be determined, providing direction for subsequent vulnerability analysis. By obtaining the network structure topology information of the target VLC relay system, including the connection mode between nodes, topology type, etc., it is helpful to understand the overall architecture of the system. Accurately locating the node deployment position and obtaining the node geographical distribution data are helpful to analyze the geographical correlation problems in the system, such as the distance between nodes, signal strength, etc., and provide a basis for subsequent security assessment. By extracting the network feature data of the VLC relay system, including the attributes, connection relationship, geographical location, etc. of the nodes, it is used to analyze the security features and potential attack surfaces of the system. Through network feature extraction, security vulnerabilities, weak links and possible attack paths in the system can be identified, providing a basis for subsequent vulnerability analysis and security protection. Based on the correlation feature matching of network feature data and known vulnerability technology library and attack technology library, the possible vulnerability exploitation scheme of the system can be identified. Collecting candidate vulnerability exploitation scheme data is helpful to understand the potential attack risks and threat scenarios of the system, and provides a basis for subsequent security assessment and protection strategy formulation. By simulating security threat scenarios, the security performance and weaknesses of the system under different attack scenarios can be evaluated. Based on the simulation results, a potential attack risk model of the system is constructed, which helps to identify the system's vulnerabilities, key risk areas, and possible attack paths. Through simulation analysis, security risks in the system can be discovered early, and corresponding security measures can be taken to improve the overall security of the system. The present invention helps to discover security vulnerabilities in the system, evaluate the security performance of the system, and identify potential attack risks, thereby providing guidance for formulating targeted security protection measures and improving the security and reliability of the VLC relay system.

[0043] Preferably, step S14 comprises the following steps:

[0044] Step S141: performing principal component analysis and dimensionality reduction on the VLC relay system network characteristic data, thereby obtaining a system network characteristic principal component matrix;

[0045] Specifically, for example, the network feature data of the VLC relay system can be constructed into a feature matrix, in which each row represents a sample (such as a network node or device) and each column represents a feature. The feature matrix is ​​reduced in dimension using the principal component analysis (PCA) algorithm. Data analysis tools such as the PCA algorithm in the scikit-learn library of Python can be used for implementation. The PCA algorithm is executed to extract the principal components of the system network features and obtain a principal component matrix. Each column of the principal component matrix represents a principal component, which is arranged in descending order of feature variance. According to the set dimension reduction dimension or the proportion of principal components retained, it is determined how many principal components are retained as the main representation of the network features.

[0046] Step S142: performing kernel clustering extraction on the main component matrix of the system network features, thereby obtaining a core feature set of the system network;

[0047] Specifically, for example, a kernel clustering algorithm can be used to perform cluster analysis on the principal component matrix of the system network features, and the features in the principal component matrix can be clustered. An appropriate kernel function and clustering algorithm, such as a Gaussian kernel function and a spectral clustering algorithm, are selected to cluster the features in the principal component matrix. The spectral clustering algorithm in the scikit-learn library of Python can be used for data analysis tools. In the clustering process, the features in the principal component matrix are grouped according to the clustering results to obtain a core feature set of the system network, each set representing a clustering cluster containing network features with similar characteristics.

[0048] Step S143: using the system network core feature set to calculate the correlation between the known VLC system vulnerability technology library and the attack technology library, thereby obtaining a candidate vulnerability-attack solution group; mining the evolution chain and propagation channel of the candidate vulnerability-attack solution group, thereby obtaining solution technology diffusion link data;

[0049] Specifically, for example, the core feature set of the system network can be correlated with the features in the known VLC system vulnerability technology library and attack technology library to measure the correlation between the core features of the system network and the vulnerabilities and attacks. An association analysis algorithm such as the Apriori algorithm or the association rule mining algorithm can be used to find the correlation between the features. Based on the results of the correlation calculation, the vulnerabilities and attack schemes that are highly correlated with the core features of the system network are screened out to form a candidate vulnerability-attack scheme group. The candidate vulnerability-attack scheme group is mined for evolutionary chains and propagation channels to analyze the propagation paths and evolutionary relationships between the vulnerabilities and attack schemes. Data analysis tools such as graph theory algorithms and social network analysis algorithms can be used to identify the diffusion links and propagation channels of the scheme technology. Organize and record the diffusion link data of the scheme technology, including key information such as the propagation path of the attacker's exploitation of the vulnerability and the propagation path of the attack technology. These data can be used for subsequent attack risk assessment and the formulation of security protection measures.

[0050] Step S144: Based on the candidate vulnerability-attack scheme group, the scheme technology diffusion link data is reconstructed using the connection analysis method, and the blood relationship of the candidate vulnerability-attack scheme group is corrected and reconstructed by combining the attack and defense evolution history analysis technology, so as to obtain a candidate vulnerability exploitation chain scheme library;

[0051] Specifically, for example, a connection analysis method can be used to establish a graph model of solution technology diffusion link data. Each solution technology is represented as a node in the graph, and the propagation relationship between solution technologies is represented as an edge in the graph. According to the solution technology diffusion link data, the solution technology is reconstructed and clustered using graph analysis algorithms such as graph clustering algorithms and community discovery algorithms. Graph theory libraries such as NetworkX can be used for graph analysis and clustering. Combined with the attack and defense evolution history analysis technology, the blood relationship of the candidate vulnerability-attack solution group is reconstructed by correcting the correlation strength. By analyzing the historical records of vulnerability exploitation solutions, the behavior patterns of attackers, and the evolution of security defense measures, the correlation strength between solutions is corrected to more accurately reflect the blood relationship between solutions. Based on the reconstructed and corrected solution technology diffusion link data, a candidate vulnerability exploitation chain solution library is established, which contains various vulnerability exploitation chain solutions and their blood relationship information.

[0052] Step S145: Evaluate the possibility of successful penetration of each solution in the candidate vulnerability exploit chain solution library, so as to obtain a solution penetration effectiveness data set;

[0053] Specifically, for example, for each solution in the candidate vulnerability exploit chain solution library, a successful penetration possibility assessment can be performed. The goal of the assessment is to measure the possibility of each solution successfully penetrating the target system. Based on the vulnerabilities, attack techniques, defense measures and other factors involved in the solution, combined with the security status and configuration information of the system, the penetration effectiveness of the solution is quantitatively analyzed using evaluation methods and models. The assessment can be based on a knowledge base, empirical rules or machine learning algorithms. Taking into account the success probability, difficulty and dependency of each link in the solution, the possibility of successful penetration of each solution is comprehensively assessed, and the penetration effectiveness data of the solution is recorded.

[0054] Step S146: Optimize and screen the candidate exploit chain solution library according to the solution penetration efficiency data set to obtain candidate vulnerability exploitation solution data.

[0055] Specifically, for example, the solutions in the candidate vulnerability exploit chain solution library can be sorted and screened based on the solution penetration effectiveness data set. The solutions can be sorted according to the probability of successful penetration based on the evaluation results of the penetration effectiveness. According to actual needs and resource constraints, the screening threshold or requirements are determined, and efficient solutions that meet the conditions are selected as candidate vulnerability exploitation solution data. The feasibility and implementation difficulty of the screened candidate vulnerability exploitation solutions are evaluated to ensure the effectiveness and operability of the solutions. Finally, the candidate vulnerability exploitation solution data is obtained, which contains feasible and efficient vulnerability exploitation solutions that have been optimized and screened, which can be used for further penetration testing and attack path planning.

[0056] The present invention can reduce the dimension of network feature data, remove redundant information, and extract the most representative main features through principal component analysis dimensionality reduction. By obtaining the system network feature principal component matrix, it is helpful to simplify the data analysis process and improve the efficiency and accuracy of subsequent processing. Through kernel clustering extraction, the core feature set in the network feature principal component matrix, that is, the most representative and relevant features, can be identified. By obtaining the system network core feature set, it is helpful to focus on the key features of the system and improve the pertinence and accuracy of subsequent analysis. Through correlation calculation, the correlation degree between the system network core feature set and the known vulnerability technology library and the attack technology library is determined, and the possible vulnerability-attack scheme group is found. Mining the scheme technology diffusion link data can analyze the propagation path and evolution chain of the attack scheme, understand the diffusion of vulnerability exploitation and the propagation channel of the attack technology. By reconstructing the scheme technology diffusion link data through the connection analysis method, the propagation path and correlation relationship of the attack scheme can be restored, and the blood relationship between the schemes can be provided. Combined with the attack and defense evolution history analysis technology, the correlation strength of the candidate vulnerability-attack scheme group is corrected to improve the accuracy and credibility of the scheme library. By obtaining a library of candidate vulnerability exploit chain solutions, it is helpful to comprehensively analyze the exploitation methods, attack paths and attack means of the vulnerabilities, and provide a basis for subsequent security assessments and protective measures. By evaluating the possibility of successful penetration of each solution in the candidate vulnerability exploit chain solution library, the actual attack effect and success rate of each solution can be evaluated. Obtaining a solution penetration effectiveness data set helps to compare and screen solutions with higher attack success potential, and provide a basis for security protection strategies. According to the solution penetration effectiveness data set, the candidate vulnerability exploit chain solution library is optimized and screened, and solutions with higher attack success potential are selected. Obtaining candidate vulnerability exploitation solution data helps to determine the optimal vulnerability exploitation strategy and attack solution, and improve the efficiency and success rate of the attack. The present invention can help security experts and researchers better understand and analyze the vulnerabilities and attack characteristics of the system, thereby providing a scientific basis and guidance for security assessments, protective measures and vulnerability repairs.

[0057] Preferably, in step S145, the penetration success rate evaluation calculation formula is used to evaluate the possibility of successful penetration of each solution in the candidate vulnerability exploit chain solution library, wherein the penetration success rate evaluation calculation formula is as follows:

[0058]

[0059] Where P s is the penetration success rate, e is the base of the natural logarithm, x is time, t is the duration of the attack, f(x) is the change of the attacker's attack intensity over time, n is the number of terms in the infinite series, i is the i-th term in the infinite series, π is pi, V is the number of vulnerabilities in the system, and A is the number of attack techniques of the attacker.

[0060] The present invention constructs a penetration success rate evaluation calculation formula, in which the first step is to calculate This is to find the definite integral of the function f(x) on the interval [0, t], which represents the cumulative effect of the attacker's attack intensity during this period of time. It can reflect the impact of the attacker's attack capability and strategy on the penetration success rate; it can increase the complexity and randomness of the formula, making the penetration success rate difficult to predict or crack; it can also avoid the impact of the absolute value of the attack intensity on the penetration success rate, but consider the relative change of the attack intensity. The second step is to calculate This is the derivative of the result of the first step with respect to x, which represents the rate of change of the attacker's attack strength. It can reflect the impact of the change of the attacker's attack strength on the penetration success rate; the third step is to calculate This is the negative exponent of the base of the natural logarithm of the result of the second step, which represents the exponential decay of the rate of change of the attacker's attack strength. It can map any range of values ​​to between (0, +∞), thereby representing an exponential factor; it can reflect that the greater the rate of change of the attacker's attack strength, the smaller the exponential factor, and the higher the penetration success rate, which is in line with the actual situation; it can also avoid the situation where the rate of change of the attack strength is negative or zero, ensuring the rationality of the formula. The fourth step is to calculate This is the result of step 3 plus 1, which represents the exponential attenuation of the rate of change of the attacker's attack strength. It can map any range of values ​​to (1, +∞), thereby representing a translation factor; it can reflect that the greater the rate of change of the attacker's attack strength, the closer the translation factor is to 1, and the higher the penetration success rate, which is in line with the actual situation; it can also avoid the situation where the exponential factor is equal to 1, ensuring the rationality of the formula. Step 5 calculates This is the inverse of the result of the fourth step, which represents the inverse of the exponential decay of the rate of change of the attacker's attack strength. It can map any range of values ​​to between (0, 1), thereby representing a probability value; it can reflect that the greater the rate of change of the attacker's attack strength, the closer the probability value is to 1, and the higher the penetration success rate, which is in line with the actual situation; it can also avoid the situation where the translation factor is equal to 0, ensuring the rationality of the formula. The sixth step is to calculate This is the sine function The summation over the interval [1, n] represents the periodic cumulative effect of the attacker's attack duration. It can reflect the impact of the attacker's attack duration on the penetration success rate; it can also avoid the impact of the absolute value of the attack duration on the penetration success rate, but consider the relative change of the attack duration. Step 7 This is the result of step 6 divided by n, which represents the periodic average effect of the attacker's attack duration. It can map any range of values ​​to (-1, 1) to represent an average factor; it can reflect that the longer the attacker's attack duration, the closer the average factor is to -1, and the higher the penetration success rate, which is in line with the actual situation; it can also avoid the situation where the periodic cumulative effect is too large or too small, ensuring the rationality of the formula. Step 8 calculates This is the result of step 7 minus 1, which represents the periodic shift effect of the attacker's attack duration. It can map any range of values ​​to between (0, 2) to represent a shift factor; it can map any range of values ​​to between (0, 2) to represent a shift factor; it can avoid the situation where the average factor is equal to 1, ensuring the rationality of the formula. Step 9 calculates This is the result of step 8. When n tends to infinity, the limit is found, which indicates the limit effect of the attacker's attack duration. It can map any range of values ​​to a certain value, thereby indicating a limit factor; it can reflect that the longer the attacker's attack duration, the closer the limit factor is. The higher the penetration success rate, the more in line with the actual situation; it can also avoid the situation where the translation factor appears infinite or infinitesimal, ensuring the rationality of the formula. This is the natural logarithm of the ratio of the number of system vulnerabilities to the number of attackers’ attack techniques, which represents the logarithmic ratio of the system’s vulnerability level to the attacker’s attack capability. This can reflect the impact of the ratio of the number of system vulnerabilities to the number of attackers’ attack techniques on the penetration success rate; it can avoid the impact of the absolute values ​​of the number of system vulnerabilities and the number of attackers’ attack techniques on the penetration success rate, and instead consider their relative ratio. Step 11: By calculating This is the square root of the result of the tenth step, which represents the square root effect of the logarithmic ratio of the system's vulnerability level and the attacker's attack capability. It can reflect that the greater the logarithmic ratio of the system's vulnerability level and the attacker's attack capability, the greater the square root effect, and the higher the penetration success rate, which is in line with the actual situation; this formula can calculate the penetration success rate based on some system and attacker characteristics, that is, the probability that the attacker can successfully invade the system; it can comprehensively consider the impact of the attacker's attack intensity, attack duration, number of system vulnerabilities and number of attacker's attack techniques on the penetration success rate, which is in line with the actual situation; it can ensure that the value of the penetration success rate is between (0, 1), which is in line with the definition of probability; it can increase the complexity and randomness of the formula, making the penetration success rate not easy to predict or crack. It can avoid some unreasonable or impossible situations, such as the penetration success rate being negative, greater than 1, equal to 0 or 1, etc., to ensure the rationality of the formula.

[0061] Preferably, step S2 comprises the following steps:

[0062] Step S21: Designing a directional threat penetration scheme for the target VLC relay system according to the system potential attack risk model, thereby obtaining a directional threat penetration test scheme;

[0063] Specifically, for example, a targeted threat penetration plan can be designed based on the analysis results of the system's potential attack risk model. The plan should include specific attack paths, attack techniques, attack tools, etc. to meet the needs and requirements of the test objectives. The plan design can be combined with penetration testing methods and frameworks such as the OWASP (Open Web Application Security Project) test guide.

[0064] Step S22: Based on the directional threat penetration test solution, a high-fidelity network environment simulation reconstruction is performed on the target VLC relay system to obtain a VLC test sample;

[0065] Specifically, for example, a high-fidelity network environment simulation can be constructed according to a targeted threat penetration test solution. A simulation environment can be built using virtualization technologies such as VMware, VirtualBox, or container technologies such as Docker. Reproduce the network topology, operating system, application programs, and other components of the target VLC relay system in the simulation environment, as well as other systems and network devices that interact with it. For each component in the simulation environment, configure and adjust its parameters, security settings, and vulnerability reproduction environment to be as close to the real environment as possible to ensure the accuracy and reliability of the penetration test. Establish a VLC test bench, that is, the target VLC relay system in the simulation environment, for subsequent penetration testing and attack simulation.

[0066] Step S23: setting anti-tampering monitoring watermarks for key test nodes of the VLC test sample, thereby obtaining intelligent protection intermediate nodes;

[0067] Specifically, for example, tamper-proof monitoring watermarks can be set for key test nodes in the VLC test bench. Monitoring watermarks are a technology used to detect and track system tampering. Specific identification information or data can be inserted at key nodes to monitor the integrity and security of the nodes. Select appropriate tamper-proof monitoring watermark technologies, such as hash chains, digital signatures, data integrity checks, etc., and apply them to key test nodes. For each key test node, implement tamper-proof monitoring watermark settings, and configure corresponding monitoring and alarm mechanisms, as well as data collection and analysis tools, so as to promptly detect and respond to tampering behavior at any node. Establish intelligent protection intermediate nodes, that is, key test nodes with tamper-proof monitoring watermarks, which are used to monitor and protect the security of the target system during penetration testing.

[0068] Step S24: performing behavior reproduction analysis and identification on the target VLC relay system, thereby obtaining a VLC system risk scenario data set;

[0069] Specifically, for example, the behavior and operation process of the target VLC relay system can be observed and recorded in the VLC test sample, including the behavior in terms of system startup, network communication, user interaction, etc. Use behavior reproduction tools or technologies, such as system monitoring tools, network packet capture tools, etc., to record and analyze the behavior of the target VLC relay system. Tools such as Wireshark, tcpdump, etc. can be used to capture and analyze network traffic. Behavioral data is identified and analyzed to identify normal and abnormal behaviors and possible risk scenarios. Machine learning algorithms such as anomaly detection algorithms and rule matching can be used for data analysis and identification. Based on the identification results, a VLC system risk scenario data set is constructed to organize and record information such as risk scenarios, possible attack paths, and attacker behaviors for subsequent risk assessment and security protection strategy formulation.

[0070] Step S25: constructing a distributed simulation test cluster environment for the intelligent protection intermediate node and the VLC system risk scenario data set to generate a configurable simulation test platform;

[0071] Specifically, for example, a distributed simulation test cluster environment can be built based on the intelligent protection intermediate node and the VLC system risk scenario data set. The test cluster environment can be built using virtualization technologies such as VMware, VirtualBox, or container technologies such as Docker. In the test cluster environment, multiple simulation nodes are deployed to simulate the behavior and interaction of attackers, victims, and other network devices to generate more realistic test scenarios. Configure the network topology of the cluster environment to ensure that the communication and interaction between nodes conform to the actual situation, and consider factors such as network latency and bandwidth limitations to improve the accuracy and reliability of the simulation. To support configuration flexibility, automated deployment tools such as Ansible or orchestration tools such as Kubernetes can be used to manage and configure the test cluster environment so that rapid configuration changes and expansions can be made as needed.

[0072] Step S26: Based on the configurable simulation test platform, an active defense type directional threat combat simulation is performed on the target VLC relay system according to the intelligent protection intermediate node, so as to obtain simulation test result data;

[0073] Specifically, for example, an active defense-type directional threat combat simulation can be performed on a configurable simulation test platform according to the functions and configurations of the intelligent protection intermediate node. This can include simulating the attacker's attack behavior, the analysis and response of threat intelligence, the triggering and execution of defense mechanisms, etc. Configure the parameters and behaviors of each node in the simulation test platform to simulate the attack and defense scenarios in a real environment. Tools such as Metasploit, Nmap, etc. can be used for attack simulation and scanning, and defense tools such as Snort, Suricata, etc. can be used for threat detection and blocking. Perform a directional threat combat simulation and record the simulation test result data, including indicators such as attack success rate, defense effect, response time, etc., to evaluate the performance and effectiveness of the intelligent protection intermediate node. Log analysis tools such as ELK (Elasticsearch, Logstash, Kibana) can be used to collect, analyze and visualize the simulation test result data for subsequent result analysis and evaluation.

[0074] Step S27: performing system security vulnerability matching detection on the simulation test result data, thereby obtaining a system security vulnerability association list;

[0075] Specifically, for example, the simulation test result data can be used to match system security vulnerabilities, and the abnormal behaviors and attack traces found during the test can be matched with known system security vulnerabilities. Use vulnerability scanning tools such as Nessus and OpenVAS to scan the test result data to identify known vulnerabilities in the system. Based on the scan results, a system security vulnerability association list is generated, including the name, type, level, impact range and other information of the vulnerability, which is used for subsequent security protection strategy formulation and vulnerability repair work.

[0076] Step S28: Perform an adaptive security protection algorithm evaluation on the target VLC relay system according to the system security vulnerability association list, so as to obtain vulnerability corresponding protection model data; perform hierarchical security implementation improvement on the target VLC relay system according to the vulnerability corresponding protection model data, so as to obtain a system security protection solution design report.

[0077] Specifically, for example, each vulnerability can be evaluated and analyzed based on the system security vulnerability association list. Vulnerabilities can be scored using vulnerability assessment tools such as CVSS (Common Vulnerability Scoring System) to determine the severity and potential impact of the vulnerability. Based on the vulnerability assessment results, appropriate protection algorithms and models are selected. Machine learning algorithms such as decision trees, support vector machines, etc., or deep learning algorithms such as neural networks, convolutional neural networks, etc. can be used to build protection models corresponding to the vulnerabilities. According to the selected protection model, hierarchical security implementation improvements are made to the target VLC relay system. This includes implementing corresponding protection measures in the system, such as strengthening access control, implementing encrypted communications, updating patches and security configurations, etc. For each vulnerability, a corresponding protection plan is formulated, including specific operating steps and suggestions to ensure the security and reliability of the system. A system security protection plan design report can be written to describe the protection strategy and implementation details of each vulnerability in detail for subsequent reference and implementation.

[0078] The present invention designs a directional threat penetration scheme according to a system potential attack risk model, and performs targeted penetration testing on a target VLC relay system. Obtaining a directional threat penetration test scheme is helpful to identify potential vulnerabilities and weaknesses in the system, evaluate the security of the system, and provide guidance for subsequent security protection measures. By performing high-fidelity network environment simulation reconstruction, the characteristics and behaviors in the real network environment can be restored, and the working state of the target VLC relay system can be accurately simulated. A VLC test sample is obtained to provide a controllable and repeatable test environment for performing penetration testing and evaluating the security of the system. By setting an anti-tampering monitoring watermark at a key test node, the state and data integrity of the node can be monitored to improve the security and credibility of the test environment. An intelligent protection intermediate node is obtained as a security protection layer in the test environment to monitor and defend against potential attacks, thereby improving the accuracy and reliability of the penetration test. By performing a behavior reproduction analysis on the target VLC relay system, the operating behavior of the system can be restored and potential risk scenarios in the system can be identified. Obtaining a VLC system risk scenario data set is helpful to discover vulnerabilities, security risks and attack paths in the system, and provide a basis for security protection. By building a distributed simulation test cluster environment, complex network environments and attack scenarios can be simulated to improve the authenticity and reliability of the test. A configurable simulation test platform is generated to execute multiple test schemes and security protection algorithm evaluations, providing an experimental basis for the security improvement of the system. Through active defense-type directional threat combat simulation, the defense effect and response capability of the intelligent protection intermediate node to the target VLC relay system can be evaluated. The simulation test result data is obtained to analyze and evaluate the security performance of the system and guide the subsequent security protection strategy formulation and improvement. By performing security vulnerability matching detection on the simulation test result data, the security vulnerabilities and weaknesses in the system can be identified. A system security vulnerability association list is obtained to provide a system vulnerability list to provide guidance and priority sorting for subsequent security improvements and vulnerability repairs. The effectiveness and adaptability of the security protection algorithm of the target VLC relay system are evaluated based on the system security vulnerability association list. The vulnerability corresponding protection model data is obtained to provide protection strategies and models for different vulnerabilities to provide guidance for the security improvement of the system. By performing hierarchical security implementation improvements, the system security protection plan is designed and implemented according to the protection model corresponding to the vulnerability. Generate a system security protection scheme design report, record improvement measures and suggestions, and provide comprehensive evaluation and guidance for the overall security of the system. In summary, the present invention can obtain the security assessment results, vulnerability list and security protection scheme design report of the target VLC relay system, providing useful guidance and basis for the security improvement and protection of the system.

[0079] Preferably, step S241 includes the following steps:

[0080] Step S241: Acquire a standardized general VLC relay system risk data set;

[0081] Specifically, for example, the existing standardized general VLC relay system risk data set can be collected and organized. Relevant data can be obtained by investigating known VLC relay system vulnerabilities, security incidents, attack patterns, etc., and referring to relevant security standards and guidelines. Use vulnerability databases such as CVE (Common Vulnerabilities and Exposures) and NVD (National Vulnerability Database) to obtain publicly available VLC relay system vulnerability information. Considering the characteristics of the VLC system, some customized risk data collection can also be performed. For example, risk data in specific scenarios can be obtained by simulating attacks, vulnerability mining, etc., to enrich the content and diversity of the data set.

[0082] Step S242: Performing service process simulation recovery on the target VLC relay system, thereby obtaining a VLC system behavior operation data set;

[0083] Specifically, for example, the business process and functions of the target VLC relay system can be studied to understand its behavioral characteristics and operation methods. Based on existing documents, configuration files or system-related information, the business process simulation and restoration of the target VLC relay system can be performed. Simulation tools such as GNS3, NS-3, etc. can be used to build a simulation environment to simulate the operation and interaction process of the system. In the simulation environment, various business operations are performed to simulate the behavior of users, attackers or other systems, and the operation data of the system is recorded. These data may include system requests, responses, logs, events and other information.

[0084] Step S243: performing a weak link security assessment analysis on the target VLC relay system according to the VLC system behavior operation data set, thereby obtaining a customized risk attack surface data set;

[0085] Specifically, for example, the VLC system behavior operation data set can be analyzed to identify weak links, that is, possible weaknesses and security risks in the system. Use weak link assessment tools such as OWASP Top 10 and CWE (Common Weakness Enumeration) to conduct a weak link security assessment on the target VLC relay system. This can include vulnerability scanning, code review, security configuration auditing, and other means. Based on the assessment results, the weak links and potential attack surfaces of the system are determined. These customized risk and attack surface data are recorded as the basis for subsequent risk analysis and simulation testing.

[0086] Step S244: using the customized risk attack surface dataset to perform differential fusion injection on the standardized general VLC relay system risk dataset, thereby obtaining a VLC system risk scenario dataset.

[0087] Specifically, for example, the customized risk attack surface dataset can be differentially fused and injected with the normalized general VLC relay system risk dataset. This can be achieved through dataset merging, data mapping, data conversion, etc. In order to maintain the consistency and integrity of the data, the data needs to be normalized and standardized. Ensure that the format, fields, and meaning of the dataset are consistent for subsequent risk scenario analysis and simulation testing. Combined with the dataset after differential fusion injection, the VLC system risk scenario can be simulated and analyzed. This involves processes such as simulated attacks, risk assessment, and security protection strategy formulation to obtain more comprehensive system risk scenario data.

[0088] The present invention obtains a standardized general VLC relay system risk data set, which contains known, sorted and classified risk situations. The standardized risk data set can be used as a reference to help analyze and evaluate the potential risks of the target VLC relay system and provide basic data for subsequent security assessment and improvement. Through business process simulation recovery, the actual business operation behavior of the target VLC relay system can be restored, including key behaviors such as data flow, request and response. The VLC system behavior operation data set is obtained. These data are operation records generated by the system in actual operation, which help to deeply understand the behavioral characteristics and potential risks of the system. Based on the VLC system behavior operation data set, weak link security assessment analysis is performed to identify weaknesses and vulnerable links in the system. By obtaining a customized risk attack surface data set, the data set contains specific weaknesses and attack surfaces that may be attacked in the target VLC relay system, which helps to accurately assess the security status of the system. By differentially merging the customized risk attack surface data set and injecting it into the standardized general VLC relay system risk data set, a VLC system risk scenario data set is generated. The VLC system risk scenario data set covers specific risk scenarios in the target VLC relay system and provides a more detailed and comprehensive description of the security of the system. This dataset can be used for system risk assessment and security protection strategy formulation, and helps to repair vulnerabilities and optimize security measures for specific risk scenarios.

[0089] Preferably, step S3 comprises the following steps:

[0090] Step S31: Acquire VLC relay system security coding knowledge base data and target VLC relay system service model data;

[0091] Specifically, for example, the VLC relay system security coding knowledge base data can be collected, which can be obtained by studying relevant security standards, security documents, security vulnerability reports, security papers, etc. These data may include known security coding patterns, security protection measures, encryption algorithms, authentication mechanisms, and other information. The business model data of the target VLC relay system can be obtained by communicating with system developers and business personnel, analyzing the system's functional requirements documents, business process diagrams, system architecture, etc. These data can help understand the business characteristics and security requirements of the target system.

[0092] Step S32: classify and annotate the VLC relay system security coding knowledge base data, thereby obtaining a hierarchical security coding technology data set;

[0093] Specifically, for example, the VLC relay system security coding knowledge base data can be analyzed and sorted to identify different coding technologies and security measures. Different coding technologies are classified and labeled based on existing classification methods or custom classification standards. For example, the security coding knowledge base data can be classified according to encryption algorithms, access control, input verification, etc. Based on the results of classification and labeling, a hierarchical security coding technology data set is established. This data set can include information such as the name, description, application scope, advantages and disadvantages of various coding technologies to facilitate subsequent security performance evaluation and technical assessment.

[0094] Step S33: Perform security performance evaluation on various coding technologies in the hierarchical security coding technology data set, thereby obtaining technology evaluation matrix data;

[0095] Specifically, for example, appropriate performance evaluation methods and indicators can be selected based on the various coding technologies in the hierarchical security coding technology data set. For example, the speed, security performance, scalability and other indicators of the encryption algorithm can be considered for evaluation. For each coding technology, design a corresponding experiment or test environment to conduct performance evaluation. Performance testing tools such as JMeter, LoadRunner, etc. can be used to simulate performance under different load conditions. Collect experimental data and perform data analysis and processing. Generate technical evaluation matrix data based on the evaluation results of performance indicators. This data set can include performance scores, pros and cons comparisons and other information of various coding technologies, which will be used for subsequent generation of hierarchical security coding system framework data.

[0096] Step S34: Generate hierarchical security coding system framework data according to the technical assessment matrix data;

[0097] Specifically, for example, it can be assumed that the technology evaluation matrix data includes performance scores and pros and cons comparisons of different coding technologies. For example, the score of symmetric encryption algorithm A is 90, the score of asymmetric encryption algorithm B is 80, and the score of hash algorithm C is 70. According to the scoring results, the coding technologies are sorted and graded. For example, coding technologies with scores of 90 and above are classified as high-level, coding technologies with scores between 80 and 89 are classified as medium-level, and coding technologies with scores between 70 and 79 are classified as low-level. Design a hierarchical security coding system framework to divide coding technologies into corresponding levels or modules according to different security levels. For example, high-level coding technologies are placed in the core layer of the system, medium-level technologies are placed in the middle layer, and low-level technologies are placed in the peripheral layer. Generate hierarchical security coding system framework data, including descriptions of each level or module, included coding technologies, security levels and other information.

[0098] Step S35: performing hierarchical heterogeneous fusion framework matching on the hierarchical security coding system framework data based on the system network topology data, the business mode data of the target VLC relay system, and the communication protocol mode data, so as to obtain a coding protection framework solution;

[0099] Specifically, for example, the hierarchical heterogeneous fusion framework matching of the hierarchical security coding system framework can be performed according to the system network topology data, the business mode data of the target VLC relay system, and the communication protocol mode data. This includes matching and aligning the coding technology with the system's network topology, business mode, and communication protocol. Combining the results of matching and alignment, a coding protection framework solution is generated. The solution includes selecting appropriate coding technology, applying coding technology at different levels, defining configuration parameters of coding technology, etc.

[0100] Step S36: Perform security performance evaluation and analysis on the coding protection framework solution, thereby obtaining framework protection effectiveness scorecard data;

[0101] Specifically, for example, the indicators and methods for security performance evaluation can be determined based on the coding protection framework scheme. For example, the system's security performance, security protection effect, performance overhead and other indicators can be considered, and appropriate evaluation methods and tools can be selected. Design a security performance evaluation experiment or test environment, and set it according to the configuration parameters defined in the framework scheme. Performance testing tools, security assessment tools, etc. can be used for experiments or tests. Run experiments or tests to collect data. For example, collect system performance data, security event logs, attack attempt records, etc. Analyze and compare the collected data to calculate the protection effectiveness score of the framework. Statistical analysis methods, data mining techniques, etc. can be used for analysis. Generate framework protection effectiveness scorecard data, including scores of various evaluation indicators, comprehensive scores and other information.

[0102] Step S37: performing a multi-dimensional optimization search of the coding mechanism combination within the coding protection framework scheme, thereby obtaining coding mechanism fusion topology data;

[0103] Specifically, for example, a multidimensional optimization search space can be constructed based on the characteristics and performance indicators of the coding mechanism of the coding protection framework scheme. For example, the parameters of each coding mechanism are defined and ranged, such as the key length of the encryption algorithm, the hash function selection of the hash algorithm, etc. Multidimensional optimization search algorithms, such as genetic algorithms and particle swarm optimization algorithms, are applied to search and optimize the search space. The algorithm evaluates the pros and cons of each search solution based on a predefined fitness function, and performs an iterative search to find the optimal solution. In the multidimensional optimization search process, the coding mechanisms are combined and adjusted to obtain better performance and security effects. For example, different combinations of coding mechanisms can be tried, parameter configurations can be adjusted, etc. Based on the results of the optimization search, coding mechanism fusion topology data is generated, including information such as the selected coding mechanism combination and parameter configuration.

[0104] Step S38: Generate cascaded multi-dimensional security coding by performing distributed evolutionary coding on the coding mechanism fusion topology data according to the framework protection effectiveness scorecard data.

[0105] Specifically, for example, the framework protection effectiveness scorecard data can be used as the objective function to define the fitness function of distributed evolutionary coding. The fitness function can be calculated based on the various indicators and weights in the scorecard data to evaluate the pros and cons of the coding mechanism fusion topological data. Design the algorithm and process of distributed evolutionary coding. For example, a distributed version of the genetic algorithm can be used to divide the coding mechanism fusion topological data into multiple subpopulations, and perform evolutionary search through operations such as crossover and mutation. The coding mechanism fusion topological data is distributed to each subpopulation, each subpopulation performs evolutionary search independently, and information exchange and merging between populations are performed after a certain number of iterations. In the evolutionary search process, the coding mechanism fusion topological data is evaluated and selected according to the fitness function, excellent solutions are retained, and mutation and crossover operations are introduced to introduce new changes and diversity. After a certain evolutionary iteration, the optimal solution or a set of excellent solutions for cascaded multi-dimensional security coding is obtained. These solutions contain information about the combination of coding mechanisms, parameter configurations, and topological structures, and can be used as the final security coding solution.

[0106] The present invention obtains the VLC relay system security coding knowledge base data, which includes the existing security coding technology and experience knowledge. By obtaining the business model data of the target VLC relay system, it is used to better understand the business needs and communication mode of the system. By classifying and labeling the security coding knowledge base data, different types of security coding technologies can be classified and summarized. By obtaining the hierarchical security coding technology data set, which includes security coding technologies of different levels, it is helpful for subsequent security performance evaluation and framework design. By performing security performance evaluation on the hierarchical security coding technology, the security performance and applicability of various coding technologies can be evaluated. By obtaining the technical evaluation matrix data, it is used to compare and evaluate the advantages and disadvantages of different coding technologies, providing a basis for framework design. Based on the technical evaluation matrix data, the hierarchical security coding system framework data is generated, which includes a combination of coding technologies and security measures of different levels. The hierarchical security coding system framework data provides the guiding principles and framework structure of the system security design, which helps to achieve multi-level security protection of the system. By combining the system network topology data, the business model data of the target VLC relay system and the communication protocol mode data, the hierarchical heterogeneous fusion framework matching is performed on the hierarchical security coding system framework data. A coding protection framework scheme is obtained, which provides an adaptable and multi-level coding protection strategy based on the specific needs and conditions of the target system. The security performance and effectiveness of the framework are evaluated by performing a security performance evaluation and analysis on the coding protection framework scheme. Framework protection effectiveness scorecard data is generated to quantitatively evaluate and compare the coding protection framework scheme and guide the optimization and improvement of the framework. The coding protection framework scheme is subjected to a combined multi-dimensional optimization search of the coding mechanism within the framework to improve the effectiveness and security of the coding protection framework. Coding mechanism fusion topology data is obtained, which includes an optimized coding mechanism combination scheme, which can better ensure the security of the system. Based on the framework protection effectiveness scorecard data and the coding mechanism fusion topology data, distributed evolutionary coding is performed to generate cascaded multi-dimensional security coding. The generated cascaded multi-dimensional security coding scheme can comprehensively consider the security performance of the framework and the optimization of the coding mechanism, and provide more efficient and multi-level security protection. The present invention can effectively improve the security and performance of the system by comprehensively considering coding technologies, system requirements and security performance evaluations at different levels, and provide a feasible security coding solution for actual application scenarios.

[0107] Preferably, step S4 comprises the following steps:

[0108] Step S41: Acquire the user's eye image and user identity information data;

[0109] Specifically, for example, a device with a camera function, such as a smart phone, a video camera, etc., can be used. Through the camera of the device, the user can aim his eyes at the camera and capture an image. An existing camera application or a custom application can be used for image acquisition. User identity information can include name, ID number, biometric data, etc. It can be obtained by user input, scanning of ID card, etc.

[0110] Step S42: performing iris recognition extraction on the user's eye image to obtain iris feature parameters;

[0111] Specifically, for example, an applicable iris recognition algorithm may be selected, such as the Daugman algorithm, the SIFT algorithm, etc. These algorithms may extract iris features from eye images. The selected iris recognition algorithm is applied to extract iris feature parameters from the user's eye image. These feature parameters may be iris texture, pore distribution, etc. The extracted iris feature parameters are stored in an appropriate format, such as a binary file, a feature vector, etc. These parameters may be used for subsequent iris matching and recognition.

[0112] Step S43: Generate a biometric information watermark for the user identity information data, thereby obtaining the user digital watermark data;

[0113] Specifically, for example, a suitable biometric watermark generation algorithm can be selected, such as LSB (least significant bit) algorithm, discrete wavelet transform, etc. These algorithms can embed user identity information data into an image. Embed user identity information data into a specific image. You can select a user eye image as an embedding carrier, or you can select other images as carriers. According to the algorithm requirements, set appropriate embedding parameters, such as embedding strength, embedding position, etc. These parameters can affect the visibility and robustness of the watermark. Apply the selected biometric watermark generation algorithm to embed the user identity information data into the image to generate image data with a digital watermark.

[0114] Step S44: reversibly embedding the iris feature parameters according to the user digital watermark data, thereby obtaining the user biometric feature data;

[0115] Specifically, for example, the previously generated digital watermark data and the corresponding decoding algorithm can be used to decode the image with the digital watermark and extract the user's identity information data. The iris recognition algorithm is applied to extract the iris feature parameters from the user's eye image to obtain the original iris feature data. The reversible watermark embedding algorithm is used to embed the user's identity information data into the iris feature parameters. In this way, the user's biometric data contains the watermark of the identity information. According to the requirements of the embedding algorithm, appropriate embedding parameters are set, such as embedding strength, embedding position, etc. These parameters can affect the visibility and extraction quality of the watermark. After the watermark is embedded, the iris feature parameters contain the user's identity information data, that is, the user's biometric data.

[0116] Step S45: constructing a confidential communication coding framework for the user's biometric data, thereby obtaining confidential coding framework data;

[0117] Specifically, for example, an applicable confidential communication coding framework can be selected, such as AES (Advanced Encryption Standard), RSA (asymmetric encryption algorithm), etc. These coding frameworks can be used to transmit and store user biometric data confidentially. According to the requirements of the coding framework, set appropriate coding parameters, such as key length, encryption algorithm selection, etc. These parameters can affect the security and efficiency of the coding. According to the selected coding framework and parameters, the user biometric data is encoded to generate confidential coding framework data. These data are encrypted user biometric data. The generated confidential coding framework data is securely stored or transmitted to ensure the confidentiality and integrity of the user biometric data.

[0118] Step S46: Use the confidentiality coding framework data to perform user-personalized double encryption on the cascaded multi-dimensional security coding, thereby obtaining specific confidentiality communication encryption parameters.

[0119] Specifically, for example, specific confidential communication encryption parameters can be generated based on the user's identity information or other personalized requirements. These parameters may include encryption keys, encryption algorithm selection, encryption mode, etc. The confidential coding framework data and user personalized encryption parameters are applied to double encrypt the cascade multi-dimensional security coding. In this way, the user's data is protected at a higher level during transmission and storage. The generated specific confidential communication encryption parameters are securely stored or transmitted for subsequent data encryption and decryption.

[0120] The present invention obtains the user's eye image, which is the basic data for iris recognition and can be used to identify and extract the user's iris features. By obtaining the user's identity information data, the user's identity is associated with the iris features to ensure the accuracy and credibility of the subsequent processing process. By performing iris recognition extraction, unique iris feature parameters can be extracted from the user's eye image. The iris feature parameters have a high degree of individual difference and uniqueness and can be used as the basis for user identity authentication and identification. Through the generation of biometric information watermark, the user's identity information data is converted into digital watermark data. The digital watermark can be embedded in the iris feature parameters for subsequent confidential communication and authentication processes. By using the user's digital watermark data, the iris feature parameters are reversibly watermarked. The reversible watermark embedding technology can embed the digital watermark information into the feature parameters without changing the iris feature parameters themselves, thereby realizing the confidential transmission and authentication of biometric data. By constructing a confidential communication coding framework, it can be used to protect the secure transmission and storage of user biometric data. The confidential coding framework can provide security and privacy protection to ensure that the user's biometric data is not obtained and tampered with by unauthorized personnel. Through personalized double encryption, a higher level of security protection can be provided according to the user's specific security needs and encryption parameters, ensuring the security and credibility of the user's biometric data.

[0121] Preferably, step S5 comprises the following steps:

[0122] Step S51: building a distributed simulation platform including multiple network nodes, and performing software-defined network configuration on the distributed simulation platform, thereby obtaining a distributed network attack and defense confrontation simulation platform;

[0123] Specifically, for example, multiple visible light communication nodes can be created on a distributed simulation platform using virtual machines or container technology. Each node represents a visible light communication device or host. Tools such as VirtualBox, VMware, or Docker can be used to create and manage these nodes. Design the topology of the visible light communication network according to simulation requirements and attack and defense scenarios. Consider factors such as optical communication links, channel characteristics, and communication distances between nodes. Optical communication network simulation tools such as OpNet, NS-3, or customized simulation software can be used to help design and visualize the visible light communication network topology. Use software defined networking (SDN) technology to configure and manage the network of the distributed simulation platform. Select an SDN controller suitable for visible light communication, such as OpenDaylight or ONOS, and deploy it to the simulation platform. Through the SDN controller, the visible light communication nodes are centrally managed and programmed to achieve flexible configuration and control of network functions. Configure the parameters and protocols of the visible light communication nodes according to the requirements of the attack and defense scenarios. Consider factors such as modulation and demodulation technology, transmission rate, channel coding, etc. of the optical communication link. Optical communication modems, channel encoders, optical transmitters, and optical receivers can be configured on each node. By building multiple visible light communication nodes and configuring software-defined networks, a distributed network attack and defense simulation platform for simulating attacks on visible light communication systems is constructed.

[0124] Step S52: setting a deceptive relay node for the distributed network attack and defense confrontation simulation platform, thereby obtaining deceptive node parameter configuration data;

[0125] Specifically, for example, one or more nodes can be selected as deceptive relay nodes on a distributed simulation platform. These nodes can simulate attackers or malicious nodes to attack, interfere with or manipulate the communication traffic of the visible light communication system. Tools such as Scapy, Ettercap or custom scripts can be used to set up and manage deceptive relay nodes. An optical receiver is set on the deceptive relay node on the simulation platform to receive the optical signal sent by the target visible light communication system. An existing hardware optical receiver can be used, or the function of the optical receiver can be simulated in a simulation environment. The optical signal collected by the optical receiver is converted into a digital signal and stored in the form of a data stream or a data packet.

[0126] Step S53: generating tampering information for the target VLC relay system according to the spoofed node parameter configuration data, thereby obtaining a fake data injection stream;

[0127] Specifically, for example, the parameter configuration data obtained from the deceptive relay node can be analyzed to understand the working principle and communication protocol of the target VLC relay system. According to the parameter configuration data, tampering information is generated to deceive the target VLC relay system. According to specific needs, different methods or algorithms can be used to generate tampering information, such as modifying the content of the data packet, changing the communication protocol header, inserting malicious data, etc.

[0128] Step S54: Based on the distributed network attack and defense confrontation simulation platform, the data transmitted by the virtual target VLC relay system is reversibly visually encrypted using specific confidential communication encryption parameters, and the encrypted data is tampered with by a fake data injection stream, thereby obtaining a network attack and defense simulation data set;

[0129] Specifically, for example, specific confidential communication encryption algorithms and parameters can be configured according to simulation requirements and security requirements. Existing encryption algorithms such as AES, RSA, user biometric parameters, etc. can be used, and parameters such as key length, encryption mode, padding method, etc. can be customized as needed. The data transmitted by the virtual target VLC relay system is reversibly visually encrypted using the configured encryption algorithm and parameters. This can be achieved by applying the same encryption algorithm and parameters at the sending and receiving ends respectively. The encryption process can use encryption libraries or tools such as OpenSSL or custom encryption functions. The generated fake data injection stream is mixed with the encrypted data and injected into the target VLC relay system through a deceptive relay node. This can be achieved by modifying or tampering with the data packet content, changing the communication protocol header, etc. Network deception tools such as Scapy, Ettercap, or custom scripts can be used to perform fake data injection and man-in-the-middle tampering operations.

[0130] Step S55: performing anti-tampering performance evaluation on the network attack and defense simulation data set, thereby obtaining anti-tampering performance data;

[0131] Specifically, for example, an integrity algorithm (such as a hash algorithm) can be used to detect data to verify whether the data has been tampered with. Compare the hash value of the original data and the hash value of the received data. If they do not match, it means that the data has been tampered with. Evaluate the anti-tampering performance by analyzing the encryption and authentication mechanisms during data transmission. Cryptographic analysis methods and security assessment models such as information theory, cryptanalysis, and attack models can be used. Use machine learning or deep learning algorithms to train models to detect tampering in data sets. Common classification algorithms such as support vector machines (SVM), decision trees, random forests, or deep learning models such as convolutional neural networks (CNN) or recursive neural networks (RNN) can be used.

[0132] Step S56: Perform security performance evaluation on specific confidential communication encryption parameters according to the anti-tampering performance data, so as to obtain an anti-attack optimization solution;

[0133] Specifically, for example, based on the anti-tampering performance data, a specific set of confidential communication encryption parameters can be selected for encrypting and decrypting data. These parameters may include encryption algorithm, key length, encryption mode, padding method, etc. Perform security analysis on the selected encryption parameters to evaluate their protection capabilities against various attack methods. Cryptographic analysis methods such as key space analysis, differential attack, linear attack, etc. can be used. Evaluate the impact of the selected encryption parameters on computational complexity. The running time of the encryption algorithm, the complexity of the encryption and decryption process, and the impact on real-time communication can be analyzed. Prove the security of the selected encryption parameters to verify that they meet specific security requirements and assumptions. Formal methods, mathematical proofs, or simulated attacks can be used. Based on the results of the security performance evaluation, obtain an anti-attack optimization plan. This can include adjusting encryption parameters, adopting more powerful encryption algorithms, enhancing key management, improving authentication mechanisms, and other strategies.

[0134] Step S57: Dynamically update the key of specific confidential communication encryption parameters according to the anti-attack optimization solution.

[0135] Specifically, for example, a fixed time interval can be set to regularly update the keys used in the communication. For example, a new key is generated at a certain time interval (such as every hour or every day) and the communicating parties are notified to update the key. The instant update of the key is triggered according to the triggering conditions of a specific event, such as communication interruption, key leakage or attack detection. This can provide more timely security response and protection. Design a specific protocol or mechanism to enable the communicating parties to coordinate the dynamic update of the key. The protocol may include steps such as key negotiation, key distribution, authentication and key update confirmation. Generate a new key according to the update strategy and algorithm. Distribute the newly generated key securely to the communicating parties. Encrypted channels, security protocols or key exchange protocols can be used to ensure the secure transmission of keys. Notify the communicating parties to update the key. This can be achieved through encrypted channels, secure channels, key update protocols or other security means. After receiving the key update notification, the communicating parties confirm and apply the new key. Ensure that both parties switch to the new key at the same time point to ensure the continuity and security of communication.

[0136] The present invention can simulate a real network environment by building a distributed simulation platform, which includes multiple network nodes. In this way, attack and defense confrontation simulation can be performed more accurately to evaluate the performance and security of the system in the actual network. By defining the network configuration through software, the network structure and communication flow in the simulation platform can be flexibly controlled and adjusted. This makes it possible to customize according to different simulation requirements to meet specific test goals and scenario requirements. By setting a deceptive relay node, a potential attacker or malicious node in a real network environment can be simulated. In this way, the system's ability to respond to deceptive behavior, including the effectiveness of detection and defense measures, can be evaluated. By obtaining the deceptive node parameter configuration data, the behavior and strategy of potential attackers can be analyzed and studied. This provides an important basis and reference for subsequent defense and optimization. According to the parameter configuration data of the deceptive node, tampering information is generated to simulate the attack behavior of potential attackers on the target VLC relay system. In this way, the system's detection and response capabilities to tampering attacks can be evaluated, and improvement suggestions and optimization solutions can be provided. By generating a false data injection stream, the security of the system and the weaknesses of the defense mechanism can be discovered. This provides valuable guidance for improving and optimizing the security of the system. By using specific confidential communication encryption parameters to perform reversible visual encryption on the transmission data of the virtual target VLC relay system, the security and confidentiality of the data can be enhanced to prevent unauthorized access and information leakage. By tampering the encrypted data with a middleman, the attack behavior in the real network environment is simulated to generate a network attack and defense simulation data set. This can be used to evaluate the security of the system and the effectiveness of the defense mechanism, and provide improvement plans and optimization suggestions. By evaluating the anti-tampering performance of the network attack and defense simulation data set, the performance and effect of the system in the face of tampering attacks can be evaluated. This helps to understand the advantages and disadvantages of the system in defending against middleman tampering, and to make improvements and optimizations. By evaluating the anti-tampering performance data, the weaknesses and room for improvement of the system can be discovered. This provides useful guidance and decision-making basis for improving the anti-tampering capabilities of the system. According to the anti-attack optimization plan, the security performance evaluation of specific confidential communication encryption parameters can analyze its advantages and disadvantages in defending against middleman tampering. In this way, the impact of encryption parameters on system security can be understood, providing a basis for subsequent anti-attack optimization. Through security performance evaluation, targeted anti-attack optimization solutions can be provided for the system. This includes adjusting and improving the encryption parameters of confidential communications to improve the security and defense capabilities of the system. According to the anti-attack optimization plan, the keys of specific confidential communication encryption parameters are dynamically updated. This can respond to evolving attack methods and threats in a timely manner and improve the security and defense capabilities of the system. Dynamic key updates can increase the security of the system and reduce the probability of being attacked. At the same time, the confidentiality and integrity of the communication data are maintained to ensure that the data is not tampered with or leaked during transmission. This is essential for protecting sensitive information and maintaining the security of communications.

[0137] Preferably, step S55 comprises the following steps:

[0138] Step S551: classify, identify and extract the network attack and defense simulation data set, so as to obtain the original light beam data set and the received light beam data set;

[0139] Specifically, for example, the network attack and defense simulation data set can be classified and identified, and the primary beam and the received beam can be extracted and classified. Effective features are extracted from the data of the primary beam and the received beam. Signal processing techniques such as Fourier transform, wavelet transform, time domain feature extraction, etc. can be used to extract the frequency, amplitude, phase and other characteristics of the optical signal. Or machine learning or deep learning algorithms can be used to classify the extracted optical signal features. Common classification algorithms such as support vector machines (SVM), decision trees, random forests or deep learning models such as convolutional neural networks (CNN) or recursive neural networks (RNN) can be used. According to the classification and identification results, the primary beam data set and the received beam data set are obtained.

[0140] Step S552: performing a difference comparison on the original light beam data set and the received light beam data set, thereby obtaining an optical communication tampering feature set;

[0141] Specifically, for example, the correlation or similarity measure between the original light beam and the received light beam can be calculated. The similarity between the two light beam data can be compared using correlation coefficients, cross-correlation and other methods to detect whether there is tampering or modification. The difference measure between the original light beam and the received light beam is calculated. Distance measurement methods such as Euclidean distance, Manhattan distance, cosine similarity, etc. can be used to quantify the degree of difference between the two light beam data. Tampering features of the optical signal are extracted from the difference comparison results. The extracted features can be selected according to specific needs, such as amplitude changes, phase changes, frequency changes, etc.

[0142] Step S553: ​​performing optical path backtracking analysis on the optical communication tampering feature set, thereby obtaining an optical communication propagation trajectory data set;

[0143] Specifically, for example, an optical model can be established to simulate the attenuation, scattering, interference and other phenomena of light during propagation. Methods such as ray tracing algorithms and wave optics models can be used to simulate the propagation characteristics of light in optical fibers or free space. An optimization algorithm is used to fit the tampering feature set and the optical model to determine the most likely optical communication propagation trajectory. Methods such as least squares method, genetic algorithm, particle swarm optimization, etc. can be used to find the optimal solution. Based on the optical model and optimization results, the optical communication propagation trajectory is reconstructed to obtain the changes and path information of the optical signal during the propagation process. According to the results of the optical path backtracking analysis, an optical communication propagation trajectory data set is obtained. These data sets will provide information about the optical communication propagation path, possible tampering locations, and other related information.

[0144] Step S554: performing optical path length calculation and analysis on the original light beam data corresponding to the optical communication tampering feature set according to the optical communication propagation trajectory data set, thereby obtaining an optical communication path loss data set;

[0145] Specifically, for example, the propagation length of light on different paths can be calculated based on the propagation trajectory dataset and the optical model. This can be achieved by measuring the propagation distance, scattering loss, etc. of light in optical fiber or free space. According to the optical path length and transmission characteristics, the loss of the optical signal during propagation is analyzed. The influence of factors such as attenuation, scattering, and interference on the optical signal can be considered to evaluate the quality and strength of the optical signal. Based on the results of the optical path length calculation analysis, the optical communication path loss dataset is obtained. These datasets will provide information about the path length and loss of the optical signal during propagation.

[0146] Step S555: performing spectral similarity matching calculation on the original light beam data set and the received light beam data set, thereby obtaining optical communication signal similarity evaluation data;

[0147] Specifically, for example, the spectral characteristics of the optical signal can be extracted from the data of the primary light beam and the received light beam. Spectral analysis methods, such as fast Fourier transform (FFT) or wavelet transform, can be used to obtain the spectral information of the optical signal. Based on the spectral characteristics, the similarity between the primary light beam and the received light beam is calculated. Similarity measurement methods, such as correlation coefficient, cosine similarity, mutual information, etc., can be used to compare the degree of similarity between the two optical signals. Based on the results of the spectral similarity matching calculation, similarity evaluation data of the optical communication signal is obtained. These data will provide information about the similarity between the primary light beam and the received light beam, which is used to evaluate the quality and consistency of the optical communication signal.

[0148] Step S556: perform anti-tampering performance evaluation on the target VLC relay system according to the optical communication path loss data set and the optical communication signal similarity evaluation data, thereby obtaining anti-tampering performance data.

[0149] Specifically, for example, a tampering detection algorithm can be designed based on an optical communication path loss data set and an optical communication signal similarity evaluation data to determine whether there is tampering behavior. Machine learning algorithms, such as support vector machines (SVM), random forests, etc., can be used to train and classify data. By analyzing and evaluating the tampering detection results, the anti-tampering performance of the target VLC relay system is calculated. Indicators such as accuracy, false alarm rate, and missed alarm rate can be used to evaluate the performance of the system. According to the results of the anti-tampering performance evaluation, the tampering performance data of the target VLC relay system is obtained. These data will provide information about the system's anti-tampering capabilities for evaluating and improving the security of the system.

[0150] The present invention can obtain the original light beam data set and the received light beam data set by classifying, identifying and extracting the network attack and defense simulation data set. These data sets contain the optical communication transmission data in the simulation environment, providing basic data for subsequent analysis and processing. The original light beam data set and the received light beam data set are used as reference data sets for comparison and analysis with subsequent data to detect and identify tampering behavior in optical communication. By performing a difference comparison on the original light beam data set and the received light beam data set, an optical communication tampering feature set can be extracted. These features can reflect the tampering behavior that may exist in optical communication, such as modification, insertion or deletion of data. The optical communication tampering feature set provides clues and indications of tampering behavior that may exist in the network. By analyzing these features, tampering detection and analysis can be performed, potential security threats can be identified, and a basis can be provided for subsequent anti-tampering measures. By performing optical path backtracking analysis on the optical communication tampering feature set, the propagation trajectory of the optical signal in the network can be traced. In this way, it can be understood whether the optical signal has been tampered with or interfered with during the transmission process, and further confirm whether there is a security threat. The optical communication propagation trajectory data set helps determine the nodes or paths that may be tampered with during the transmission of the optical signal. This provides a basis for further security analysis and the formulation of anti-tampering measures. Through the optical communication propagation trajectory dataset, the optical path length calculation and analysis are performed on the original light beam data corresponding to the optical communication tampering feature set. In this way, the path loss of the optical signal during transmission can be evaluated, whether there is abnormal loss, and whether it has been tampered with or attacked. The optical communication path loss dataset can provide information about the loss on the optical signal transmission path. By analyzing these data, it can be determined whether the optical signal has experienced abnormal path loss, thereby evaluating the degree and scope of tampering. By performing spectral similarity matching calculations on the original light beam dataset and the received light beam dataset, the similarity between optical communication signals can be evaluated. In this way, it is possible to detect whether there is tampering or interference, such as replacement or modification of data, and verify the integrity and reliability of optical communication signals. According to the optical communication path loss dataset and the optical communication signal similarity evaluation data, the anti-tampering performance of the target visible light communication (VLC) relay system can be evaluated. In this way, the anti-tampering capability and reliability of the system can be evaluated, and potential security risks can be discovered and resolved. By evaluating the anti-tampering performance of the system, performance data on the system in resisting tampering can be obtained. These data can be used to improve the security performance of the system, optimize anti-tampering strategies and mechanisms, and improve the security and stability of the system. The present invention helps detect and identify tampering behaviors in optical communications, evaluates the anti-tampering capabilities of the system, and provides basic data and guidance to ensure the security and reliability of optical communications.

[0151] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0152] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for optimizing the security performance of a VLC relay system, characterized in that: The following steps are involved: Step S1: performing risk identification on the target VLC relay system to obtain a potential attack risk model of the system; wherein step S1 includes the following steps: Step S11: Acquire communication protocol mode data of the target VLC relay system; Step S12: performing network structure topology restoration on the target VLC relay system, thereby obtaining system network topology data; performing precise positioning of node deployment positions on the target VLC relay system according to the system network topology data, thereby obtaining system node geographic distribution data; Step S13: extracting network features of the target VLC relay system according to the communication protocol mode data, system network topology data and system node geographic distribution data of the target VLC relay system, thereby obtaining network feature data of the VLC relay system; Step S14: performing correlation feature matching and collection on a known VLC system vulnerability technology library and an attack technology library based on the VLC relay system network feature data, thereby obtaining candidate vulnerability exploitation solution data; Step S15: simulating security threat scenarios for the target VLC relay system according to the candidate vulnerability exploitation scenario data, thereby obtaining a potential attack risk model for the system; Step S2: Based on the system potential attack risk model, a penetration combat simulation is performed on the target VLC relay system through simulation testing to obtain system security vulnerability analysis data; based on the system security vulnerability analysis data, a security protection algorithm is evaluated on the target VLC relay system to obtain a system security protection solution design report; Step S3: cascade multi-dimensional security coding is performed on the target VLC relay system according to the system security protection scheme design report, thereby obtaining the cascade multi-dimensional security coding; wherein step S3 includes the following steps: Step S31: Acquire VLC relay system security coding knowledge base data and target VLC relay system service model data; Step S32: classify and annotate the VLC relay system security coding knowledge base data, thereby obtaining a hierarchical security coding technology data set; Step S33: Perform security performance evaluation on various coding technologies in the hierarchical security coding technology data set, thereby obtaining technology evaluation matrix data; Step S34: Generate hierarchical security coding system framework data according to the technical assessment matrix data; Step S35: performing hierarchical heterogeneous fusion framework matching on the hierarchical security coding system framework data based on the system network topology data, the business mode data of the target VLC relay system, and the communication protocol mode data, so as to obtain a coding protection framework solution; Step S36: Perform security performance evaluation and analysis on the coding protection framework solution, thereby obtaining framework protection effectiveness scorecard data; Step S37: performing a multi-dimensional optimization search of the coding mechanism combination within the coding protection framework scheme, thereby obtaining coding mechanism fusion topology data; Step S38: Distributed evolution encoding is performed on the encoding mechanism fusion topology data according to the framework protection effectiveness scorecard data to generate a cascaded multi-dimensional security code; Step S4: Perform retinal recognition and digital watermark extraction on the user to obtain the user's biometric data; use the biometric data to perform user-personalized double encryption on the cascaded multi-dimensional security code to obtain specific confidential communication encryption parameters; wherein step S4 includes the following steps: Step S41: Acquire the user's eye image and user identity information data; Step S42: performing iris recognition extraction on the user's eye image to obtain iris feature parameters; Step S43: Generate a biometric information watermark for the user identity information data, thereby obtaining the user digital watermark data; Step S44: reversibly embedding the iris feature parameters according to the user digital watermark data, thereby obtaining the user biometric feature data; Step S45: constructing a confidential communication coding framework for the user's biometric data, thereby obtaining confidential coding framework data; Step S46: using the confidentiality coding framework data to perform user-personalized double encryption on the cascaded multi-dimensional security coding, thereby obtaining specific confidentiality communication encryption parameters; Step S5: construct a distributed network attack and defense simulation platform; based on specific confidential communication encryption parameters, use middleman spoofing signals of different strengths to simulate attack tests on the target VLC relay system to obtain anti-tampering performance data; perform security performance evaluation on specific confidential communication encryption parameters according to the anti-tampering performance data to obtain an anti-attack optimization solution; dynamically update the key of specific confidential communication encryption parameters according to the anti-attack optimization solution.

2. The VLC relay system security performance optimization method according to claim 1, characterized in that: Step S14 includes the following steps: Step S141: performing principal component analysis and dimensionality reduction on the VLC relay system network characteristic data, thereby obtaining a system network characteristic principal component matrix; Step S142: performing kernel clustering extraction on the main component matrix of the system network features, thereby obtaining a core feature set of the system network; Step S143: using the system network core feature set to calculate the correlation between the known VLC system vulnerability technology library and the attack technology library, thereby obtaining a candidate vulnerability-attack solution group; mining the evolution chain and propagation channel of the candidate vulnerability-attack solution group, thereby obtaining solution technology diffusion link data; Step S144: Based on the candidate vulnerability-attack scheme group, the scheme technology diffusion link data is reconstructed using the connection analysis method, and the blood relationship of the candidate vulnerability-attack scheme group is corrected and reconstructed by combining the attack and defense evolution history analysis technology, so as to obtain a candidate vulnerability exploitation chain scheme library; Step S145: Evaluate the possibility of successful penetration of each solution in the candidate vulnerability exploit chain solution library, so as to obtain a solution penetration effectiveness data set; Step S146: Optimize and screen the candidate exploit chain solution library according to the solution penetration efficiency data set to obtain candidate vulnerability exploitation solution data.

3. The VLC relay system security performance optimization method according to claim 2, characterized in that: In step S145, the penetration success rate evaluation calculation formula is used to evaluate the possibility of successful penetration of each solution in the candidate vulnerability exploit chain solution library, wherein the penetration success rate evaluation calculation formula is as follows: ； In the formula, is the penetration success rate, is the base of natural logarithms, For time, is the duration of the attack, is the change of the attacker’s attack intensity over time, is the number of terms in the infinite series, is the infinite series item, is the circumference of a circle, is the number of vulnerabilities in the system, The number of attack techniques used by the attacker.

4. The VLC relay system security performance optimization method according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: Designing a directional threat penetration scheme for the target VLC relay system according to the system potential attack risk model, thereby obtaining a directional threat penetration test scheme; Step S22: Based on the directional threat penetration test solution, a high-fidelity network environment simulation reconstruction is performed on the target VLC relay system to obtain a VLC test sample; Step S23: setting anti-tampering monitoring watermarks on key test nodes of the VLC test sample, thereby obtaining intelligent protection intermediate nodes; Step S24: performing behavior reproduction analysis and identification on the target VLC relay system, thereby obtaining a VLC system risk scenario data set; Step S25: constructing a distributed simulation test cluster environment for the intelligent protection intermediate node and the VLC system risk scenario data set to generate a configurable simulation test platform; Step S26: Based on the configurable simulation test platform, an active defense type directional threat combat simulation is performed on the target VLC relay system according to the intelligent protection intermediate node, so as to obtain simulation test result data; Step S27: performing system security vulnerability matching detection on the simulation test result data, thereby obtaining a system security vulnerability association list; Step S28: Perform an adaptive security protection algorithm evaluation on the target VLC relay system according to the system security vulnerability association list, so as to obtain vulnerability corresponding protection model data; perform hierarchical security implementation improvement on the target VLC relay system according to the vulnerability corresponding protection model data, so as to obtain a system security protection solution design report.

5. The VLC relay system security performance optimization method according to claim 4, characterized in that: Step S241 includes the following steps: Step S241: Acquire a standardized general VLC relay system risk data set; Step S242: Performing service process simulation recovery on the target VLC relay system, thereby obtaining a VLC system behavior operation data set; Step S243: performing a weak link security assessment analysis on the target VLC relay system according to the VLC system behavior operation data set, thereby obtaining a customized risk attack surface data set; Step S244: using the customized risk attack surface dataset to perform differential fusion injection on the standardized general VLC relay system risk dataset, thereby obtaining a VLC system risk scenario dataset.

6. The VLC relay system security performance optimization method according to claim 1, characterized in that: Step S5 includes the following steps: Step S51: building a distributed simulation platform including multiple network nodes, and performing software-defined network configuration on the distributed simulation platform, thereby obtaining a distributed network attack and defense confrontation simulation platform; Step S52: setting a deceptive relay node for the distributed network attack and defense confrontation simulation platform, thereby obtaining deceptive node parameter configuration data; Step S53: generating tampering information for the target VLC relay system according to the spoofed node parameter configuration data, thereby obtaining a fake data injection stream; Step S54: Based on the distributed network attack and defense confrontation simulation platform, the data transmitted by the virtual target VLC relay system is reversibly visually encrypted using specific confidential communication encryption parameters, and the encrypted data is tampered with by a fake data injection stream, thereby obtaining a network attack and defense simulation data set; Step S55: performing anti-tampering performance evaluation on the network attack and defense simulation data set, thereby obtaining anti-tampering performance data; Step S56: Perform security performance evaluation on specific confidential communication encryption parameters according to the anti-tampering performance data, so as to obtain an anti-attack optimization solution; Step S57: Dynamically update the key of specific confidential communication encryption parameters according to the anti-attack optimization solution.

7. The VLC relay system security performance optimization method according to claim 6, characterized in that: Step S55 includes the following steps: Step S551: classify, identify and extract the network attack and defense simulation data set, so as to obtain the original light beam data set and the received light beam data set; Step S552: performing a difference comparison on the original light beam data set and the received light beam data set, thereby obtaining an optical communication tampering feature set; Step S553: ​​performing optical path backtracking analysis on the optical communication tampering feature set, thereby obtaining an optical communication propagation trajectory data set; Step S554: performing optical path length calculation and analysis on the original light beam data corresponding to the optical communication tampering feature set according to the optical communication propagation trajectory data set, thereby obtaining an optical communication path loss data set; Step S555: performing spectral similarity matching calculation on the original light beam data set and the received light beam data set, thereby obtaining optical communication signal similarity evaluation data; Step S556: perform anti-tampering performance evaluation on the target VLC relay system according to the optical communication path loss data set and the optical communication signal similarity evaluation data, thereby obtaining anti-tampering performance data.

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