Network security test and evaluation system based on big data

Abstract

The invention relates to the technical field of network security, in particular to a network security test and evaluation system based on big data. The system comprises a network security data loading module used for loading network security data of a network test node; the network node importance calculation module is used for calculating a node structure importance index LMS of a node, a function importance index GLS of the node and a device integrity index WZS of the node based on network security data; according to the node structure importance index LMS, the function importance index GLS of the node and the equipment integrity index WZS of the node, calculating to obtain a node importance index; the evaluation weight distribution module is used for distributing evaluation weights based on the node importance indexes; and the evaluation module is used for carrying out security evaluation on the network based on the evaluation weight. According to the invention, the accuracy and efficiency of network security test evaluation can be improved.

Description

Technical Field

[0001] The present invention relates to the field of network security technology, and in particular to a network security testing and evaluation system based on big data. Background Art

[0002] With the rapid development of information technology, big data has penetrated into every corner of society, providing unprecedented opportunities for enterprises, but also bringing severe security challenges. Network security issues have become one of the key factors restricting the in-depth development of big data applications.

[0003] The network security testing and evaluation system based on big data mainly relies on big data technology, combined with professional knowledge and technical means in the field of network security, to conduct comprehensive security testing and evaluation of network systems. The system aims to timely discover and respond to network security threats through real-time monitoring, data collection, analysis and processing, risk assessment and other links, and ensure the security and stability of network systems.

[0004] In the existing technology, due to the complex and changeable network environment, various forms of network attacks, non-fixed attack targets, and the different attack tolerance and detection capabilities of each node in the network, network security testing and evaluation may cause incomplete data on the degree of danger measured by the nodes and uncertain information, which in turn causes inaccurate evaluation results of network security testing and evaluation. Therefore, there is an urgent need for a network security testing and evaluation system based on big data to solve the above problems. Summary of the invention

[0005] The purpose of the present invention is to provide a network security testing and evaluation system based on big data: to solve the technical problem that the evaluation results of network security testing and evaluation are inaccurate due to factors such as the complex and changeable network environment, various forms of network attacks, non-fixed attack targets, and different attack tolerance and detection capabilities of each node in the network.

[0006] The purpose of the present invention can be achieved through the following technical solutions:

[0007] A network security test and evaluation system based on big data, the system includes a network security data loading module, a network node importance calculation module, an evaluation weight allocation module and an evaluation module;

[0008] The network security data loading module is used to load the network security data of the network test node, wherein the network security data includes the vertex degree information, the shortest path information, and the node location information of the node;

[0009] The network node importance calculation module is used to calculate the node structure importance index LMS, the node function importance index GLS and the node equipment integrity index WZS of the node based on the network security data, and calculate the node importance index according to the node structure importance index LMS, the node function importance index GLS and the node equipment integrity index WZS;

[0010] The evaluation weight allocation module is used to allocate evaluation weights based on the node importance index;

[0011] The assessment module is used to perform security assessment on the network based on the assessment weight.

[0012] Furthermore, the node structure importance index LMS of the node calculated based on the network security data specifically includes the following process:

[0013] Based on the vertex degree information of the node, the vertex degree corresponding to the node and the sum of the vertex degrees of all nodes are obtained, where the vertex degree is the number of nodes adjacent to the node, and the ratio of the vertex degree corresponding to the node to the sum of the vertex degrees of all nodes is calculated, and the ratio is recorded as the node vertex degree index S;

[0014] Based on the shortest path information, the number of shortest paths passing through the node is obtained, wherein the shortest path information is the number of all shortest routing paths from node A to node B within the management period, and the number of shortest paths passing through the node is the number of shortest routing paths from node A to node B passing through the node within the management period. The ratio of the number of shortest routing paths passing through the node to the number of all shortest routing paths is calculated, and the ratio is recorded as the node centrality index P;

[0015] Based on the node location information, the routing distance between the node and other nodes is obtained, and the routing distances are summed up to obtain a sum value, and the reciprocal of the sum value is taken, and the reciprocal is recorded as the node location feature index R;

[0016] Substitute the node vertex index S, node centrality index P and node position characteristic index R into the association formula to calculate the node structure importance index LMS. The association formula is as follows:

[0017]

[0018] Among them, α and β represent parameters respectively, and their sum is 1.

[0019] Furthermore, the function importance index GLS of the node is calculated based on network security data, which specifically includes the following process:

[0020] Network security data includes the number m of service types supported by the node;

[0021] The function importance index GLS of the node is calculated by the calculation formula, which is as follows:

[0022]

[0023] Where j is the service type number, the service type is HTTP, DHCP, DNS, FTP, SMTP, w is the importance parameter of the j-th service relative to other services, which is set by the system, and v j The service frequency for the jth service on this node.

[0024] Furthermore, the device integrity index WZS of the node is calculated based on the network security data, which specifically includes the following process:

[0025] Obtain the hardware assembly information of the node, obtain the actual hardware assembly quantity of the node according to the hardware assembly information, and calculate the ratio YJ of the actual hardware assembly quantity of the node to the hardware assembly quantity of the node, where the hardware assembly quantity of the node is the number of index items preset by the system, and the hardware includes CPU, memory, and network card;

[0026] Obtain the software installation information of the node, obtain the actual software installation quantity of the node according to the software installation information of the node, and calculate the ratio RJ of the actual software installation quantity of the node to the software installation quantity of the node, where the software installation quantity of the node is the number of index items preset by the system, and the software includes operating system, document data, application software, and database;

[0027] The sum of YJ and RJ is recorded as the equipment integrity index WZS of the node.

[0028] Furthermore, the node importance index is calculated according to the node structure importance index LMS, the node function importance index GLS and the node equipment integrity index WZS, which specifically includes the following process:

[0029] Determine the weights of the node structure importance index LMS, the node function importance index GLS and the node equipment integrity index WZS respectively: K1, K2, K3;

[0030] The node importance index ZYS is obtained according to the association formula, which is as follows:

[0031]

[0032] Among them, Aj and Al are the number of vulnerabilities and network attacks on the nodes respectively, and N is the total number of nodes.

[0033] Furthermore, allocating evaluation weights based on node importance index includes the following process:

[0034] Data preparation: Collect the importance index data of each node in the network, ensure the accuracy and completeness of the data, and process missing or abnormal data;

[0035] Data standardization: Standardize the node importance index to eliminate the impact of dimension and unit. Standardization methods include min-max standardization or Z-score standardization.

[0036] Calculate the index proportion: For the standardized data, calculate the index proportion of each node's importance index in the sum of all nodes' importance indexes, which is achieved by dividing each node's importance index by the sum of all nodes' importance indexes;

[0037] Calculate the entropy value: According to the calculated indicator proportion, use the entropy calculation formula to calculate the entropy value e of each node importance index j :

[0038]

[0039] in, P ij is the probability distribution function of the indicator weight, i is the node number, and N is the total number of nodes;

[0040] Calculate information entropy redundancy:

[0041] By calculating the information entropy redundancy d j :

[0042] d j =1-e j

[0043] Calculate weights: Use information entropy redundancy d j Calculate the weight of each node's importance index. The weight reflects the importance of the index in the comprehensive evaluation.

[0044] Weight distribution: The calculated weight is distributed to each node to obtain the evaluation weight based on the node importance index.

[0045] Furthermore, the network security assessment based on the assessment weight includes the following process:

[0046] Use professional security assessment tools and technologies to conduct security assessments on each network node. Security assessment tools and technologies include vulnerability scanning, penetration testing, and security configuration verification;

[0047] Assess whether the node has security vulnerabilities, configuration errors, and weak password security risks, and quantify these risks based on the node's weight;

[0048] Analyze the evaluation results: Summarize and analyze the evaluation results to identify high-risk nodes and potential security issues in the network; evaluate the dependencies and mutual influences between nodes, and analyze the propagation path and scope of security risks in the network;

[0049] Formulate response strategies: Based on the assessment results, formulate targeted security response strategies and measures, including strengthening the security configuration of high-risk nodes, fixing security vulnerabilities, updating software versions, and strengthening access control;

[0050] Establish priorities and timelines to ensure effective implementation and monitoring of response strategies;

[0051] Write assessment report: Write a detailed safety assessment report summarizing the assessment results, safety issues found, response strategies and recommendations;

[0052] The report should include a quantitative analysis of the node's security status, as well as risk ranking and priority based on the assessment weights.

[0053] Compared with the existing solutions, the present invention achieves the following beneficial effects:

[0054] The present invention loads network security data of a network test node, calculates the node structure importance index LMS, the node function importance index GLS and the node equipment integrity index WZS based on the network security data, and calculates the node importance index according to the node structure importance index LMS, the node function importance index GLS and the node equipment integrity index WZS; assigns an evaluation weight based on the node importance index; and performs a security evaluation on the network based on the evaluation weight. The accuracy of network security test evaluation can be improved.

[0055] The network security testing and evaluation system based on big data can realize the rapid processing and analysis of massive network node data and improve the efficiency of security evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0057] Figure 1 It is a system block diagram of a network security testing and evaluation system based on big data according to an embodiment of the present invention;

[0058] Figure 2 It is a system workflow diagram of a network security testing and evaluation system based on big data according to an embodiment of the present invention. DETAILED DESCRIPTION

[0059] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0060] In addition, the described features, structures or characteristics may be combined in one or more example embodiments in any suitable manner. In the following description, many specific details are provided to provide a full understanding of the example embodiments of the present disclosure. However, those skilled in the art will appreciate that the technical solutions of the present disclosure may be practiced while omitting one or more of the specific details, or other methods, components, steps, etc. may be adopted. In other cases, well-known structures, methods, implementations or operations are not shown or described in detail to avoid obscuring various aspects of the present disclosure.

[0061] This embodiment provides a network security testing and evaluation system based on big data. Figure 1 is a system block diagram of a network security testing and evaluation system based on big data according to an embodiment of the present invention. Figure 1 As shown, the system includes a network security data loading module, a network node importance calculation module, an evaluation weight allocation module and an evaluation module;

[0062] The network security data loading module is used to load the network security data of the network test node, wherein the network security data includes the vertex degree information, the shortest path information, and the node location information of the node;

[0063] The network node importance calculation module is used to calculate the node structure importance index LMS, the node function importance index GLS and the node equipment integrity index WZS of the node based on the network security data, and calculate the node importance index according to the node structure importance index LMS, the node function importance index GLS and the node equipment integrity index WZS;

[0064] The evaluation weight allocation module is used to allocate evaluation weights based on the node importance index;

[0065] The assessment module is used to perform security assessment on the network based on the assessment weight.

[0066] In summary, the present invention loads the network security data of the network test node, calculates the node structure importance index LMS, the node function importance index GLS and the node equipment integrity index WZS based on the network security data, and calculates the node importance index according to the node structure importance index LMS, the node function importance index GLS and the node equipment integrity index WZS; assigns an evaluation weight based on the node importance index; and performs a security evaluation on the network based on the evaluation weight. The accuracy of network security test evaluation can be improved.

[0067] In some embodiments, Figure 2 : is a system workflow diagram of a network security testing and evaluation system based on big data according to an embodiment of the present invention. Figure 2 As shown, the node structure importance index LMS of the node calculated based on network security data specifically includes the following steps:

[0068] Step S201, based on the vertex degree information of the node, obtain the vertex degree corresponding to the node and the sum of the vertex degrees of all nodes, wherein the vertex degree is the number of nodes adjacent to the node, calculate the ratio of the vertex degree corresponding to the node to the sum of the vertex degrees of all nodes, and record the ratio as the node vertex degree index S;

[0069] Step S202, based on the shortest path information, the number of shortest paths passing through the node is obtained, wherein the shortest path information is the number of all shortest routing paths from node A to node B within the management period, the number of shortest paths passing through the node is the number of shortest routing paths from node A to node B passing through the node within the management period, and the ratio of the number of shortest routing paths passing through the node to the number of all shortest routing paths is calculated, and the ratio is recorded as the node centrality index P;

[0070] Step S203, based on the node position information, the routing distances between the node and other nodes are obtained, and the routing distances are summed up to obtain a sum value, and the reciprocal of the sum value is taken, and the reciprocal is recorded as the node position feature index R;

[0071] Step S204, the node vertex index S, the node centrality index P and the node position characteristic index R are substituted into the association formula to calculate the node structure importance index LMS, and the association formula is as follows:

[0072]

[0073] Among them, α and β represent parameters respectively, and their sum is 1.

[0074] Furthermore, the function importance index GLS of the node is calculated based on network security data, which specifically includes the following process:

[0075] Network security data includes the number m of service types supported by the node;

[0076] The function importance index GLS of the node is calculated by the calculation formula, which is as follows:

[0077]

[0078] Where j is the service type number, the service type is HTTP, DHCP, DNS, FTP, SMTP, w is the importance parameter of the j-th service relative to other services, which is set by the system, and v j The service frequency for the jth service on this node.

[0079] In some embodiments, calculating the device integrity index WZS of a node based on network security data specifically includes the following process:

[0080] Obtain the hardware assembly information of the node, obtain the actual hardware assembly quantity of the node according to the hardware assembly information, and calculate the ratio YJ of the actual hardware assembly quantity of the node to the hardware assembly quantity of the node, where the hardware assembly quantity of the node is the number of index items preset by the system, and the hardware includes CPU, memory, and network card;

[0081] Obtain the software installation information of the node, obtain the actual software installation quantity of the node according to the software installation information of the node, and calculate the ratio RJ of the actual software installation quantity of the node to the software installation quantity of the node, where the software installation quantity of the node is the number of index items preset by the system, and the software includes operating system, document data, application software, and database;

[0082] The sum of YJ and RJ is recorded as the equipment integrity index WZS of the node.

[0083] Furthermore, the node importance index is calculated according to the node structure importance index LMS, the node function importance index GLS and the node equipment integrity index WZS, which specifically includes the following process:

[0084] Determine the weights of the node structure importance index LMS, the node function importance index GLS and the node equipment integrity index WZS respectively: K1, K2, K3;

[0085] The node importance index ZYS is obtained according to the association formula, which is as follows:

[0086]

[0087] Among them, Aj and Al are the number of vulnerabilities and network attacks on the nodes respectively, and N is the total number of nodes.

[0088] Furthermore, allocating evaluation weights based on node importance index includes the following process:

[0089] Data preparation: Collect the importance index data of each node in the network, ensure the accuracy and completeness of the data, and process missing or abnormal data;

[0090] Data standardization: Standardize the node importance index to eliminate the impact of dimension and unit. Standardization methods include min-max standardization or Z-score standardization.

[0091] Calculate the index proportion: For the standardized data, calculate the index proportion of each node's importance index in the sum of all nodes' importance indexes, which is achieved by dividing each node's importance index by the sum of all nodes' importance indexes;

[0092] Calculate the entropy value: According to the calculated indicator proportion, use the entropy calculation formula to calculate the entropy value e of each node importance index j :

[0093]

[0094] in, P ij is the probability distribution function of the indicator weight, i is the node number, and N is the total number of nodes;

[0095] Calculate information entropy redundancy:

[0096] By calculating the information entropy redundancy d j :

[0097] d j =1-e j

[0098] Calculate weights: Use information entropy redundancy d j Calculate the weight of each node's importance index. The weight reflects the importance of the index in the comprehensive evaluation.

[0099] Weight distribution: The calculated weight is distributed to each node to obtain the evaluation weight based on the node importance index.

[0100] In some embodiments, performing security assessment on a network based on the assessment weights includes the following process:

[0101] Use professional security assessment tools and technologies to conduct security assessments on each network node. Security assessment tools and technologies include vulnerability scanning, penetration testing, and security configuration verification;

[0102] Assess whether the node has security vulnerabilities, configuration errors, and weak password security risks, and quantify these risks based on the node's weight;

[0103] Analyze the evaluation results: Summarize and analyze the evaluation results to identify high-risk nodes and potential security issues in the network; evaluate the dependencies and mutual influences between nodes, and analyze the propagation path and scope of security risks in the network;

[0104] Formulate response strategies: Based on the assessment results, formulate targeted security response strategies and measures, including strengthening the security configuration of high-risk nodes, fixing security vulnerabilities, updating software versions, and strengthening access control;

[0105] Establish priorities and timelines to ensure effective implementation and monitoring of response strategies;

[0106] Write assessment report: Write a detailed safety assessment report summarizing the assessment results, safety issues found, response strategies and recommendations;

[0107] The report should include a quantitative analysis of the node's security status, as well as risk ranking and priority based on the assessment weights.

[0108] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0109] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0110] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0111] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only some logical function divisions. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0112] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0113] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A network security testing and evaluation system based on big data, characterized in that: The system includes a network security data loading module, a network node importance calculation module, an evaluation weight distribution module and an evaluation module; The network security data loading module is used to load the network security data of the network test node, wherein the network security data includes the vertex degree information, the shortest path information, and the node location information of the node; The network node importance calculation module is used to calculate the node structure importance index LMS, the node function importance index GLS and the node equipment integrity index WZS of the node based on the network security data, and calculate the node importance index according to the node structure importance index LMS, the node function importance index GLS and the node equipment integrity index WZS; The evaluation weight allocation module is used to allocate evaluation weights based on the node importance index; The assessment module is used to perform security assessment on the network based on the assessment weight.

2. The network security testing and evaluation system based on big data according to claim 1 is characterized in that: The node structure importance index LMS of the node is calculated based on network security data. The process includes: Based on the vertex degree information of the node, the vertex degree corresponding to the node and the sum of the vertex degrees of all nodes are obtained, where the vertex degree is the number of nodes adjacent to the node, and the ratio of the vertex degree corresponding to the node to the sum of the vertex degrees of all nodes is calculated, and the ratio is recorded as the node vertex degree index S; Based on the shortest path information, the number of shortest paths passing through the node is obtained, wherein the shortest path information is the number of all shortest routing paths from node A to node B within the management period, and the number of shortest paths passing through the node is the number of shortest routing paths from node A to node B passing through the node within the management period. The ratio of the number of shortest routing paths passing through the node to the number of all shortest routing paths is calculated, and the ratio is recorded as the node centrality index P; Based on the node location information, the routing distance between the node and other nodes is obtained, and the routing distances are summed up to obtain a sum value, and the reciprocal of the sum value is taken, and the reciprocal is recorded as the node location feature index R; Substitute the node vertex index S, node centrality index P and node position characteristic index R into the association formula to calculate the node structure importance index LMS. The association formula is as follows: Among them, α and β represent parameters respectively, and their sum is 1.

3. The network security testing and evaluation system based on big data according to claim 1 is characterized in that: The GLS function importance index of nodes based on network security data calculation specifically includes the following processes: Network security data includes the number m of service types supported by the node; The function importance index GLS of the node is calculated by the calculation formula, which is as follows: Where j is the service type number, the service type is HTTP, DHCP, DNS, FTP, SMTP, w is the importance parameter of the j-th service relative to other services, which is set by the system, and v j The service frequency for the jth service on this node.

4. The network security testing and evaluation system based on big data according to claim 1 is characterized in that: The device integrity index WZS based on network security data calculation node specifically includes the following processes: Obtain the hardware assembly information of the node, obtain the actual hardware assembly quantity of the node according to the hardware assembly information, and calculate the ratio YJ of the actual hardware assembly quantity of the node to the hardware assembly quantity of the node, where the hardware assembly quantity of the node is the number of index items preset by the system, and the hardware includes CPU, memory, and network card; Obtain the software installation information of the node, obtain the actual software installation quantity of the node according to the software installation information of the node, and calculate the ratio RJ of the actual software installation quantity of the node to the software installation quantity of the node, where the software installation quantity of the node is the number of index items preset by the system, and the software includes operating system, document data, application software, and database; The sum of YJ and RJ is recorded as the equipment integrity index WZS of the node.

5. The network security testing and evaluation system based on big data according to claim 1 is characterized in that: The node importance index is calculated based on the node structure importance index LMS, the node function importance index GLS and the node equipment integrity index WZS. The process includes: Determine the weights of the node structure importance index LMS, the node function importance index GLS and the node equipment integrity index WZS respectively: K1, K2, K3; The node importance index ZYS is obtained according to the association formula, which is as follows: Among them, Aj and Al are the number of vulnerabilities and network attacks on the nodes respectively, and N is the total number of nodes.

6. The network security testing and evaluation system based on big data according to claim 1 is characterized in that: The process of assigning evaluation weights based on node importance index is as follows: Data preparation: Collect the importance index data of each node in the network, ensure the accuracy and completeness of the data, and process missing or abnormal data; Data standardization: Standardize the node importance index to eliminate the impact of dimension and unit. Standardization methods include min-max standardization or Z-score standardization. Calculate the index proportion: For the standardized data, calculate the index proportion of each node's importance index in the sum of all nodes' importance indexes, which is achieved by dividing each node's importance index by the sum of all nodes' importance indexes; Calculate the entropy value: According to the calculated indicator proportion, use the entropy calculation formula to calculate the entropy value e of each node importance index j : in, P ij is the probability distribution function of the indicator weight, i is the node number, and N is the total number of nodes; Calculate information entropy redundancy: By calculating the information entropy redundancy d j : d j =1-e j Calculate weights: Use information entropy redundancy d j Calculate the weight of each node's importance index. The weight reflects the importance of the index in the comprehensive evaluation. Weight distribution: The calculated weight is distributed to each node to obtain the evaluation weight based on the node importance index.

7. The network security testing and evaluation system based on big data according to claim 1 is characterized in that: Perform network security assessment based on assessment weights The process includes: Use professional security assessment tools and technologies to conduct security assessments on each network node. Security assessment tools and technologies include vulnerability scanning, penetration testing, and security configuration verification; Assess whether the node has security vulnerabilities, configuration errors, and weak password security risks, and quantify these risks based on the node's weight; Analyze the evaluation results: Summarize and analyze the evaluation results to identify high-risk nodes and potential security issues in the network; evaluate the dependencies and mutual influences between nodes, and analyze the propagation path and scope of security risks in the network; Formulate response strategies: Based on the assessment results, formulate targeted security response strategies and measures, including strengthening the security configuration of high-risk nodes, fixing security vulnerabilities, updating software versions, and strengthening access control; Establish priorities and timelines to ensure effective implementation and monitoring of response strategies; Write assessment report: Write a detailed safety assessment report summarizing the assessment results, safety issues found, response strategies and recommendations; The report should include a quantitative analysis of the node's security status, as well as risk ranking and priority based on the assessment weights.

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