Multi-factor communication link security assessment method based on data fusion

Abstract

The invention discloses a multi-factor civil aviation communication link safety index evaluation method based on subjective and objective data source fusion. The method comprises the following steps: 1) establishing a communication link safety evaluation index; 2) constructing a communication link safety evaluation model considering a plurality of factors; 3) selecting different data types to carry out security assessment according to the difficulty level of factor index quantification; 4) according to the types of the factor indexes, different evaluation methods are selected to calculate the multi-layer index weight and the underlying factor index risk value of the evaluation system; and 5) fusing the index weight and the index risk value to calculate a comprehensive risk value of the communication link under a plurality of external factors, thereby achieving the purpose of evaluating the security of the communication link transmission process. According to the method, the security of the communication system is evaluated by using a subjective and objective combined method through multiple perspectives, and a scientific and effective evaluation means is provided for research, development and application of a communication technology.

Description

Technical Field

[0001] The present invention relates to the field of communication security, and more specifically to a multi-factor communication link security assessment method based on data fusion. Background Art

[0002] In recent years, with the continuous development of communication technology, the communication links used in various industries are undergoing a revolutionary change. For example, high-frequency radio communications represented by 5G have been applied in the civil aviation field to realize information transmission between air and ground. The communication link is the main way for system data exchange, and the quality of communication is usually affected by environmental factors or human factors. In the future, the requirements for communication quality will become higher and higher with the advancement of technology. External factors have become one of the influencing parts that cannot be ignored. Therefore, how to evaluate the impact of the natural environment on the communication link is a hot issue in current research. By studying the impact assessment method of the communication link, we can calculate the security level of the communication link and the impact of external factors. By quantifying the external impact, on the one hand, we can understand the security status of the communication link so as to take corresponding measures to reduce the impact of external factors; on the other hand, by pre-assessing the impact of external factors, we can further enhance the anti-interference ability of the communication system.

[0003] The subjective method used in this invention is the analytic hierarchy process and DS evidence theory.

[0004] The objective method used in the present invention is the grey correlation method. Summary of the invention

[0005] The technical problem to be solved by the present invention is to provide a multi-factor communication link security assessment method based on the fusion of subjective and objective data. Through the application of grey correlation method, DS evidence theory, hierarchical analysis method and risk matrix in risk assessment, the uncertainty factors and indicators existing in the communication link are analyzed, the weights are determined, and the risk value of the system is calculated.

[0006] In order to solve the above technical problems, the present invention adopts the following technical solutions:

[0007] S1: Establish a communication link security evaluation model. The establishment process is as follows: first, extract the security indicators of the communication link, then analyze the external factors that affect the security indicators of the communication link, and finally establish a communication link security evaluation model that considers multiple factors. This system model is used to analyze the defense capability of the communication link under external interference, and to conduct security assessment and prevention of external influences.

[0008] S2: Pre-process the sorted multi-factor indicators and classify the indicators according to whether they can be measured using objective data.

[0009] S3: Use different methods to collect data on multiple influencing factors. For measurable indicators, the following three methods are mainly used: Collect and analyze historical data on impact indicators; Use simulation software to obtain simulation data for specific scenarios; Build experimental scenarios for field measurements; For indicators that are difficult to measure, data are obtained through subjective evaluation by experts.

[0010] S4: Use a combination of subjective and objective methods to conduct security analysis on the collected data to obtain the weight, impact degree and risk value of external factors on the communication link security indicators.

[0011] S5: The comprehensive security risk value of the communication link affected by the external environment is obtained by combining the weights and influences of multiple factors.

[0012] As a further solution of the present invention, researchers analyze the security indicators of the communication link and construct an evaluation system for the communication link. Researchers prepare and publish a score sheet or questionnaire; and a group of experts in related fields perform scoring to obtain subjective data.

[0013] As a further solution of the present invention, for evaluation indicators that cannot use objective data, according to the established recognition framework: U = {small impact, medium impact, large impact}; Invite several experts to give the basic probability distribution function of each indicator under all identification framework subsets; for each indicator and the corresponding identification framework subset, use the Dempster fusion rule to fusion the basic probability distribution functions given by experts in pairs, and finally obtain the basic probability distribution function of each indicator under all identification framework subsets, and take the subset corresponding to the maximum value of the basic probability distribution function as the final impact comment of the indicator; convert the final impact comment into an impact evaluation level.

[0014] As a further solution of the present invention, for evaluation indicators using objective data, a grey correlation method is used to calculate the grey correlation coefficient of the evaluation indicator, and the grey correlation coefficient is converted into an impact evaluation grade.

[0015] As a further solution of the present invention, the risk assessment matrix method includes possibility assessment levels and impact assessment levels; the possibility assessment levels are the same as the impact assessment levels, both including level one, level two, and level three.

[0016] As a further solution of the present invention, the evaluation index is composed of the quasi-measurement layer index, and the criterion layer index includes integrity, confidentiality and availability; the criterion layer is composed of the indicator layer; the integrity includes human error, electromagnetic interference, and modification of transmission data; the confidentiality includes link damage / failure, malware / virus; the availability is composed of environmental changes and weather conditions; the environmental changes include operating altitude, urban environment, and non-urban environment; the weather conditions include rainfall, humidity changes, thunderstorms

[0017] As a further solution of the present invention, the present invention adopts hierarchical analysis method combined with expert subjective data to calculate the weights of all indicators; scores are given according to the relative importance of the evaluation indicators; a judgment matrix is ​​constructed according to the obtained scores; a consistency check is performed on the judgment matrix; the weight vector of the evaluation indicator is calculated according to the obtained judgment matrix, and the weight vector is normalized.

[0018] As a further solution of the present invention, the method of constructing a judgment matrix includes using 1, 3, 5, 7, 9 and the reciprocal of the method to construct the judgment matrix.

[0019] As a further solution of the present invention, the method for solving the weight vector of the evaluation index includes: dividing each column element of the judgment matrix by the sum of the corresponding column elements to obtain a normalized weighted judgment matrix; performing eigenvalue decomposition according to the weighted judgment matrix to obtain the maximum eigenvalue; substituting the maximum eigenvalue into the weighted judgment matrix and solving the corresponding maximum eigenvector; normalizing the maximum eigenvector to obtain a weight vector;

[0020] As a further solution of the present invention, the method for performing consistency check on the judgment matrix includes: calculating consistency index CI (consistency index) and CR (consistency ratio); when the consistency ratio When , the judgment matrix passes the consistency test, otherwise the judgment matrix fails the consistency test, and it is necessary to rebuild the judgment matrix and re-obtain the weight vector;

[0021] As a further solution of the present invention, the risk value calculation step of S5 includes: using a risk matrix method to calculate the risk value of the scheme layer indicator; using a hierarchical weighted summation method to calculate the comprehensive risk value.

[0022] As a further solution of the present invention, the calculation process of the risk value using the risk matrix method is: obtaining the possibility evaluation level of different evaluation indicators through actual measurement or literature review; and obtaining the risk value of the scheme-level indicator by multiplying the possibility evaluation level with the impact evaluation level.

[0023] As a further scheme of the present invention, the calculation process of calculating the comprehensive risk value using the hierarchical weighted summation method is as follows: for each scheme layer indicator contained in the criterion layer indicator, the risk value of the scheme layer indicator is multiplied by its corresponding weight value to obtain the security risk value of each criterion layer indicator under the influence of the scheme layer indicator; for the criterion layer indicators contained in the target layer, the risk value of the criterion layer indicator is multiplied by its weight value to obtain the comprehensive risk value of the communication link.

[0024] As a further solution of the present invention, the condition for evaluating the communication link according to the risk matrix method is: if the risk value is between one and three, the communication link threat is acceptable; if the risk value is between three and six, the communication link threat needs to be mitigated; if the risk value is between six and nine, the communication link threat is unacceptable;

[0025] When the present invention is implemented, the following beneficial effects are achieved:

[0026] The present invention (inventive method) can analyze uncertain factors such as the lack of objective data of some indicators that appear in the risk assessment process by applying a combination of subjective and objective methods in risk assessment.

[0027] It overcomes the shortcomings of strong subjectivity in communication link evaluation and large differences in communication systems, and provides a new method for evaluating the security of communication links from different angles.

[0028] By setting criteria layer indicators, security indicators such as communication link integrity, confidentiality and availability are considered. The possibility of external factors and their impact are taken into account, and the analysis is more comprehensive. BRIEF DESCRIPTION OF THE DRAWINGS In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0029] Figure 1 It is a flow chart of an embodiment of the communication link security assessment method based on subjective and objective methods of the present invention.

[0030] Figure 2 It is a multi-factor communication link security evaluation system constructed in the present invention. DETAILED DESCRIPTION

[0031] The embodiments of the present invention are further described in detail below in conjunction with the accompanying drawings. It should be understood that the embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0032] In a specific embodiment, Figure 1 As shown, a multi-factor communication link security assessment method based on data fusion adopts the following steps:

[0033] S1: Establish a communication link security evaluation system. The process of establishing the system is as follows: first, extract the security indicators of the communication link, then analyze the external factors that affect the security indicators of the communication link, and finally establish a multi-factor communication link security evaluation system. This system model is used to analyze the defense capability of the communication link under external interference, and to conduct security assessment and prevention of external influences.

[0034] S2: Pre-process the sorted multi-factor indicators and achieve preliminary classification of indicators according to whether they can be measured using objective data.

[0035] S3: Use targeted methods to collect data on multiple influencing factors. For measurable indicators, the following three methods are mainly used: Collect and analyze historical data on impact indicators; Use simulation software to obtain simulation data for specific scenarios; Build experimental scenarios for field measurements; For indicators that are difficult to measure, data are obtained through subjective evaluation by experts.

[0036] S4: Use a combination of subjective and objective methods to conduct security analysis on the collected data to obtain the weight, influence and risk value of external factors on the communication link security index; in the present invention, the index weight is obtained by combining the hierarchical analysis method with expert scoring, and the index influence degree is calculated and converted by the DS evidence theory and the grey correlation analysis method.

[0037] S5: The security risk value of the communication link affected by the external environment is obtained by comprehensively considering the weights and influences of multiple factors; in the present invention, the possibility rating level of the evaluation indicator is obtained by actual measurement or literature review, and the possibility rating level is combined with the impact evaluation level to calculate the risk value of the evaluation indicator; the risk value of the target layer of the evaluation system is calculated according to the weight vector and the risk value; and evaluation is performed according to the risk value.

[0038] In a specific embodiment, Figure 2 As shown in the figure, the evaluation index system consists of the target layer, the criterion layer, and the solution layer. The target layer is the comprehensive risk value, the criterion layer includes integrity, confidentiality and availability, and the criterion layer is composed of the indicator layer; integrity includes human errors, electromagnetic interference, and modification of transmitted data; confidentiality includes link damage / failure, malware / virus; availability is composed of environmental changes and urban environment; environmental changes include operating altitude, urban environment, and non-urban environment; weather conditions include rainfall, humidity changes, and thunderstorms.

[0039] In a specific embodiment, the risk assessment matrix method includes a possibility assessment level and an impact assessment level; the possibility assessment level and the impact assessment level are the same, including level one, level two, and level three.

[0040] In a specific embodiment, the weights are calculated by combining the analytic hierarchy process with the expert scoring method; the evaluation indicators are scored according to their relative importance; a judgment matrix is ​​constructed according to the scores; the judgment matrix is ​​checked for consistency; and the weight vector of the evaluation indicators is calculated according to the judgment matrix.

[0041] In a specific embodiment, the method of constructing a judgment matrix includes using 1, 3, 5, 7, 9 and the reciprocal of the method to construct the judgment matrix.

[0042] For example, the scheme-level evaluation index is u A = {u A1 ,u A2 ,u A3 ,u A4 ,u A5}, a 5×5 matrix should be formed, and the element in the i-th row and j-th column of the matrix represents the importance of the i-th factor indicator relative to the j-th factor indicator.

[0043] In a specific embodiment, the method for solving the weight vector includes: dividing each column element of the judgment matrix by the sum of the corresponding column elements to obtain a normalized weighted judgment matrix; performing eigenvalue decomposition according to the weighted judgment matrix to obtain the maximum eigenvalue; substituting the maximum eigenvalue into the weighted judgment matrix and solving the corresponding maximum eigenvector; normalizing the maximum eigenvector to obtain the weight vector.

[0044] In a specific embodiment, in order to ensure that the judgment matrix P is reasonable, it is necessary to perform a consistency check on the judgment matrix. The method for performing the consistency check includes: calculating the consistency index CI (consistency index) and CR (consistency ratio); the formula is as follows: Where n represents the order of the judgment matrix, λ max is the maximum eigenvalue of the judgment matrix. RI is obtained by calculating a large number of eigenvalues ​​of the judgment matrix and taking the arithmetic mean; When the consistency ratio is less than 0.1, the judgment matrix passes the consistency test, otherwise the judgment matrix fails the consistency test and needs to be rebuilt and the weight vector is re-calculated.

[0045] In a specific embodiment, the grey correlation analysis method is used to calculate the influence degree of the metric.

[0046] For example, For the factor indicators with similar physical meanings at the solution level, the factor indicator data that can reflect the system behavior characteristics are selected as the reference sequence x0, and the remaining factor indicator data that affect the system behavior are recorded as x i ; Since different factors have different dimensions and data ranges, they need to be normalized; the normalized data is used to calculate the correlation coefficient of different indicator data relative to the reference indicator data according to the following formula: where |x0(k)-x i (k)| represents the absolute value of the difference between the kth data in the i-th indicator sequence and the kth data in the reference sequence, and ρ is the resolution, which is 0.5. It should be noted that the calculated correlation coefficient is the correlation coefficient of a single data in the data sequence relative to the reference data sequence, and weighted aggregation is also required.

[0047] The correlation coefficient of different indicators relative to the reference indicator is calculated by the following formula: The obtained correlation coefficient is converted into an impact assessment level, and the conversion rules are as follows: a correlation coefficient of 0 to 0.34 corresponds to impact assessment level three, a correlation coefficient of 0.34 to 0.67 corresponds to impact assessment level two, and a correlation coefficient of 0.67 to 1 corresponds to impact assessment level one.

[0048] In a specific embodiment, DS evidence theory is used to calculate the influence of difficult-to-measure indicators.

[0049] For example, if the recognition framework is {low, medium, high}, then the subsets of the DS theory recognition framework are {low}, {medium}, {high}, {low, medium}, {medium, high}, {low, high}, {low, medium, high}, is the set of factor indicators to be evaluated. For each element in the indicator set, experts Z = {z1, z2, z3} are invited to score in combination with the recognition framework, and the following data table can be obtained: Table 1 in arrive represents a subset of the recognition framework, Represents the jth expert's opinion on the i-th indicator in the identification framework Assigned basic probability function a.

[0050] In a specific embodiment, according to Table 1, the basic probability function of each indicator is obtained by integrating the opinions of digital experts using the Dempster combination rule. The formula of the Dempster combination rule is as follows: Where k is the normalization constant, and the calculation formula is According to the above formula, the basic probability distribution function b of each indicator under all recognition framework subsets can be obtained: ij , as shown in Table 2 Table 2 The impact evaluation level corresponding to the largest basic probability allocation function value is selected as the final impact evaluation level of the indicator.

[0051] In a specific embodiment, the possibility evaluation level of different evaluation indicators is obtained by actual measurement or reference to literature; the possibility evaluation level is multiplied by the impact evaluation level to obtain the risk value.

[0052] In a specific embodiment, the method of using the weight vector in combination with the risk value to evaluate the communication link is as follows: for each scheme layer indicator included in the criterion layer indicator, the risk value of the scheme layer indicator is multiplied by its corresponding weight value to obtain the security risk value of each criterion layer indicator under the influence of the scheme layer indicator; for the criterion layer indicator included in the target layer, the risk value of the criterion layer indicator is multiplied by its weight value to obtain the comprehensive risk value of the communication link; If the risk value is between one and three, the communication link threat is acceptable; If the risk value is between three and six, the communication link threat needs to be mitigated; If the risk value is between six and nine, the communication link threat is unacceptable.

[0053] The above are only specific embodiments of the present application, so that those skilled in the art can understand that these specific embodiments are only examples, and those skilled in the art can make various omissions, substitutions and changes to the details of the above methods and systems without departing from the principles and essence of the present invention; for example, combining the above method steps to perform substantially the same functions in substantially the same way to achieve substantially the same results is within the scope of the present invention; therefore, the scope of the present invention is limited only by the appended claims.

Claims

1. A multi-factor communication link security assessment method based on data fusion, characterized in that: Implement security assessment of communication links in complex external environments. The methods include: Step 1: Establish a communication link security evaluation system, which consists of a target layer, a criterion layer, and a solution layer. The target layer is the comprehensive security level of the communication link, the criterion layer is the security index of the link, and the solution layer is composed of external factors that threaten the security index; the target layer is composed of criterion layer indicators, and the criterion layer indicators include integrity, confidentiality, availability and other indicators; this system model is used to analyze the defense capability of the communication link under external interference and conduct security assessment for external influences; Step 2: Preprocess the sorted multi-factor indicators, that is, divide the indicators into difficult-to-measure factor indicators and measurable factor indicators according to whether they can be judged by objective data; the difficult-to-measure factor indicators include indicators that cannot be judged by objective data, such as human errors and deliberate sabotage; the measurable factor indicators include indicators that are easy to obtain objective data, such as communication environment and weather conditions; Step 3: Use different methods to collect data on multiple influencing factor indicators. For indicators that can be measured, the following three methods are mainly used: Collect and analyze historical data on factor indicators; Use simulation software to obtain simulation data for specific scenarios; Build experimental scenarios for field measurements; For indicators that are difficult to measure, subjective evaluation by experts is used to obtain data; Step 4: Use a combination of subjective and objective methods to perform security analysis on the collected data, where the combination of subjective and objective methods uses a subjective method for indicators that are difficult to measure and an objective method for indicators that can be measured; The impact value of the communication link security index is calculated based on subjective and objective methods; finally, the weights of all security indicators are calculated using the subjective method; Step 5: Use the hierarchical weighted summation method to calculate the comprehensive risk value of the communication link, and use the risk matrix method to evaluate the external threats to the communication link.

2. According to step 4 of claim 1, it is characterized in that: The weights of all indicators are calculated using subjective methods, including the analytic hierarchy process (AHP), the fuzzy comprehensive evaluation method, the DS evidence theory, etc. The steps of calculating the weights using the analytic hierarchy process in the present invention are as follows: Invite experts to rate the relative importance of the influencing factors; Construct a judgment matrix based on the score values; Performing a consistency check on the judgment matrix; The weight vector of the indicator is calculated based on the obtained judgment matrix.

3. According to claim 2, the method for constructing a judgment matrix is ​​to use scales 1, 3, 5, 7, 9 and their reciprocals to construct a judgment matrix; scale 1 represents that two factors have the same importance; scale 3 represents that one factor is slightly more important than the other factor when compared with the two factors; scale 5 represents that one factor is significantly more important than the other factor when compared with the two factors; scale 7 represents that one factor is strongly more important than the other factor when compared with the two factors; scale 9 represents that one factor is extremely more important than the other factor when compared with the two factors; scales 2, 4, 6, and 8 represent the intermediate values ​​of the above importance, and the reciprocal scale represents the importance of two factors after exchanging positions; the method for constructing a judgment matrix is ​​to select the i-th and j-th influencing indicators in the solution layer for comparison, and fill the corresponding scales into a of the judgment matrix. ij element, at this time a ij Represents the importance of factor i relative to factor j; when i=j, the selected factors are the same and the scale of the factor is 1.

4. The method for solving the weight vector of the influencing factor index according to claim 2 comprises: Dividing each column element of the judgment matrix by the sum of the corresponding column elements to obtain a normalized weighted judgment matrix; Performing eigenvalue decomposition according to the weighted judgment matrix to obtain the maximum eigenvalue; Substitute the maximum eigenvalue into the weighted judgment matrix and solve the corresponding maximum eigenvector; The maximum eigenvector is normalized to obtain the weight vector of the influencing factor index.

5. The method for performing consistency check on a judgment matrix according to claim 2 comprises: Calculate the consistency index CI and consistency ratio CR; The consistency index measures the degree of consistency of the decision maker in the comparison process. A lower consistency index indicates that the comparison results are more consistent, while a higher consistency index may mean that there is a certain degree of inconsistency in the comparison results; the consistency ratio is the ratio of the consistency index to the random consistency index (Random Index, RI), which is used to further evaluate the consistency of the comparison results; the random consistency index is an important parameter for calculating the consistency ratio; the random consistency index is a constant, which is determined according to the dimension of the judgment matrix; the value of the random consistency index is used to evaluate the consistency of the judgment matrix, so as to determine whether the decision maker's comparison is reasonable; the value of the random consistency index is usually provided in the AHP literature and can be found in the relevant AHP references or standards. The following table gives the average random consistency index values ​​of judgment matrices of various orders; The calculation formula of consistency index is as follows: Where n represents the order of the judgment matrix, λ max is the maximum eigenvalue of the judgment matrix; The formula for calculating the consistency ratio is as follows: When the consistency ratio is less than 0.1, the judgment matrix passes the consistency test, otherwise the judgment matrix fails the consistency test and needs to be reconstructed and the weight vector is re-calculated.

6. According to step 4 of claim 1, it is characterized in that: The subjective method is used to calculate the impact of indicators that are difficult to measure. The steps of calculating the impact using the Dempster-Shafer evidence theory are as follows: Construct the identification framework of DS theory, which is: U = {small impact, medium impact, large impact}; Invite several experts to give the basic probability distribution function of each indicator under all identification framework subsets; For a single indicator and the corresponding identification framework subset, the basic probability distribution functions given by experts are fused in pairs using the Dempster fusion rule to obtain the basic probability distribution function of the final single indicator under all identification framework subsets. The subset corresponding to the maximum value of the basic probability distribution function is taken as the final impact evaluation of the indicator. The final impact comments of all indicators are converted into impact evaluation grades.

7. The basic probability distribution function according to claim 6, characterized in that: The basic probability distribution function represents a trust measure for a subset of U based on the current environment.

8. According to claim 6, inviting experts to give the basic probability allocation function for each indicator, the process is as follows: experts allocate trust to subsets of the identification framework U based on subjective judgment, and obtain the basic probability allocation function of each subset corresponding to a single indicator; wherein the basic probability allocation function corresponding to the empty set is specified to be 0, and the sum of the basic probability allocation functions of all subsets is 1.

9. The Dempster fusion rule according to claim 6, wherein the calculation formula is as follows: Where m1(A) and m2(A) represent the trust measures of the two experts that the current indicators make the identification framework subset A valid, B and C are subsets that satisfy the intersection of the two to be A, k is a normalization constant, and the calculation formula is: At this time, B and C are subsets that satisfy the non-empty intersection of the two.

10. According to step 4 of claim 1, an objective method is used to calculate the influence of the measurable indicator, and the objective method includes weight calculation methods such as entropy weight method, grey relational analysis method, TOPSIS method, entropy weight method, etc.; the exemplary steps of calculating the influence degree by grey relational analysis method in the present invention are as follows: Dimensionless processing of the collected data; Determine the reference sequence; Calculate the grey correlation coefficient, the calculation formula is: where |x0(k)-x i (k)| represents the absolute value of the difference between the kth data in the i-th indicator sequence and the kth data in the reference sequence, ρ is the resolution, and its value is 0.5; the calculated correlation coefficient is the correlation coefficient of a single data in the data sequence relative to the reference data sequence; the correlation coefficient of all data is averaged to obtain the grey correlation coefficient of the i-th indicator; The obtained grey correlation coefficient is converted into an impact evaluation grade.

11. The conversion process from the final impact review to the impact evaluation level according to claim 6, wherein the conversion rule is: the impact evaluation level is divided into level 1 to level 3, and the reviews in the recognition framework are converted into impact evaluation levels 1, 2, and 3 respectively according to the impact degree from low to high. According to the above conversion rule, the final film review is converted into the impact level.

12. According to the impact evaluation level conversion process described in claim 11, the process is as follows: the impact evaluation level is divided into level one to level three, each level corresponds to a value interval between 0 and 1 with no intersection, and the impact evaluation level is the interval level where the grey correlation coefficient is located.

13. According to the possibility evaluation level of the indicator in step 5 of claim 1, in an embodiment of the present invention, the security evaluation of the communication link is regarded as a multi-factor problem, and many evaluation indicators need to be considered; in order to determine the possibility evaluation level of these indicators, the following steps can be adopted: For the security assessment of communication links, collect relevant data or literature, covering various evaluation indicators, such as signal strength, spectrum utilization, interference level, etc.; Obtain data related to evaluation indicators through field tests or simulation experiments to support evaluation; Consult relevant literature to understand the typical performance of each evaluation indicator in similar situations to supplement or verify the measured results; Based on the collected data and literature research results, a possible evaluation level is determined for each evaluation indicator, and a standardized rating system (one to three levels) is used to classify the indicators; Through the above steps, the multi-factor communication link security assessment method based on data fusion provided by the present invention can obtain the possibility evaluation level of all influencing factor indicators.

14. According to step 5 of claim 1, the comprehensive risk value is calculated by adopting the hierarchical weighted summation method, and the calculation process is as follows: the possibility evaluation level of the scheme layer indicator is multiplied by the impact degree value to obtain the risk value of the scheme layer indicator; for each scheme layer indicator included in the criterion layer indicator, the risk value of the scheme layer indicator is multiplied by its corresponding weight value to obtain the security risk value of each criterion layer indicator under the influence of the scheme layer indicator; for the criterion layer indicators included in the target layer, the risk value of the criterion layer indicator is multiplied by its corresponding weight value to obtain the comprehensive risk value of the communication link.

15. According to step 5 of claim 1, the risk matrix method is used to evaluate the external threats to the communication link, and the judgment conditions are: If the comprehensive risk value is between one and three, the communication link threat is acceptable; If the comprehensive risk value is between three and six, the communication link threat needs to be mitigated; If the combined risk value is between six and nine, the communication link threat is unacceptable.

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