Adaptive security matrix calling method and component, storage medium and program product

Abstract

The invention provides an adaptive security matrix calling method and component, a computer readable storage medium and a computer program product. The self-adaptive security matrix calling method comprises the following steps: when an API (Application Program Interface) needs to select one micro-service from a plurality of micro-services, calculating a first reference value of each micro-service in the plurality of micro-services based on a first matrix, and calculating a second reference value of each micro-service in the plurality of micro-services based on a second matrix; determining a safety coefficient of each micro-service according to the first reference value of each micro-service, the second reference value of each micro-service, the first weight coefficient and the second weight coefficient; according to the calculated safety coefficient, selecting the link with the minimum safety coefficient from the plurality of micro-services to correspond to the micro-service; the first reference value is related to the security influence degree of the attack point on the micro-service, and the second reference value is related to the security influence degree of the vulnerability of the micro-service. According to the method provided by the invention, the API can accurately select the micro-service with high security from the plurality of micro-services.

Description

Technical Field

[0001] The present application relates to network security technology, and more specifically, to a security matrix calling method when an API selects a microservice. Background Art

[0002] For a scenario, an application programming interface (API) usually corresponds to multiple microservices. When selecting microservices, API may consider factors such as microservice costs and pay less attention to the security of microservices in the network.

[0003] In the patent application with application number 20241028206.0, a microservice selection method based on a security matrix is ​​proposed. In short, when the API selects a standby microservice from multiple microservices, the patent application calculates the first reference value of each microservice in the multiple microservices based on the first matrix, and calculates the second reference value of each microservice in the multiple microservices based on the second matrix; the safety factor of each microservice is determined by the first reference value of each microservice and the second reference value of each microservice; the API selects the safest microservice from the multiple microservices according to the determined safety factor; wherein the first reference value is related to the degree of impact of the attack point on the security of the microservice, and the second reference value is related to the degree of impact of the vulnerability of the microservice on its security. The method of this application thus enables the API to call a microservice with high security from multiple microservices. However, according to this scheme, more than one microservice may have the same safety factor. For example, the first reference values ​​of microservice 1, microservice 2 and microservice 3 determined based on the first matrix (the matrix related to the attack point) are 10, 20 and 30, while the second reference values ​​of microservice 1, microservice 2 and microservice 3 determined based on the second matrix (the matrix related to the vulnerability) are 30, 20 and 10, which will result in the same final security factors for the three services.

[0004] Therefore, for the calling scenario where one API corresponds to multiple microservices, the technical solution of the above patent application did not take into account the situation of multiple identical safety factors when selecting the optimal calling link. Therefore, it did not consider how to avoid this situation, nor how to deal with it when it occurs to solve the problem. Summary of the invention

[0005] According to one aspect of the present application, an adaptive security matrix calling method is provided to solve at least one of the above problems when selecting a microservice based on a security matrix. The method for selecting a microservice for an API according to the example of the present application includes: when an API is to select a calling microservice from a plurality of microservices, a first reference value of each microservice in the plurality of microservices is calculated based on a first matrix, and a second reference value of each microservice in the plurality of microservices is calculated based on a second matrix; the security factor of each microservice is determined by the first reference value of each microservice, the second reference value of each microservice, a first weight coefficient, and a second weight coefficient; wherein the first weight coefficient is related to the optimal function value of the lowest point and the Utopia point of the first reference value, and the second weight coefficient is related to the optimal function value of the lowest point and the Utopia point of the second reference value; according to the calculated security factor, a microservice with the smallest security factor is selected from the plurality of microservices; wherein the first reference value is related to the degree of impact of the attack point on the security of the plurality of microservices, and the second reference value is related to the degree of impact of the vulnerability of the plurality of microservices on their security.

[0006] Optionally, in the method, determining the safety factor of each microservice by using the first reference value of each microservice, the second reference value of each microservice, the first weight coefficient, and the second weight coefficient includes calculating the safety factor according to the following formula: Among them, Service j is the safety factor of the j-th microservice among the multiple microservices, ω1 is the first weight coefficient, ω2 is the second weight coefficient, the values ​​of ω1 and ω2 are both between 0 and 1 and the sum of ω1 and ω2 is 1, Aj is the first reference value of the j-th microservice among the multiple microservices; Pj is the second reference value of the j-th microservice among the multiple microservices; and in, μ 1 is the weight of the first reference value, θ 1 is the normalization factor of the first reference value; μ 2 is the weight of the second reference value, θ 2 is a normalization factor of the second reference value; in, θ 1 is determined according to the optimal function value among the lowest point and the Utopia point of the first reference value, θ 2 is determined according to the optimal function value in the lowest point and the Utopia point of the second reference value.

[0007] Optionally, in the method, the normalization factor of the first reference value is θ 1 and the normalization factor of the second reference value θ 2 are determined in the following ways: in, , ;as well as in, , , , .

[0008] Optionally, in the method, the first matrix is ​​related to an attack against the microservice, and the second matrix is ​​related to a vulnerability of the microservice.

[0009] Optionally, in the method, the first matrix is ​​set to an i×j matrix, i represents rows and j represents columns, wherein 1≤j≤n, n is the number of the microservices; 1≤i≤m, m is the number of attack points against the microservices; the second matrix is ​​set to an e×j matrix, e represents rows and j represents columns, wherein 1≤j≤n, n is the number of the microservices; 1≤ e ≤p, p is the number of vulnerabilities against the microservices.

[0010] Optionally, in the method, calculating the first reference value of each microservice in the plurality of microservices based on the first matrix includes calculating the first reference value according to the following formula: Among them, A j is the first reference value of the jth microservice among the multiple microservices, V ij Represents the value of the i-th row and j-th column in the first matrix.

[0011] Optionally, in the method, calculating the second reference value of each microservice based on the second matrix includes calculating the second reference value according to the following formula: Among them, P j is the second reference value of the j-th microservice, V ej Represents the value of the e-th row and j-th column in the second matrix.

[0012] According to another aspect of the present application, a component is further provided, the component comprising a processor and a memory, the memory storing instructions, and the processor being configured to implement any one of the above methods when executing the instructions.

[0013] A computer-readable storage medium is also provided, on which instructions are stored, and when the instructions are executed, any one of the above methods is implemented.

[0014] A computer program product is also provided, comprising computer program instructions, which implement any one of the above methods when executed.

[0015] According to the examples of the present application, the first weight coefficient and the second weight coefficient are respectively limited by the optimal function values ​​of the Utopia point, which reduces the probability of overflowing the reasonable interval and avoids the situation where multiple safety factor values ​​are equal. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The present application will be more fully understood by referring to the following detailed description of specific embodiments in conjunction with the accompanying drawings, in which the same reference numerals refer to the same elements, wherein: Figure 1 is a flow chart of an adaptive security matrix calling method according to an example of the present application; Figure 2 is a schematic diagram of an example of the first matrix; Figure 3 is a schematic diagram of an example of the second matrix; Figure 4 It is a process of selecting microservices by calling an adaptive security matrix according to some specific examples of this application; Figure 5 The specific process of determining the minimum safety factor in some examples is illustrated; Figure 6 It is a structural diagram of the adaptive security matrix calling component according to the present application. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the following will be combined with the accompanying drawings to clearly and completely describe the implementation methods of the present application. It should be noted that the described implementation methods are only part of the implementation methods of the technical solution of the present application, not all. All other implementation methods obtained by ordinary technicians in this field based on the implementation methods recorded in the application documents without paying creative work are covered by the protection scope of the present application.

[0018] In this article, microservices refer to small services that consist of a single application. Generally speaking, microservices have their own processes and lightweight processing, and microservices are designed based on business functions.

[0019] Figure 1 is a flow chart of an adaptive security matrix calling method according to an example of this application. Figure 1As shown, in step S100, when the API selects a calling microservice from multiple microservices, the first reference value of each microservice in the multiple microservices is calculated based on the first matrix, and the second reference value of each microservice in the multiple microservices is calculated based on the second matrix. The first reference value is related to the degree of impact of the attack point on the security of the microservice, and the second reference value is related to the degree of impact of the vulnerability of the microservice on its security. In step S102, the safety factor of each microservice is determined by the first reference value of each microservice, the second reference value of each microservice, the first weight coefficient, and the second weight coefficient. The first weight coefficient is related to the optimal function value in the lowest point and the Utopia point of the first reference value, and the second weight coefficient is related to the optimal function value in the lowest point and the Utopia point of the second reference value. According to step S100, the first reference value and the second reference value of each microservice are calculated, and in step S102, the safety factor of the microservice is calculated according to the first reference value, the second reference value, the first weight coefficient, and the second weight coefficient of each microservice, thereby determining the safety factor of each microservice in the multiple microservices. In step S104, based on the determined safety factor, a microservice with the smallest safety factor is selected from multiple microservices. Assuming that there are n microservices, when the values ​​of the first weight coefficient and the second weight coefficient are randomly estimated, the n determined safety factors may have only one minimum value, or there may be more than one safety factor value that is equal and tied as the minimum. However, according to the example of the present application, the first weight coefficient and the second weight coefficient are respectively limited by the optimal function value of the Utopia point, which reduces the probability of overflowing the reasonable interval and avoids the situation where multiple safety factor values ​​are equal. According to the example of the present application, the smaller the value of the safety factor, the higher the security of the microservice.

[0020] As an example, the first matrix and the second matrix are set for the attack points and vulnerabilities of the multiple microservices, respectively. In the example of the present application, the first matrix is ​​set for the attack point and the first reference value is related to the degree of security impact of the attack point on the microservice, and the second matrix is ​​set for the microservice vulnerability and the second reference value is related to the degree of security impact of the vulnerability on the microservice. But this is only an example and not a limitation. For example, as an alternative, the first matrix can be set for the vulnerabilities of multiple microservices and the second matrix can be set for the attack point. As an example, the first weight coefficient is related to the lowest point of the first reference value and the optimal function value in the utopian point, and the second weight coefficient is related to the lowest point of the second reference value and the optimal function value in the utopian point.

[0021] In the example of the present application, the first matrix is ​​set to be an i× j matrix, where i represents a row and j represents a column, 1≤j≤n, n is the number of the microservices, 1≤i≤m, m is the number of attack points against the microservices. It should be understood that n and m are both integers.

[0022] Figure 2This is an example of the first matrix. Figure 2 As shown, in this example, there are 11 microservices in total, that is, the API must select one calling microservice from the 11 microservices. The 11 microservices are arranged from left to right from microservice 1 to microservice 11. At the same time, the 13 rows of the first matrix correspond to 13 attack points, and the attack points are: SQL such as, cross-site scripting attacks, operating system command injection, newline character injection, LDAP injection, Xpath injection, SSI injection, directory traversal, forced browsing, cross-site request masquerade, incorrect file and directory allocation, incorrect error handling, and unnecessary information. The area shown by the dotted box is the area of ​​the first matrix 20. Each value in the first matrix 20 represents the degree to which the microservice in its column is attacked by the attack point in the row where the value is located, such as point V when i and j are both 1. 11 The value of is 9, which means that the degree of SQL injection attack on microservice 1 is 9. ij The weight value corresponding to the i-th row and j-th column is called the weight value corresponding to the i-th row and j-th column, which indicates the degree to which the j-th column microservice is attacked by the i-th row attack point. The larger the value, the greater the degree to which it is attacked. The value of the impact of the j-th column microservice (i.e., the j-th microservice among the n microservices) in the first matrix 20 on the attack of all attack points is called the first reference value in this application. The first reference value is calculated by formula (1): Among them, A j V is the first reference value, which indicates the degree to which the microservice in the jth column is attacked by all attack points. The larger the value, the higher the degree of attack. ij represents the value of the i-th row and j-th column in the first matrix, that is, the attack degree of the i-th attack point on the j-th column microservice. The values ​​in the first matrix are predetermined based on the attack data of the microservice and the attack point.

[0023] It should be noted that in Figure 2 In the figure, the numerical value shown in the column 21 on the left side of the first matrix 20 indicates the degree of threat of the corresponding attack point to the microservice attack, and the numerical value decreases from high to low, wherein the numerical value of 5 indicates the highest threat degree.

[0024] In the example of the present application, the second matrix is ​​set to an e×j matrix, e represents rows and j represents columns, where 1≤j≤n, n is the number of microservices; 1≤e≤p, p is the number of vulnerabilities of the microservices, and p is an integer.

[0025] Figure 3 is an example of the second matrix, in which the microservices are Figure 2Therefore, a total of 11 microservices are included, arranged from microservice 1 to microservice 11 from left to right. This example includes a total of 13 vulnerabilities, which are: vulnerabilities of trusted sites, vulnerabilities of browsers and browser plug-ins, terminal users, removable storage devices, phishing, XML external entities, keyboard loggers, multiple attacks, dependence on the web, confidentiality gaps, springboards, gateway interface vulnerabilities, and compromised access control. The area shown by the dotted box is the area of ​​the second matrix 30. Each value in the second matrix represents the impact of the vulnerability corresponding to the row where the value is located on the security of the microservice corresponding to the column where the value is located. In this example, the larger the value, the worse the security of the microservice is caused by the vulnerability. For example, point V when e and j are both 1 11 The value of is 9, indicating that the vulnerability of the trusted site has an impact on the security of microservice 1 of 9, that is, the vulnerability has a great impact on the security of microservice 1, making its security very poor. The impact of the vulnerability in the second matrix on the security of the microservice in the jth column is referred to as the second reference value in this application, which can be calculated by formula (2): Among them, P j is the second reference value, that is, the impact of the vulnerability on the security of the j-th column microservice, V ej Identifies the value of the e-th row and the j-th column, that is, the security impact of the vulnerability in the e-th row on the service in the j-th column.

[0026] The numerical value shown in the column 31 on the left side of the area of ​​the second matrix 30 indicates the degree of security threat caused by the corresponding vulnerability to the microservice. The numerical value decreases from high to low, where the numerical value is 5, the highest threat level, and then the numerical value decreases from 4 to 1. The numerical value shown in the row 32 on the upper side of the area of ​​the second matrix 30 indicates the degree of security threat to the corresponding microservice due to the vulnerability. The larger the value, the more likely the service is to have security problems due to the vulnerability. For example, the value of microservice 1 is 13, and the value of microservice 10 is 4, which means that microservice 1 is more likely to be attacked due to the vulnerability, resulting in very low security, but service 10 is much better.

[0027] After determining the first reference value of the first matrix and the second reference value of the second matrix, the safety factor of each microservice can be calculated according to formula (3): Among them, Servicej represents the safety factor of the jth microservice; ω1 is the first weight coefficient, which represents the severity of the attack point and is used to measure the probability of the attack point; ω2 is the second weight coefficient, which represents the severity of the vulnerability and is used to measure the probability of the vulnerability; the sum of ω1 and ω2 is 1. According to some examples of this application, ω1 is calculated according to formula (4), and ω2 is calculated according to formula (5): (4) (5) in, μ 1 is the weight of the first reference value, θ 1 is the normalization factor of the first reference value; μ 2 is the weight of the second reference value, θ 2 is the normalization factor of the second reference value. As an example, the normalization factor of the first reference value is θ 1 and the normalization factor of the second reference value θ 2 is determined according to formula (6) and formula (7) respectively: (6) (7) in, , ;and , , , .

[0028] According to an embodiment of the present application, the first weight coefficient ω1 and the second weight coefficient ω2 are calculated according to formulas (4) and (5), respectively, and according to the example of the present application, when calculating the first weight coefficient ω1 and the second weight coefficient ω2, normalization factors are defined respectively. θ 1 and the normalization factor θ 2 (refer to formula (6) and formula (7)), thereby reducing the probability that the first weight coefficient ω1 and the second weight coefficient ω2 overflow the reasonable interval, thereby avoiding the possibility that the calculated safety factor values ​​are equal due to random values ​​of ω1 and ω2, and thus outputting the globally optimal microservice call link.

[0029] According to the example of this application, the API can select the microservice with the smallest safety factor from multiple microservices based on the safety factor of the microservice calculated by formula (3), that is, select the optimal microservice with the highest security.

[0030] For example, if there are three microservices, the attack point matrix weights are 10, 20 and 30 respectively, and the vulnerability matrix weights are 30, 20 and 10 respectively. If the first weight coefficient ω1 and the second weight coefficient ω2 are randomly selected between 0 and 1, the value of the safety factor service may be 20, making it impossible to select the best one. However, according to the example of this application, the probability of the first weight coefficient ω1 and the second weight coefficient ω2 overflowing the reasonable range is greatly reduced and almost eliminated, which basically avoids the situation of the same safety factor. That is, in this example, only a minimum safety factor will be calculated, and there is only one link corresponding to the most secure service to choose.

[0031] It should be noted that in the examples listed above, the number of microservices, the number and type / name of attack points, and the number and type / name of vulnerabilities are all for illustration and are not limited to those listed here. In actual applications, the number and type of attack points may be different from those listed here, and the number and type / name of vulnerabilities may also be different from those listed here.

[0032] Any of the methods for selecting microservices for APIs according to the examples of this application can be executed by electronic devices such as computers, tablets, and smart phones. For example, identity verification microservices from different suppliers are connected to smart phones through different communication links. When the API of the smart phone needs to select a microservice for identity verification, the security of each microservice can be evaluated according to the security factor by executing the method described herein, so as to select the microservice corresponding to one of the links with the highest security. In the prior art, when an API selects a calling microservice from multiple microservices, it does not consider whether to call the microservice from the perspective of security, but considers the cost more, and lacks a security assessment model during the calling process. In some examples of considering security, although a matrix related to the attack point and a matrix related to the vulnerability are introduced, and a decision function based on the two matrices is proposed, it is possible that the same security factor will cause the link to be unable to be selected. According to the example of this application, when determining the security factor, the weight coefficient is related to the lowest point of the reference value and the optimal function value of the utopia point, so that it has an upper and lower limit, so that the possibility of the same security factor is almost zero, thereby avoiding the problem that the API cannot call the microservice due to the same security factor. On the contrary, the microservice with the best link can always be called from the microservice.

[0033] Figure 4 It is a process of selecting microservices based on the adaptive security matrix call of some specific examples of this application. Figure 4As shown, in step S400, start. In step S402, initialize the security impact value. The security impact value here refers to the impact value of the initialization attack point and vulnerability on the microservice. The attack points on different microservices, the impact of the same attack point on different microservices, different vulnerabilities, the impact of different vulnerabilities on different microservices, etc., can all be acquired and determined in advance based on big data. For example, these data have been acquired, determined, and stored in the memory in advance so that they can be called when executing the method of the example of the present application to complete the initialization. For example, for the identity verification microservice as one of the microservices, the attack point can be determined based on historical or test data, and the situation in which each attack point in the attack point is used to attack each microservice, etc., thereby presetting the values ​​used to establish the matrix and the second matrix.

[0034] In step S404, the horizontal and vertical coordinates of the matrix are defined. Defining the horizontal coordinates means listing the multiple microservices involved in this example, and defining the vertical coordinates means listing the attack points and vulnerabilities respectively. In step S406, the vertical coordinate of the matrix is ​​determined. If the vertical coordinate represents the attack point, the process proceeds to step S408. If the vertical coordinate represents the vulnerability, the process proceeds to step S410. In step S408, a first matrix is ​​constructed, i.e., a matrix related to the attack point, also known as the attack point matrix. Specifically, a model for constructing the matrix has been set up, and the security impact values ​​of the attack points on the specific microservices are obtained from big data, thereby constructing the first matrix. In step S410, a second matrix is ​​constructed, i.e., a matrix related to the vulnerability, also known as the vulnerability matrix. Specifically, a model for constructing the matrix has been set up, and the security impact values ​​of the vulnerabilities on the specific microservices are obtained from big data, thereby constructing the second matrix. In step S412, a decision function is constructed based on the relevant information of the first matrix and the second matrix, such as row and column information, such as determining the values ​​of the coefficients in formula (3). In step S414, the safety factor value of each microservice is calculated by formula (3). In step S416, the calculated safety factor values ​​are sorted. In step S418, the microservice with the smallest safety factor value in the API call sorting, that is, the microservice corresponding to the most secure link. In step S420, the process ends.

[0035] Figure 5The specific process of determining the minimum safety factor in some examples is illustrated. In this example, the method according to the present application can be combined with the existing scheme for calculating the safety factor. There are multiple minimum safety factor values ​​determined, and the process proceeds to step S500 to start the calculation of ω, where ω is the product of μ and θ. In step S5001, ω1 is calculated according to formula (4), where θ1 is determined according to formula (6). In step S5002, ω1 is calculated according to formula (5), where θ1 is determined according to formula (7). In step S510, the safety factor Service is calculated according to formula (3). In step S512, the minimum safety factor is selected from the multiple safety factors calculated, and the corresponding microservice is the safest microservice, which can be selected to link to the microservice.

[0036] According to an example of the present application, a component is also provided. Figure 6 is a schematic diagram of the structure of the component. Figure 6 As shown, the component is configured to be able to execute any one of the adaptive security matrix calling methods according to the present application. The component includes a processor 40 and a memory 42. The memory 42 stores instructions. As an example, the component can be an electronic device such as a computer, a tablet, a smart phone, etc.

[0037] According to an example of the present application, a computer-readable storage medium is also provided, on which instructions are stored, and when the stored instructions are executed, any one of the method examples described herein is implemented.

[0038] A computer program product is also provided, which, when executed by a processor, implements any one of the method examples described herein.

[0039] In the absence of contradiction or conflict, the technical features in the examples described herein may be combined with each other to form implementations not described herein, which should also be covered by the scope of this application.

[0040] Although specific embodiments of the present application have been shown and described in detail to illustrate the principles of the present application, it should be understood that the present application may be implemented in other ways without departing from such principles, for example, the technical features of the various embodiments / examples / examples of the present application may be combined with each other to form new implementation methods.

Claims

1. An adaptive security matrix calling method, characterized in that: The method comprises: When the API is to select a calling microservice from a plurality of microservices, a first reference value of each microservice in the plurality of microservices is calculated based on the first matrix, and a second reference value of each microservice in the plurality of microservices is calculated based on the second matrix; The safety factor of each microservice is determined by the first reference value of each microservice, the second reference value of each microservice, a first weight coefficient, and a second weight coefficient; wherein the first weight coefficient is related to the optimal function value of the lowest point and the Utopia point of the first reference value, and the second weight coefficient is related to the optimal function value of the lowest point and the Utopia point of the second reference value; According to the calculated safety factor, selecting a microservice with the smallest safety factor from the multiple microservices; The first reference value is related to the impact degree of the attack point on the security of the multiple microservices, and the second reference value is related to the impact degree of the vulnerabilities of the multiple microservices on their security.

2. The method according to claim 1, characterized in that Determining the safety factor of each microservice by using the first reference value of each microservice, the second reference value of each microservice, the first weight coefficient, and the second weight coefficient includes calculating the safety factor according to the following formula: Among them, Service j is the safety factor of the jth microservice among the multiple microservices, ω1 is the first weight coefficient, ω2 is the second weight coefficient, the values ​​of ω1 and ω2 are both between 0 and 1 and the sum of ω1 and ω2 is 1, Aj is the first reference value of the jth microservice among the multiple microservices; Pj is the second reference value of the jth microservice among the multiple microservices; wherein 1≤j≤n, and n is the number of the microservices; and in, μ 1 is the weight of the first reference value, θ 1 is the normalization factor of the first reference value; μ 2 is the weight of the second reference value, θ 2 is a normalization factor of the second reference value; in, θ 1 is determined according to the optimal function value among the lowest point and the Utopia point of the first reference value, θ 2 is determined according to the optimal function value in the lowest point and the Utopia point of the second reference value.

3. The method according to claim 2, characterized in that The normalization factor of the first reference value θ 1 and the normalization factor of the second reference value θ 2 are determined in the following ways: in, , ;as well as in, , , , .

4. The method according to any one of claims 1 to 3, characterized in that The first matrix is ​​related to attacks against the microservice, and the second matrix is ​​related to vulnerabilities of the microservice.

5. The method according to claim 4, characterized in that The first matrix is ​​set to be an i×j matrix, i represents a row and j represents a column, where 1≤j≤n, and n is the number of the microservices; 1≤i≤m, m is the number of attack points against the microservice; the second matrix is ​​set to an e×j matrix, e represents rows and j represents columns, where 1≤j≤n, n is the number of the microservices; 1≤e≤p, p is the number of the vulnerabilities of the microservice.

6. The method according to claim 5, characterized in that The calculating the first reference value of each microservice in the multiple microservices based on the first matrix includes calculating the first reference value of the j-th microservice in the multiple microservices according to the following formula: Among them, A j is the first reference value of the jth microservice among the multiple microservices, V ij Represents the value of the i-th row and j-th column in the first matrix.

7. The method according to claim 6, characterized in that Calculating the second reference value of each microservice in the multiple microservices based on the second matrix includes calculating the second reference value of the j-th microservice in the multiple microservices according to the following formula: Among them, P j is the second reference value of the j-th microservice, V ej Represents the value of the e-th row and j-th column in the second matrix.

8. A component for adaptive security matrix calling, characterized in that: The component comprises a processor and a memory, the memory storing instructions, and the processor being configured to implement the method according to any one of claims 1 to 7 when executing the instructions.

9. A computer-readable storage medium storing instructions, characterized in that: When the instructions are executed, the method according to any one of claims 1 to 7 is implemented.

10. A computer program product comprising computer program instructions, characterized in that When the computer program instructions are executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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