CN113806799A - Block chain platform safety intensity assessment method and device - Google Patents

Block chain platform safety intensity assessment method and device Download PDF

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CN113806799A
CN113806799A CN202110992024.XA CN202110992024A CN113806799A CN 113806799 A CN113806799 A CN 113806799A CN 202110992024 A CN202110992024 A CN 202110992024A CN 113806799 A CN113806799 A CN 113806799A
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马兆丰
段鹏飞
张宇青
刘霄
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Beijing University of Posts and Telecommunications
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Abstract

The invention provides a block chain platform security strength evaluation method and a block chain platform security strength evaluation device, wherein the method comprises the following steps: establishing a fuzzy hierarchical analysis hierarchical structure model based on a block chain hierarchical architecture; establishing a fuzzy judgment matrix based on scales expressed by fuzzy triangular numbers determined by pairwise comparison of safety intensity importance of different indexes in the index layer corresponding to the elements in the criterion layer, establishing a fuzzy judgment matrix based on scales expressed by fuzzy triangular numbers determined by pairwise comparison of safety intensity importance of different elements in the criterion layer corresponding to the elements in the target layer, and utilizing a weight coefficient of elements or indexes in a confidence ranking method; and acquiring the subjective scores of all indexes in an index layer of the block chain platform, weighting and summing the subjective scores of all indexes corresponding to the elements to obtain the weighted scores of the corresponding elements in the criterion layer, and weighting and summing the weighted scores of all elements in the criterion layer to obtain an evaluation result. By the scheme, the evaluation accuracy of the safety intensity of the block chain can be improved.

Description

Block chain platform safety intensity assessment method and device
Technical Field
The invention relates to the technical field of block chains, in particular to a block chain platform security strength evaluation method and device.
Background
The block chain is a non-falsifiable distributed account book, and is a multi-node, self-organizing, non-falsifiable, safe and credible distributed account book system which is based on a cryptology algorithm, based on a specific consensus mechanism, and adopts a P2P network to perform data synchronization by constructing a time-ordered chain data structure taking blocks as units. In general, a blockchain system is composed of a network layer, a consensus layer, a stimulus layer, a contract layer, and an application layer (software/hardware application). Wherein, the data layer encapsulates the bottom data block and the related data encryption and time stamp technology; the network layer comprises a distributed networking mechanism, a data transmission mechanism, a data verification mechanism and the like; the consensus layer mainly encapsulates various consensus algorithms of the network nodes; the incentive layer integrates economic factors into a block chain technology system, and mainly comprises an economic incentive issuing mechanism, an economic incentive distributing mechanism and the like; the contract layer mainly encapsulates various scripts, algorithms and intelligent contracts and is the basis of the programmable characteristic of the block chain; the application layer encapsulates various application scenarios and cases of the blockchain. From the aspect of application form, the application layer can be divided into software application and hardware application. From the application scope, the application layer can be divided into programmable currency, programmable finance and programmable society.
The rapid development and wide application of the block chain technology are producing great influence on people's production and life. As the application of the block chain technology in the social and economic fields is continuously expanded, the safety problem of the block chain is concerned more and more. Therefore, the research on the safety risk of the block chain is becoming a research hotspot at home and abroad.
In recent years, various methods have been proposed for detecting and evaluating the security risk of blockchains. Currently, most studies are to use a mathematical method to analyze the influence of each attack (such as 51% attack, eclipse attack, physical attack, etc.) in the blockchain, and to evaluate the security of the blockchain. The variety and number of attacks in a blockchain is large, and it is not comprehensive to analyze the role of each attack individually.
In order to comprehensively evaluate the safety of a block chain, a method for evaluating the safety risk of the block chain is provided from the perspective of a technical system architecture and calculation power, the method firstly establishes a block chain trusted calculation base according to the block chain technical system architecture, and then provides a safety sensitivity analysis method combining hierarchical analysis and pairing comparison, weights are distributed for each factor influencing the safety risk of the block chain, and finally a block chain safety risk evaluation model is designed.
The subjective evaluation aiming at the safety intensity of the block chain platform has important significance in the development and supervision process of the block chain platform. However, it is relatively easy for evaluators to score the safety of a single index, and it is difficult to score the safety intensity of the whole blockchain platform. At present, a suitable method for evaluating the safety intensity of a blockchain platform is rarely available, and as the application of blockchain technology in the social and economic fields is continuously expanded, the evaluation of the safety intensity of the blockchain platform is very important, so that a method capable of effectively evaluating the safety intensity of the blockchain platform is urgently needed.
Disclosure of Invention
In view of this, the present invention provides a method and an apparatus for evaluating the security strength of a blockchain platform, so as to improve the accuracy of the evaluation result of the security strength of the blockchain platform.
In order to achieve the purpose, the invention is realized by adopting the following scheme:
according to an aspect of the embodiments of the present invention, there is provided a method for evaluating security strength of a blockchain platform, including:
establishing a fuzzy hierarchical analysis hierarchical structure model for evaluating the safety intensity of a block chain platform based on a block chain technology hierarchical architecture; the fuzzy hierarchical analysis hierarchical structure model comprises a target layer, a criterion layer and an index layer; the target layer is an upper layer of the criterion layer, and the index layer is a lower layer of the criterion layer; the elements of the target layer comprise blockchain platform security strengths corresponding to the elements of the criteria layer; elements of the criteria layer include system security, user security, contract security, consensus security, data security, and network security; indexes of the index layer comprise system fault tolerance capability, incentive mechanism security, traceability management security, user identity security, private data security, user account security, contract attack resistance capability, contract algorithm normalization, consensus mechanism robustness, consensus mechanism effectiveness, data privacy security, data storage security, data sharing security, propagation mechanism security, verification mechanism security and P2P network structure security; the system security corresponds to the system fault tolerance capability, the excitation mechanism security and the traceability management security; user security corresponds to user identity security, private data security, and user account security; contract security corresponds to contract attack resistance and contract algorithm normalization; consensus security corresponds to consensus mechanism robustness and consensus mechanism effectiveness; data security corresponds to data privacy security, data storage security, and data sharing security; network security corresponds to propagation mechanism security, verification mechanism security, and P2P network fabric security;
based on fuzzy scales represented by fuzzy triangular numbers, which are determined by pairwise comparison of safety intensity importance of different indexes in an index layer corresponding to each element in a criterion layer, constructing a fuzzy judgment matrix corresponding to the corresponding element of the criterion layer, based on fuzzy scales represented by fuzzy triangular numbers, which are determined by pairwise comparison of safety intensity importance of different elements in the criterion layer corresponding to safety intensity of a block chain platform in a target layer, constructing a fuzzy judgment matrix corresponding to safety intensity of the block chain platform of the criterion layer, calculating a weight coefficient of each index in the index layer corresponding to the corresponding element in the criterion layer by using a confidence degree sorting method based on the fuzzy judgment matrix corresponding to the element in the criterion layer, and calculating a weight coefficient of each element in the criterion layer by using a confidence degree sorting method based on the fuzzy judgment matrix corresponding to the element in the target layer;
and acquiring subjective scores of all indexes in an index layer of the block chain platform, performing weighted summation on the subjective scores of all indexes in the index layer corresponding to each element in the criterion layer by using the weight coefficient of the corresponding index to obtain weighted scores of the corresponding elements in the criterion layer, performing weighted summation on the weighted scores of all elements in the layer by using the weight coefficient of the corresponding element to obtain a final score of the safety strength of the block chain platform of a target layer of the block chain platform, and taking the final score as the safety strength evaluation result of the block chain platform.
In some embodiments, constructing a fuzzy judgment matrix corresponding to the corresponding element of the criterion layer based on fuzzy scales expressed by fuzzy triangular numbers determined by pairwise comparison of security strength importance of different indexes in an index layer corresponding to each element in the criterion layer, and constructing a fuzzy judgment matrix corresponding to block chain platform security strength of the criterion layer based on fuzzy scales expressed by fuzzy triangular numbers determined by pairwise comparison of security strength importance of different elements in the criterion layer corresponding to block chain platform security strength in the target layer, includes:
based on comparing the safety intensity importance of different indexes in the index layer corresponding to each element in the criterion layer two by two and defining a determined fuzzy scale represented by fuzzy triangle number according to a set fuzzy scale, constructing a fuzzy judgment matrix corresponding to the corresponding element of the criterion layer, and based on comparing the safety intensity importance of different elements in the criterion layer corresponding to the block chain platform safety intensity in the target layer two by two and defining a determined fuzzy scale represented by fuzzy triangle number according to the set fuzzy scale, constructing a fuzzy judgment matrix corresponding to the block chain platform safety intensity of the criterion layer; wherein the set fuzzy scale definition includes a reference scale and a corresponding security strength significance relationship specification.
In some embodiments, calculating a weight coefficient of each index in the index layer corresponding to the corresponding element in the criterion layer by using a confidence ranking method based on the fuzzy judgment matrix corresponding to the element in the criterion layer includes:
calculating a probability matrix of a fuzzy judgment matrix corresponding to each element in the criterion layer;
calculating a fuzzy judgment matrix of a fuzzy judgment matrix corresponding to each element in the criterion layer;
multiplying the probability matrix of the fuzzy judgment matrix corresponding to each element in the criterion layer by the corresponding position matrix element of the corresponding fuzzy judgment matrix to obtain a corresponding adjustment judgment matrix;
calculating a reachable matrix corresponding to a fuzzy complementary judgment matrix corresponding to the adjustment judgment matrix corresponding to each element in the criterion layer;
judging whether the corresponding fuzzy complementary judgment matrix has satisfactory consistency according to diagonal elements of the reachable matrix corresponding to each element in the criterion layer, and calculating the weight coefficient of the corresponding element in the criterion layer based on the corresponding fuzzy complementary judgment matrix under the condition that the corresponding fuzzy complementary judgment matrix has satisfactory consistency;
calculating the weight coefficient of each element in the criterion layer by using a confidence ranking method based on the fuzzy judgment matrix corresponding to the element in the target layer, wherein the method comprises the following steps:
calculating a probability matrix of a fuzzy judgment matrix corresponding to the safety intensity of a block chain platform in a target layer;
calculating a fuzzy judgment matrix of a fuzzy judgment matrix corresponding to the safety intensity of the block chain platform in the target layer;
multiplying the probability matrix of the fuzzy judgment matrix corresponding to the block chain platform safety intensity in the target layer by the corresponding position matrix element of the corresponding fuzzy judgment matrix to obtain a corresponding adjustment judgment matrix;
calculating a reachable matrix corresponding to a fuzzy complementary judgment matrix corresponding to an adjustment judgment matrix corresponding to the block chain platform safety strength in the target layer;
and judging whether the corresponding fuzzy complementary judgment matrix has satisfactory consistency according to the diagonal elements of the reachable matrix corresponding to the block chain platform safety strength in the target layer, and calculating the weight coefficient of the block chain platform safety strength in the target layer based on the corresponding fuzzy complementary judgment matrix under the condition that the corresponding fuzzy complementary judgment matrix has satisfactory consistency.
In some embodiments, calculating a weight coefficient of each index in the index layer corresponding to the corresponding element in the criterion layer by using a confidence ranking method based on the fuzzy judgment matrix corresponding to the element in the criterion layer, further includes:
under the condition that the fuzzy complementary judgment matrixes corresponding to the elements in the criterion layer do not have satisfactory consistency, adjusting the corresponding fuzzy judgment matrixes to obtain fuzzy complementary judgment matrixes with satisfactory consistency;
calculating the weight coefficient of each index in the index layer corresponding to the corresponding element in the criterion layer by using a confidence ranking method based on the fuzzy judgment matrix corresponding to the element in the criterion layer, and further comprising the following steps:
and under the condition that the fuzzy complementary judgment matrix corresponding to the block chain platform safety intensity in the target layer does not have satisfactory consistency, adjusting the corresponding fuzzy judgment matrix to obtain the fuzzy complementary judgment matrix with the satisfactory consistency.
In some embodiments, calculating a reachable matrix corresponding to the fuzzy complementary determination matrix corresponding to the adjustment determination matrix corresponding to each element in the criterion layer includes:
under the condition that the adjustment judgment matrix corresponding to the element in the criterion layer is a fuzzy complementary judgment matrix, the fuzzy complementary judgment matrix corresponding to the corresponding adjustment judgment matrix is the fuzzy complementary judgment matrix; under the condition that the adjustment judgment matrix corresponding to the element in the criterion layer is not the fuzzy complementary judgment matrix, transforming the corresponding adjustment judgment matrix into the fuzzy complementary judgment matrix; calculating reachable matrixes corresponding to the corresponding fuzzy complementary judgment matrixes;
calculating a reachable matrix corresponding to a fuzzy complementary judgment matrix corresponding to an adjustment judgment matrix corresponding to the block chain platform security strength in the target layer, wherein the reachable matrix comprises the following steps:
under the condition that the adjustment judgment matrix corresponding to the block chain platform safety intensity in the target layer is a fuzzy complementary judgment matrix, correspondingly adjusting the fuzzy complementary judgment matrix corresponding to the judgment matrix to be the fuzzy complementary judgment matrix; under the condition that the adjustment judgment matrix corresponding to the block chain platform safety intensity in the target layer is not a fuzzy complementary judgment matrix, converting the corresponding adjustment judgment matrix into a fuzzy complementary judgment matrix; and calculating a reachable matrix corresponding to the corresponding fuzzy complementary judgment matrix.
In some embodiments, the probability matrix is represented as:
Figure BDA0003232653190000051
wherein B represents a probability matrix, BijMatrix elements of ith row and jth column of the probability matrix, n represents the number of elements or indexes corresponding to the fuzzy judgment matrix, lij、mijAnd uijRespectively representing an upper bound value, a median value and a lower bound value in a fuzzy scale represented by fuzzy triangular numbers corresponding to matrix elements of the ith row and the jth column in the fuzzy judgment matrix;
the fuzzy evaluation matrix is represented as:
Figure BDA0003232653190000052
wherein S represents a fuzzy judgment matrix, n represents the number of elements or indexes corresponding to the fuzzy judgment matrix, and lij、mijAnd uijRespectively representing an upper bound value, a median value and a lower bound value in a fuzzy scale represented by fuzzy triangular numbers corresponding to matrix elements of the ith row and the jth column in the fuzzy judgment matrix; e.g. of the typeij＝uij-lij，eijRepresenting a fuzzy judgment interval;
the transformation formula for transforming the adjustment judgment matrix into the fuzzy complementary judgment matrix is as follows:
Figure BDA0003232653190000053
wherein the content of the first and second substances,
Figure BDA0003232653190000054
matrix element, t, representing the ith row and jth column of the fuzzy complementary decision matrixijMatrix elements representing the ith row and jth column in the adjustment decision matrix, tjiThe matrix element of the jth row and ith column in the adjustment judgment matrix is represented;
the reachable matrix is represented as:
Figure BDA0003232653190000061
Figure BDA0003232653190000062
wherein, K represents a reachable matrix,
Figure BDA0003232653190000063
representing the sum operation of Boolean operators, P representing an indicator matrix, n representing the number of elements or indicators corresponding to the fuzzy decision matrix, PijRepresenting the matrix element indicating the ith row and the jth column of the matrix, aijAnd matrix elements of the ith row and the jth column of the fuzzy complementary judging matrix are represented.
In some embodiments, in the step of calculating the reachable matrix corresponding to the fuzzy complementary determination matrix corresponding to the adjustment determination matrix corresponding to each element in the criterion layer, or in the step of calculating the reachable matrix corresponding to the fuzzy complementary determination matrix corresponding to the adjustment determination matrix corresponding to the block chain platform security strength in the target layer, the calculation formula of the weight coefficient is as follows:
Figure BDA0003232653190000064
wherein, wiThe weight coefficient of the index layer corresponding to the ith row matrix element in the fuzzy judgment matrix representing the element of the rule layer or the weight coefficient of the element of the rule layer corresponding to the ith row matrix element in the fuzzy judgment matrix representing the element of the target layer, n represents the number of the corresponding element or the corresponding index, alpha represents an adjustable parameter,
Figure BDA0003232653190000065
matrix elements representing the ith row and the jth column of the fuzzy complementary judgment matrix;
judging whether the corresponding fuzzy complementary judgment matrix has satisfactory consistency according to the diagonal elements of the reachable matrix corresponding to each element in the criterion layer, comprising the following steps:
if matrix elements which are 1 do not exist on the diagonal line of the reachable matrix corresponding to the elements in the criterion layer, judging that the corresponding fuzzy complementary judgment matrix has satisfactory consistency;
judging whether the corresponding fuzzy complementary judgment matrix has satisfactory consistency according to the diagonal elements of the reachable matrix corresponding to the block chain platform security strength in the target layer, wherein the judgment comprises the following steps:
and if the matrix element of 1 does not exist on the diagonal line of the reachable matrix corresponding to the element in the target layer, judging that the corresponding fuzzy complementary judgment matrix has satisfactory consistency.
In some embodiments, obtaining subjective scores of each index in an index layer of a block chain platform, performing weighted summation on the subjective scores of all indexes in the index layer corresponding to each element in a criterion layer by using a weight coefficient of the corresponding index to obtain a weighted score of the corresponding element in the criterion layer, performing weighted summation on the weighted scores of all elements in the alignment layer by using the weight coefficient of the corresponding element to obtain a final score of the block chain platform security strength of a target layer of the block chain platform, as a security strength evaluation result of the block chain platform, includes:
obtaining subjective scores of all indexes in index layers of a plurality of different block chain platforms, carrying out weighted summation on the subjective scores of all indexes in the index layers corresponding to all elements in the criterion layer by using weight coefficients of the corresponding indexes aiming at each block chain platform to obtain weighted scores of the corresponding elements in the criterion layer, carrying out weighted summation on the weighted scores of all elements in the criterion layer by using the weight coefficients of the corresponding elements aiming at the weighted scores of all elements in the criterion layer to obtain a final score of the block chain platform safety strength of a target layer of the corresponding block chain platform, and taking the final score as a safety strength evaluation result of the corresponding block chain platform.
According to another aspect of the embodiments of the present invention, there is also provided an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method according to any of the above embodiments when executing the computer program.
According to another aspect of the embodiments of the present invention, there is also provided a computer-readable storage medium, on which a computer program is stored, which when executed by a processor implements the steps of the method of any of the above embodiments.
According to the block chain platform safety intensity evaluation method, the electronic device and the computer readable storage medium, an FAHP hierarchical structure model for block chain platform safety intensity evaluation is constructed based on a block chain technology layered architecture, a confidence degree sorting method is used for determining element weight coefficients of each layer, and the safety intensity evaluation of the block chain platform is completed by combining subjective evaluation of developers or supervisors on indexes of each index layer; compared with the existing scheme of singly using the analytic hierarchy process, the method fully considers the consideration ambiguity of the appraisers, and the subjective evaluation index weight coefficient result obtained by calculation through the confidence ranking method has higher consistency with the weight expectation of the appraisers to the index, so that the final evaluation result is more accurate.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts. In the drawings:
FIG. 1 is a flowchart illustrating a method for evaluating security strength of a blockchain platform according to an embodiment of the invention;
FIG. 2 is a block chain reference model structure of a hierarchical architecture in accordance with an embodiment of the present invention;
FIG. 3 is a block chain platform security strength indicator model according to an embodiment of the present invention;
FIG. 4 is a block chain platform security strength evaluation FAHP hierarchy model according to an embodiment of the present invention;
FIG. 5 is a timing diagram illustrating evaluation of security strength of a blockchain platform according to an embodiment of the present invention;
FIG. 6 is a block chain platform security strength evaluation flow diagram according to an embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the embodiments of the present invention are further described in detail below with reference to the accompanying drawings. The exemplary embodiments and descriptions of the present invention are provided to explain the present invention, but not to limit the present invention.
For the problem of scaling numerical value determination of the analytic hierarchy process in block chain safety risk assessment, the consideration ambiguity of an evaluator is considered, so that the final evaluation result is more accurate; the problem of insufficient overall consideration when the safety strength of the block chain platform is evaluated is solved, so that the evaluation effect is more comprehensive and reliable. Therefore, the FAHP is applied to block chain platform safety intensity evaluation, the fuzziness of the thinking of evaluators is fully considered, the thinking consistency of the evaluators is guaranteed, and the final evaluation result is more accurate. Meanwhile, based on the block chain technology layered architecture, an FAHP hierarchical structure model of the block chain platform safety strength is constructed, and the safety strength of the block chain platform is evaluated on the whole, so that the evaluation result is comprehensive and reliable.
Fig. 1 is a flowchart illustrating a method for evaluating a security strength of a blockchain platform according to an embodiment of the present invention, and referring to fig. 1, the method for evaluating a security strength of a blockchain platform according to an embodiment of the present invention may include the following steps S110 to S130.
Specific embodiments of steps S110 to S130 will be described in detail below.
Step S110: establishing a fuzzy hierarchical analysis hierarchical structure model for evaluating the safety intensity of a block chain platform based on a block chain technology hierarchical architecture; the fuzzy hierarchical analysis hierarchical structure model comprises a target layer, a criterion layer and an index layer; the target layer is an upper layer of the criterion layer, and the index layer is a lower layer of the criterion layer; the elements of the target layer comprise blockchain platform security strengths corresponding to the elements of the criteria layer; elements of the criteria layer include system security, user security, contract security, consensus security, data security, and network security; indexes of the index layer comprise system fault tolerance capability, incentive mechanism security, traceability management security, user identity security, private data security, user account security, contract attack resistance capability, contract algorithm normalization, consensus mechanism robustness, consensus mechanism effectiveness, data privacy security, data storage security, data sharing security, propagation mechanism security, verification mechanism security and P2P network structure security; the system security corresponds to the system fault tolerance capability, the excitation mechanism security and the traceability management security; user security corresponds to user identity security, private data security, and user account security; contract security corresponds to contract attack resistance and contract algorithm normalization; consensus security corresponds to consensus mechanism robustness and consensus mechanism effectiveness; data security corresponds to data privacy security, data storage security, and data sharing security; network security corresponds to propagation mechanism security, authentication mechanism security, and P2P network fabric security.
In step S110, the blockchain technology layered architecture may include a network layer, a consensus layer, an excitation layer, a contract layer, and an application layer (software/hardware application), based on which the target layer, the criterion layer, and the index layer in the fuzzy layered analysis are divided, so that the fuzzy layered analysis can be conveniently applied to the blockchain architecture. Moreover, the partitioning is comprehensive and helps to take various factors into full consideration.
Step S120: the method comprises the steps of establishing a fuzzy judgment matrix corresponding to corresponding elements of a criterion layer based on fuzzy scales expressed by fuzzy triangle numbers determined by pairwise comparison of safety intensity importance of different indexes in an index layer corresponding to each element in the criterion layer, establishing a fuzzy judgment matrix corresponding to the corresponding elements of the criterion layer based on fuzzy scales expressed by the fuzzy triangle numbers determined by pairwise comparison of safety intensity importance of different elements in the criterion layer corresponding to block chain platform safety intensity in a target layer, establishing a fuzzy judgment matrix corresponding to the block chain platform safety intensity of the criterion layer, calculating a weight coefficient of each index in the index layer corresponding to the corresponding element in the criterion layer by using a confidence degree sorting method based on the fuzzy judgment matrix corresponding to the elements in the criterion layer, and calculating a weight coefficient of each element in the criterion layer by using a confidence degree sorting method based on the fuzzy judgment matrix corresponding to the elements in the target layer.
In particular, the fuzzy triangle number may be given by a person with reference to a given standard. In this case, in the step S120, based on fuzzy scales represented by fuzzy triangles obtained by comparing the importance of the security strength of different indexes in the index layer corresponding to each element in the criterion layer two by two, a fuzzy judgment matrix corresponding to the corresponding element in the criterion layer is constructed, and based on fuzzy scales represented by fuzzy triangles obtained by comparing the importance of the security strength of different elements in the criterion layer corresponding to the security strength of the block chain platform in the target layer two by two, a fuzzy judgment matrix corresponding to the security strength of the block chain platform in the criterion layer is constructed, which may specifically include the steps of: based on comparing the safety intensity importance of different indexes in the index layer corresponding to each element in the criterion layer two by two and defining a determined fuzzy scale represented by fuzzy triangle number according to a set fuzzy scale, constructing a fuzzy judgment matrix corresponding to the corresponding element of the criterion layer, and based on comparing the safety intensity importance of different elements in the criterion layer corresponding to the block chain platform safety intensity in the target layer two by two and defining a determined fuzzy scale represented by fuzzy triangle number according to the set fuzzy scale, constructing a fuzzy judgment matrix corresponding to the block chain platform safety intensity of the criterion layer; wherein the set fuzzy scale definition includes a reference scale and a corresponding security strength significance relationship specification.
The reference scale may be a plurality of values from small to large, and may further include a comparison rule. For example, in setting the fuzzy scale definition, a fuzzy scale of 0.5 indicates that two elements are compared, both of equal importance; a blur scale of 0.6 indicates that the former is slightly more important than the latter in comparison with the two elements; a blur scale of 0.7 indicates that the former is significantly more important than the latter in comparison with the two elements; a fuzzy scale of 0.8 indicates that the two elements are compared, the stronger being much more important than the latter; a blur scale of 0.9 indicates that the former is extremely important compared to the latter; if the first element aiWith a second element ajCompared withObtaining a first scale rijThen the second element ajWith a second element aiSecond scale r obtained by comparisonji＝1-rij。
In this embodiment, by giving a reference to the blur scale, the deviation of the person given the blur triangle number can be reduced.
In a specific implementation, in the step S120, the calculating, by using a confidence ranking method, a weight coefficient of each index in the index layer corresponding to the corresponding element in the criterion layer based on the fuzzy determination matrix corresponding to the element in the criterion layer may specifically include the steps of:
s1211: calculating a probability matrix of a fuzzy judgment matrix corresponding to each element in the criterion layer;
s1212: calculating a fuzzy judgment matrix of a fuzzy judgment matrix corresponding to each element in the criterion layer;
s1213: multiplying the probability matrix of the fuzzy judgment matrix corresponding to each element in the criterion layer by the corresponding position matrix element of the corresponding fuzzy judgment matrix to obtain a corresponding adjustment judgment matrix;
s1214: calculating a reachable matrix corresponding to a fuzzy complementary judgment matrix corresponding to the adjustment judgment matrix corresponding to each element in the criterion layer;
s1215: and judging whether the corresponding fuzzy complementary judgment matrix has satisfactory consistency or not according to the diagonal elements of the reachable matrix corresponding to each element in the criterion layer, and calculating the weight coefficient of the corresponding element in the criterion layer based on the corresponding fuzzy complementary judgment matrix under the condition that the corresponding fuzzy complementary judgment matrix has satisfactory consistency.
More specifically, in the step S1215, if the consistency is not satisfied, consistency adjustment may be performed. For example, in the step S120, the calculating the weight coefficient of each index in the index layer corresponding to the corresponding element in the criterion layer by using the confidence ranking method based on the fuzzy judgment matrix corresponding to the element in the criterion layer may further include the steps of: s1216, in a case that the fuzzy complementary judging matrices corresponding to the elements in the criterion layer do not have satisfactory consistency, adjusting the corresponding fuzzy complementary judging matrix to obtain a fuzzy complementary judging matrix with satisfactory consistency.
Further, in the step S1214, that is, calculating the reachable matrix corresponding to the fuzzy complementary determination matrix corresponding to the adjustment determination matrix corresponding to each element in the criterion layer, the method may specifically include the steps of: s12141, under the condition that the adjustment judgment matrix corresponding to the element in the criterion layer is the fuzzy complementary judgment matrix, correspondingly adjusting the fuzzy complementary judgment matrix corresponding to the judgment matrix to be the fuzzy complementary judgment matrix; under the condition that the adjustment judgment matrix corresponding to the element in the criterion layer is not the fuzzy complementary judgment matrix, transforming the corresponding adjustment judgment matrix into the fuzzy complementary judgment matrix; and calculating a reachable matrix corresponding to the corresponding fuzzy complementary judgment matrix.
In a specific implementation, in the step S120, calculating the weight coefficient of each element in the criterion layer by using a confidence ranking method based on the fuzzy judgment matrix corresponding to the element in the target layer may specifically include the steps of:
s1221: calculating a probability matrix of a fuzzy judgment matrix corresponding to the safety intensity of a block chain platform in a target layer;
s1222: calculating a fuzzy judgment matrix of a fuzzy judgment matrix corresponding to the safety intensity of the block chain platform in the target layer;
s1223: multiplying the probability matrix of the fuzzy judgment matrix corresponding to the block chain platform safety intensity in the target layer by the corresponding position matrix element of the corresponding fuzzy judgment matrix to obtain a corresponding adjustment judgment matrix;
s1224: calculating a reachable matrix corresponding to a fuzzy complementary judgment matrix corresponding to an adjustment judgment matrix corresponding to the block chain platform safety strength in the target layer;
s1225: and judging whether the corresponding fuzzy complementary judgment matrix has satisfactory consistency according to the diagonal elements of the reachable matrix corresponding to the block chain platform safety strength in the target layer, and calculating the weight coefficient of the block chain platform safety strength in the target layer based on the corresponding fuzzy complementary judgment matrix under the condition that the corresponding fuzzy complementary judgment matrix has satisfactory consistency.
More specifically, in step S1225, if the consistency is not satisfactory, consistency adjustment may be performed. For example, in the step S120, the calculating the weight coefficient of each index in the index layer corresponding to the corresponding element in the criterion layer by using the confidence ranking method based on the fuzzy judgment matrix corresponding to the element in the criterion layer may further include the steps of: and S1226, under the condition that the fuzzy complementary judgment matrix corresponding to the block chain platform safety intensity in the target layer does not have satisfactory consistency, adjusting the corresponding fuzzy complementary judgment matrix to obtain the fuzzy complementary judgment matrix with satisfactory consistency.
Further, in the step S1224, that is, calculating the reachable matrix corresponding to the fuzzy complementary determination matrix corresponding to the adjustment determination matrix corresponding to the safety strength of the block chain platform in the target layer, the method may specifically include the steps of: s12241, in the case that the adjustment judgment matrix corresponding to the block chain platform security strength in the target layer is the fuzzy complementary judgment matrix, correspondingly adjusting the fuzzy complementary judgment matrix corresponding to the judgment matrix to be itself; under the condition that the adjustment judgment matrix corresponding to the block chain platform safety intensity in the target layer is not a fuzzy complementary judgment matrix, converting the corresponding adjustment judgment matrix into a fuzzy complementary judgment matrix; and calculating a reachable matrix corresponding to the corresponding fuzzy complementary judgment matrix.
Further, in the embodiment of calculating the weight coefficient for the element in the criterion layer and the index in the index layer, the specific process can be implemented in a similar manner.
For example, the probability matrix may be expressed as:
Figure BDA0003232653190000121
wherein B represents a probability matrix, BijMatrix elements of ith row and jth column of the probability matrix, n represents the number of elements or indexes corresponding to the fuzzy judgment matrix, lij、mijAnd uijAnd respectively representing an upper limit value, a middle value and a lower limit value in a fuzzy scale represented by fuzzy triangle numbers corresponding to the matrix elements of the ith row and the jth column in the fuzzy judgment matrix.
For example, the fuzzy evaluation matrix may be expressed as:
Figure BDA0003232653190000122
wherein S represents a fuzzy judgment matrix, n represents the number of elements or indexes corresponding to the fuzzy judgment matrix, and lij、mijAnd uijRespectively representing an upper bound value, a median value and a lower bound value in a fuzzy scale represented by fuzzy triangular numbers corresponding to matrix elements of the ith row and the jth column in the fuzzy judgment matrix; e.g. of the typeij＝uij-lij，eijIndicating a blur determination section.
For example, the transformation formula used to transform the adjustment judgment matrix into the fuzzy complementary judgment matrix may be:
Figure BDA0003232653190000123
wherein the content of the first and second substances,
Figure BDA0003232653190000124
matrix element, t, representing the ith row and jth column of the fuzzy complementary decision matrixijMatrix elements representing the ith row and jth column in the adjustment decision matrix, tjiAnd matrix elements of the jth row and ith column in the adjustment judgment matrix are shown.
For example, the reach matrix may be represented as:
Figure BDA0003232653190000125
Figure BDA0003232653190000126
wherein, K represents a reachable matrix,
Figure BDA0003232653190000127
representing the sum of Boolean operators, P representing an indicator matrix, n representing the number of elements or indices corresponding to the fuzzy decision matrixAmount, pijRepresenting the matrix element indicating the ith row and the jth column of the matrix, aijAnd matrix elements of the ith row and the jth column of the fuzzy complementary judging matrix are represented.
Further, in step S1214 of calculating the reachable matrix corresponding to the fuzzy complementary determining matrix corresponding to the adjusting determining matrix corresponding to each element in the criterion layer, or in step S1224 of calculating the reachable matrix corresponding to the fuzzy complementary determining matrix corresponding to the adjusting determining matrix corresponding to the block chain platform security strength in the target layer, the calculation formula of the weight coefficient may be:
Figure BDA0003232653190000131
wherein, wiThe weight coefficient of the index layer corresponding to the ith row matrix element in the fuzzy judgment matrix representing the element of the rule layer or the weight coefficient of the element of the rule layer corresponding to the ith row matrix element in the fuzzy judgment matrix representing the element of the target layer, n represents the number of the corresponding element or the corresponding index, alpha represents an adjustable parameter,
Figure BDA0003232653190000132
and matrix elements of the ith row and the jth column of the fuzzy complementary judging matrix are represented.
Further, in the step S1215, determining whether the corresponding fuzzy complementary determining matrix has satisfactory consistency according to the diagonal element of the reachable matrix corresponding to each element in the criterion layer, specifically, the method includes the steps of: and if matrix elements which are 1 do not exist on the diagonal line of the reachable matrix corresponding to the elements in the criterion layer, judging that the corresponding fuzzy complementary judgment matrix has satisfactory consistency.
Further, the step S1225, namely, determining whether the corresponding fuzzy complementary determining matrix has satisfactory consistency according to the diagonal elements of the reachable matrix corresponding to the security strength of the block chain platform in the target layer, may specifically include the steps of: and if the matrix element of 1 does not exist on the diagonal line of the reachable matrix corresponding to the element in the target layer, judging that the corresponding fuzzy complementary judgment matrix has satisfactory consistency.
Step S130: and acquiring subjective scores of all indexes in an index layer of the block chain platform, performing weighted summation on the subjective scores of all indexes in the index layer corresponding to each element in the criterion layer by using the weight coefficient of the corresponding index to obtain weighted scores of the corresponding elements in the criterion layer, performing weighted summation on the weighted scores of all elements in the layer by using the weight coefficient of the corresponding element to obtain a final score of the safety strength of the block chain platform of a target layer of the block chain platform, and taking the final score as the safety strength evaluation result of the block chain platform.
In other embodiments, when there are multiple blockchain platforms to be evaluated, the evaluation may be performed in a similar manner in step S130, and the weighting coefficients may be the same for different blockchain platforms, mainly different from the subjective scores corresponding to different blockchain platforms for a specific blockchain platform. In this case, the step S130, that is, obtaining the subjective score of each index in the index layer of the blockchain platform, performing weighted summation on the subjective scores of all indexes in the index layer corresponding to each element in the criterion layer by using the weight coefficient of the corresponding index to obtain the weighted score of the corresponding element in the criterion layer, performing weighted summation on the weighted scores of all elements in the alignment layer by using the weight coefficient of the corresponding element to obtain the final score of the safety strength of the blockchain platform of the target layer of the blockchain platform, which is used as the safety strength evaluation result of the blockchain platform, may specifically include the steps of: s131, obtaining subjective scores of all indexes in the index layers of a plurality of different block chain platforms, carrying out weighted summation on the subjective scores of all indexes in the index layer corresponding to each element in the criterion layer by using the weight coefficient of the corresponding index aiming at each block chain platform to obtain the weighted score of the corresponding element in the criterion layer, carrying out weighted summation on the weighted scores of all elements in the criterion layer by using the weight coefficient of the corresponding element to obtain the final score of the block chain platform safety strength of the target layer of the corresponding block chain platform, and taking the final score as the safety strength evaluation result of the corresponding block chain platform.
In addition, an embodiment of the present invention further provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor executes the computer program to implement the steps of the method according to any of the above embodiments.
Embodiments of the present invention further provide a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps of the method according to any of the above embodiments.
According to the block chain platform safety intensity evaluation method based on the fuzzy analytic hierarchy process, firstly, a block chain platform safety intensity evaluation FAHP hierarchical structure model is established based on a block chain technology hierarchical framework, then, an evaluator subjectively scores indexes of each index layer in a block chain platform to be evaluated in safety and constructs a fuzzy judgment matrix, a confidence ranking method is used for determining weight coefficients of elements of each layer, and finally, the subjective evaluation of the block chain platform safety intensity is completed by combining the score values of the elements of each layer. The safety intensity evaluation can be realized on the block chain platform, developers or supervisors can be helped to put forward improvement opinions on the safety of the block chain platform, and scientific basis and reference function are provided for building a safe and credible block chain platform.
The above method is described below with reference to a specific example, however, it should be noted that the specific example is only for better describing the present application and is not to be construed as limiting the present application.
In a specific embodiment, a fuzzy analytic hierarchy process is applied to the evaluation of the safety intensity of the blockchain platform, firstly, a hierarchical structure model of the blockchain platform safety intensity evaluation FAHP is established based on a layered architecture of a blockchain technology (as shown in fig. 2), then, an evaluator subjectively scores indexes of each index layer in the blockchain platform to be evaluated in safety and constructs a fuzzy judgment matrix, a confidence ranking method is used for determining weight coefficients of elements of each layer, and finally, the subjective evaluation of the safety intensity of the blockchain platform is completed by combining the evaluation values of the elements of each layer. Specifically, referring to fig. 5 and 6, the method for evaluating the safety intensity of the block chain platform based on the fuzzy hierarchy analysis of this embodiment may include the following steps S1 to S6.
S1, establishing a block chain platform safety intensity evaluation and evaluation FAHP hierarchical structure model based on a block chain technology hierarchical architecture, wherein the block chain platform safety intensity evaluation and evaluation FAHP hierarchical structure model comprises a target layer, a criterion layer and an index layer.
In step S1, the block chain platform security strength evaluation FAHP hierarchical model includes a target layer, a criterion layer, and an index layer. Wherein, the top layer is a target layer which is a final target achieved after comprehensively analyzing each index; the middle level is called a factor layer and also called a criterion layer; the lowest level is called an index layer, which is a number quantification criterion and refers to a specific numerical value of each influencing factor to be considered in order to achieve the final evaluation goal.
Specifically, referring to fig. 3 and 4, the target layer is the blockchain platform security strength a1(ii) a The criteria layer includes system security B1User security B2Contract security B3Consensus security B4Data security B5Network security B66 elements are equal; the index layer comprises a system fault tolerance capability C1Safety of the excitation mechanism C2And source tracing management safety C3User identity security C4Private data security C5User account security C6Contract attack resistance C7Normalization of contract algorithm C8Consensus mechanism robustness C9Consensus mechanism effectiveness C10Data privacy security C11Data storage security C12Data sharing security C13Secure propagation mechanism C14Authentication mechanism security C15P2P network architecture security C16Etc. 16 elements.
And S2, receiving subjective scores given by indexes of each index layer in the block chain platform of the safety intensity to be evaluated by the evaluating personnel.
In step S2, referring to fig. 5, the developer or the supervisor can subjectively score the index layer indexes in the blockchain platform of the security strength to be evaluated.
The safe intensity score value interval may be [0,10 ]]The higher the score value, the better the safety strength. The score set of each element of the criterion layer isZS＝{zs1,zs2,zs3,zs4,zs5,zs6Where zs isiI is more than or equal to 1 and less than or equal to 6; each element of the index layer is divided into BS ═ BS1,bs2,…,bs16In which bsjJ is more than or equal to 1 and less than or equal to 16 and is the score of the jth element of the index layer.
And S3, comparing every two factors of the same level with the importance of the safety intensity in the previous level to construct a fuzzy judgment matrix.
In step S3, the basis of the fuzzy judgment matrix is a scaling method of the elements in the matrix, and the embodiment may select a FAHP scaling method based on a triangular fuzzy number, where the elements in the fuzzy judgment matrix are represented by the triangular fuzzy number. The triangular blur number is represented by (l, m, u), where l and u are its upper and lower bounds, and m is its median. The upper and lower bounds of the fuzzy number represent the relative importance degree relationship range of the two indexes, and the selection of the median is based on the fuzzy scale shown in Table 1.
TABLE 1 fuzzy Scale and its meanings
Figure BDA0003232653190000151
Figure BDA0003232653190000161
In this embodiment, a total of 7 fuzzy judgment matrices are constructed, which are respectively a block chain platform security strength judgment matrix, a system security index comparison judgment matrix, a user security index comparison judgment matrix, a contract security index comparison judgment matrix, a consensus security index comparison judgment matrix, a data security index comparison judgment matrix, and a network security index comparison judgment matrix, as shown in tables 2 to 8 below. In tables 2 to 8, (l)ij,mij,uij) Elements in a triangular fuzzy number representing the ith row and the jth column in the corresponding matrix.
TABLE 2 Block chain platform Security intensity decision matrix
Figure BDA0003232653190000162
TABLE 3 comparison and judgment matrix for each index of system security
Figure BDA0003232653190000163
TABLE 4 comparison and judgment matrix for each index of user security
Figure BDA0003232653190000171
TABLE 5 contract security index comparison and judgment matrix
Figure BDA0003232653190000172
TABLE 6 comparison and judgment matrix for each index of consensus security
Figure BDA0003232653190000173
TABLE 7 comparison and judgment matrix for each index of data security
Figure BDA0003232653190000174
TABLE 8 comparison and judgment matrix for each index of network security
Figure BDA0003232653190000175
And S4, calculating the weight coefficient of each layer of elements by using a confidence degree sorting method, checking the consistency of the weight coefficient and the consistency, and performing consistency adjustment on the matrix which does not meet the requirement of consistency so as to finally obtain the weight of each layer of elements.
In step S4, the weight of each element in the criterion layer is represented by ZW ═ { ZW ═ ZW1,zw2,zw3,zw4,zw5,zw6And the weight of each element of the index layer is expressed as BW ═ BW1,bw2,…,bw16}。
For each index, there is a fuzzy judgment interval eij＝uij-lijThis interval reflects the reliability of the evaluation result of the evaluator, and may be understood as a "confidence interval" in mathematical statistics in a sense. If eijThe larger the evaluation result is, the smaller the reliability of the evaluation result of the evaluator is; on the contrary, if eijThe smaller the evaluation result, the greater the reliability of the evaluation result of the evaluator. The confidence ranking method specifically comprises the following steps:
a) according to the formula (1), the probability matrix B of each fuzzy judgment matrix is calculated as (B)ij)n×n；
Figure BDA0003232653190000181
b) Calculating a fuzzy judgment matrix S of each fuzzy judgment matrix according to the formula (2):
Figure BDA0003232653190000182
eij＝uij-lij (2)
c) calculating an adjustment judgment matrix T ═ B · S (element T)ij＝bijsij)；
d) The obtained adjustment judgment matrix T may not be a fuzzy complementary judgment matrix, and the judgment matrix is transformed into the fuzzy complementary judgment matrix by the formula (3)
Figure BDA0003232653190000183
Figure BDA0003232653190000184
e) Checking fuzzy complementary judging matrix
Figure BDA0003232653190000185
The method is as follows: setting fuzzy complementary judgment matrix as
Figure BDA0003232653190000186
The matrix K is called a matrix corresponding to
Figure BDA0003232653190000187
Can be reached.
Figure BDA0003232653190000188
(4) In the formula (I), the compound is shown in the specification,
Figure BDA0003232653190000191
is the sum of Boolean operators, and the operation rule is shown in formula (5):
Figure BDA0003232653190000192
p is called an indication matrix, the elements of which are defined as shown in formula (6):
Figure BDA0003232653190000193
if the matrix is fuzzy
Figure BDA0003232653190000194
Does not have an element of 1 on the diagonal of the reachable matrix K, the matrix
Figure BDA0003232653190000195
With satisfactory consistency, otherwise called
Figure BDA0003232653190000196
Is inconsistent, one needs to be performedAnd (5) adjusting the sexual performance.
f) If matrix
Figure BDA0003232653190000197
And when the satisfaction is consistent, the weight of each index is obtained according to the formula (7):
Figure BDA0003232653190000198
(7) wherein alpha is a tunable parameter, alpha is not less than (n-1)/2, i is 1,2, …, n.
g) Finally, the element weight of each criterion layer is represented as ZW ═ { ZW1,zw2,zw3,zw4,zw5,zw6And the weight of each element of the index layer is expressed as BW ═ BW1,bw2,…,bw16}。
And S5, combining subjective scoring of the assessment personnel on each index layer index with the obtained element weight of each layer in the step S4, and calculating to obtain a final score S of the block chain platform safety strength.
Assuming that the evaluation score of the safety intensity of the block chain platform is s, the evaluation score is
Figure BDA0003232653190000199
Wherein zwiFor each criterion layer element weight, zsiScoring each criterion layer element, n being the number of index layer elements contained in each criterion layer element, bwijFor each index layer element weight, bs, under the criterion layer element iijAnd scoring each index layer element under the criterion layer element i.
S6, it is assumed that there are m block chain platforms to be evaluated for security strength in this embodiment, that is, a block chain platform set block chain ═ block chain1,blockchain2,...,blockchainmWherein, blockchainiRepresenting the ith blockchain platform, the set of security strength scores of the final m blockchain platforms is S ═ S1,s2,…,smIn which s isiThe safety intensity of the ith block chain platform is represented, and the safety intensity of the m block chain platforms can be enhanced according to the size of the score valueAnd (5) evaluating the degree.
The block chain platform safety intensity evaluation method based on the fuzzy analytic hierarchy process, which is provided aiming at the problems that the scale value of the analytic hierarchy process in the block chain platform safety risk evaluation is fixed and the evaluators are difficult to ensure the thinking consistency when evaluating the overall performance of the block chain platform, has the following beneficial effects: building a block chain platform safety intensity evaluation FAHP hierarchical structure model based on a block chain technology layered architecture, determining element weight coefficients of each layer by using a confidence degree sorting method, and finishing safety intensity evaluation on the block chain platform by combining subjective evaluation of developers or supervisors on indexes of each index layer; compared with the existing scheme of singly using the analytic hierarchy process, the method has the advantages that the consideration fuzziness of the appraisers is fully considered, the subjective evaluation index weight coefficient result obtained by calculation through the confidence degree sequencing method is higher in consistency with the weight expectation of the appraisers to the index, and therefore the final evaluation result is more accurate; through the evaluation result of the safety intensity of the block chain platform, a developer or a supervisor can put forward an improvement suggestion on the safety intensity of the block chain platform, and a scientific basis and a reference function are provided for constructing a safe and credible block chain platform.
In the description herein, reference to the description of the terms "one embodiment," "a particular embodiment," "some embodiments," "for example," "an example," "a particular example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. The sequence of steps involved in the various embodiments is provided to schematically illustrate the practice of the invention, and the sequence of steps is not limited and can be suitably adjusted as desired.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are only exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.

Claims (10)

1. A method for evaluating security strength of a block chain platform is characterized by comprising the following steps:
establishing a fuzzy hierarchical analysis hierarchical structure model for evaluating the safety intensity of a block chain platform based on a block chain technology hierarchical architecture; the fuzzy hierarchical analysis hierarchical structure model comprises a target layer, a criterion layer and an index layer; the target layer is an upper layer of the criterion layer, and the index layer is a lower layer of the criterion layer; the elements of the target layer comprise blockchain platform security strengths corresponding to the elements of the criteria layer; elements of the criteria layer include system security, user security, contract security, consensus security, data security, and network security; indexes of the index layer comprise system fault tolerance capability, incentive mechanism security, traceability management security, user identity security, private data security, user account security, contract attack resistance capability, contract algorithm normalization, consensus mechanism robustness, consensus mechanism effectiveness, data privacy security, data storage security, data sharing security, propagation mechanism security, verification mechanism security and P2P network structure security; the system security corresponds to the system fault tolerance capability, the excitation mechanism security and the traceability management security; user security corresponds to user identity security, private data security, and user account security; contract security corresponds to contract attack resistance and contract algorithm normalization; consensus security corresponds to consensus mechanism robustness and consensus mechanism effectiveness; data security corresponds to data privacy security, data storage security, and data sharing security; network security corresponds to propagation mechanism security, verification mechanism security, and P2P network fabric security;
based on fuzzy scales represented by fuzzy triangular numbers, which are determined by pairwise comparison of safety intensity importance of different indexes in an index layer corresponding to each element in a criterion layer, constructing a fuzzy judgment matrix corresponding to the corresponding element of the criterion layer, based on fuzzy scales represented by fuzzy triangular numbers, which are determined by pairwise comparison of safety intensity importance of different elements in the criterion layer corresponding to safety intensity of a block chain platform in a target layer, constructing a fuzzy judgment matrix corresponding to safety intensity of the block chain platform of the criterion layer, calculating a weight coefficient of each index in the index layer corresponding to the corresponding element in the criterion layer by using a confidence degree sorting method based on the fuzzy judgment matrix corresponding to the element in the criterion layer, and calculating a weight coefficient of each element in the criterion layer by using a confidence degree sorting method based on the fuzzy judgment matrix corresponding to the element in the target layer;
and acquiring subjective scores of all indexes in an index layer of the block chain platform, performing weighted summation on the subjective scores of all indexes in the index layer corresponding to each element in the criterion layer by using the weight coefficient of the corresponding index to obtain weighted scores of the corresponding elements in the criterion layer, performing weighted summation on the weighted scores of all elements in the layer by using the weight coefficient of the corresponding element to obtain a final score of the safety strength of the block chain platform of a target layer of the block chain platform, and taking the final score as the safety strength evaluation result of the block chain platform.
2. The method according to claim 1, wherein the constructing of the fuzzy judgment matrix corresponding to the corresponding elements of the criterion layer is based on fuzzy scales expressed by fuzzy triangles obtained by pairwise comparison of safety strength importance of different indexes in the index layer corresponding to each element in the criterion layer, and the constructing of the fuzzy judgment matrix corresponding to the safety strength of the block chain platform of the criterion layer is based on fuzzy scales expressed by fuzzy triangles obtained by pairwise comparison of safety strength importance of different elements in the criterion layer corresponding to the safety strength of the block chain platform in the target layer comprises:
based on comparing the safety intensity importance of different indexes in the index layer corresponding to each element in the criterion layer two by two and defining a determined fuzzy scale represented by fuzzy triangle number according to a set fuzzy scale, constructing a fuzzy judgment matrix corresponding to the corresponding element of the criterion layer, and based on comparing the safety intensity importance of different elements in the criterion layer corresponding to the block chain platform safety intensity in the target layer two by two and defining a determined fuzzy scale represented by fuzzy triangle number according to the set fuzzy scale, constructing a fuzzy judgment matrix corresponding to the block chain platform safety intensity of the criterion layer; wherein the set fuzzy scale definition includes a reference scale and a corresponding security strength significance relationship specification.
3. The method of claim 1, wherein the evaluation method of the block chain platform security strength,
calculating the weight coefficient of each index in the index layer corresponding to the corresponding element in the criterion layer by using a confidence ranking method based on the fuzzy judgment matrix corresponding to the element in the criterion layer, wherein the method comprises the following steps:
calculating a probability matrix of a fuzzy judgment matrix corresponding to each element in the criterion layer;
calculating a fuzzy judgment matrix of a fuzzy judgment matrix corresponding to each element in the criterion layer;
multiplying the probability matrix of the fuzzy judgment matrix corresponding to each element in the criterion layer by the corresponding position matrix element of the corresponding fuzzy judgment matrix to obtain a corresponding adjustment judgment matrix;
calculating a reachable matrix corresponding to a fuzzy complementary judgment matrix corresponding to the adjustment judgment matrix corresponding to each element in the criterion layer;
judging whether the corresponding fuzzy complementary judgment matrix has satisfactory consistency according to diagonal elements of the reachable matrix corresponding to each element in the criterion layer, and calculating the weight coefficient of the corresponding element in the criterion layer based on the corresponding fuzzy complementary judgment matrix under the condition that the corresponding fuzzy complementary judgment matrix has satisfactory consistency;
calculating the weight coefficient of each element in the criterion layer by using a confidence ranking method based on the fuzzy judgment matrix corresponding to the element in the target layer, wherein the method comprises the following steps:
calculating a probability matrix of a fuzzy judgment matrix corresponding to the safety intensity of a block chain platform in a target layer;
calculating a fuzzy judgment matrix of a fuzzy judgment matrix corresponding to the safety intensity of the block chain platform in the target layer;
multiplying the probability matrix of the fuzzy judgment matrix corresponding to the block chain platform safety intensity in the target layer by the corresponding position matrix element of the corresponding fuzzy judgment matrix to obtain a corresponding adjustment judgment matrix;
calculating a reachable matrix corresponding to a fuzzy complementary judgment matrix corresponding to an adjustment judgment matrix corresponding to the block chain platform safety strength in the target layer;
and judging whether the corresponding fuzzy complementary judgment matrix has satisfactory consistency according to the diagonal elements of the reachable matrix corresponding to the block chain platform safety strength in the target layer, and calculating the weight coefficient of the block chain platform safety strength in the target layer based on the corresponding fuzzy complementary judgment matrix under the condition that the corresponding fuzzy complementary judgment matrix has satisfactory consistency.
4. The method of claim 3, wherein the evaluation method for security strength of blockchain platform,
calculating the weight coefficient of each index in the index layer corresponding to the corresponding element in the criterion layer by using a confidence ranking method based on the fuzzy judgment matrix corresponding to the element in the criterion layer, and further comprising the following steps:
under the condition that the fuzzy complementary judgment matrixes corresponding to the elements in the criterion layer do not have satisfactory consistency, adjusting the corresponding fuzzy judgment matrixes to obtain fuzzy complementary judgment matrixes with satisfactory consistency;
calculating the weight coefficient of each index in the index layer corresponding to the corresponding element in the criterion layer by using a confidence ranking method based on the fuzzy judgment matrix corresponding to the element in the criterion layer, and further comprising the following steps:
and under the condition that the fuzzy complementary judgment matrix corresponding to the block chain platform safety intensity in the target layer does not have satisfactory consistency, adjusting the corresponding fuzzy judgment matrix to obtain the fuzzy complementary judgment matrix with the satisfactory consistency.
5. The method of claim 3, wherein the evaluation method for security strength of blockchain platform,
calculating a reachable matrix corresponding to the fuzzy complementary judgment matrix corresponding to the adjustment judgment matrix corresponding to each element in the criterion layer, wherein the reachable matrix comprises the following steps:
under the condition that the adjustment judgment matrix corresponding to the element in the criterion layer is a fuzzy complementary judgment matrix, the fuzzy complementary judgment matrix corresponding to the corresponding adjustment judgment matrix is the fuzzy complementary judgment matrix; under the condition that the adjustment judgment matrix corresponding to the element in the criterion layer is not the fuzzy complementary judgment matrix, transforming the corresponding adjustment judgment matrix into the fuzzy complementary judgment matrix; calculating reachable matrixes corresponding to the corresponding fuzzy complementary judgment matrixes;
calculating a reachable matrix corresponding to a fuzzy complementary judgment matrix corresponding to an adjustment judgment matrix corresponding to the block chain platform security strength in the target layer, wherein the reachable matrix comprises the following steps:
under the condition that the adjustment judgment matrix corresponding to the block chain platform safety intensity in the target layer is a fuzzy complementary judgment matrix, correspondingly adjusting the fuzzy complementary judgment matrix corresponding to the judgment matrix to be the fuzzy complementary judgment matrix; under the condition that the adjustment judgment matrix corresponding to the block chain platform safety intensity in the target layer is not a fuzzy complementary judgment matrix, converting the corresponding adjustment judgment matrix into a fuzzy complementary judgment matrix; and calculating a reachable matrix corresponding to the corresponding fuzzy complementary judgment matrix.
6. The method of claim 5, wherein the evaluation method of the block chain platform security strength,
the probability matrix is represented as:
Figure FDA0003232653180000041
wherein B represents a probability matrix, BijMatrix elements of ith row and jth column of the probability matrix, n represents the number of elements or indexes corresponding to the fuzzy judgment matrix, lij、mijAnd uijFuzzy marks expressed by fuzzy triangular numbers and corresponding to matrix elements of ith row and jth column in fuzzy judgment matrixUpper, middle and lower bounds of the degree;
the fuzzy evaluation matrix is represented as:
Figure FDA0003232653180000042
wherein S represents a fuzzy judgment matrix, n represents the number of elements or indexes corresponding to the fuzzy judgment matrix, and lij、mijAnd uijRespectively representing an upper bound value, a median value and a lower bound value in a fuzzy scale represented by fuzzy triangular numbers corresponding to matrix elements of the ith row and the jth column in the fuzzy judgment matrix; e.g. of the typeij＝uij-lij，eijRepresenting a fuzzy judgment interval;
the transformation formula for transforming the adjustment judgment matrix into the fuzzy complementary judgment matrix is as follows:
Figure FDA0003232653180000043
wherein the content of the first and second substances,
Figure FDA0003232653180000044
matrix element, t, representing the ith row and jth column of the fuzzy complementary decision matrixijMatrix elements representing the ith row and jth column in the adjustment decision matrix, tjiThe matrix element of the jth row and ith column in the adjustment judgment matrix is represented;
the reachable matrix is represented as:
Figure FDA0003232653180000045
Figure FDA0003232653180000046
wherein, K represents a reachable matrix,
Figure FDA0003232653180000047
representing the sum operation of Boolean operators, P representing an indicator matrix, n representing the number of elements or indicators corresponding to the fuzzy decision matrix, PijRepresenting the matrix element indicating the ith row and the jth column of the matrix, aijAnd matrix elements of the ith row and the jth column of the fuzzy complementary judging matrix are represented.
7. The method of claim 3, wherein the evaluation method for security strength of blockchain platform,
in the step of calculating the reachable matrix corresponding to the fuzzy complementary judgment matrix corresponding to the adjustment judgment matrix corresponding to each element in the criterion layer, or in the step of calculating the reachable matrix corresponding to the fuzzy complementary judgment matrix corresponding to the adjustment judgment matrix corresponding to the block chain platform security strength in the target layer, the calculation formula of the weight coefficient is as follows:
Figure FDA0003232653180000051
wherein, wiThe weight coefficient of the index layer corresponding to the ith row matrix element in the fuzzy judgment matrix representing the element of the rule layer or the weight coefficient of the element of the rule layer corresponding to the ith row matrix element in the fuzzy judgment matrix representing the element of the target layer, n represents the number of the corresponding element or the corresponding index, alpha represents an adjustable parameter,
Figure FDA0003232653180000052
matrix elements representing the ith row and the jth column of the fuzzy complementary judgment matrix;
judging whether the corresponding fuzzy complementary judgment matrix has satisfactory consistency according to the diagonal elements of the reachable matrix corresponding to each element in the criterion layer, comprising the following steps:
if matrix elements which are 1 do not exist on the diagonal line of the reachable matrix corresponding to the elements in the criterion layer, judging that the corresponding fuzzy complementary judgment matrix has satisfactory consistency;
judging whether the corresponding fuzzy complementary judgment matrix has satisfactory consistency according to the diagonal elements of the reachable matrix corresponding to the block chain platform security strength in the target layer, wherein the judgment comprises the following steps:
and if the matrix element of 1 does not exist on the diagonal line of the reachable matrix corresponding to the element in the target layer, judging that the corresponding fuzzy complementary judgment matrix has satisfactory consistency.
8. The method for evaluating the security strength of a blockchain platform according to claim 1, wherein the step of obtaining the subjective scores of each index in the index layer of the blockchain platform, and performing weighted summation on the subjective scores of all indexes in the index layer corresponding to each element in the criterion layer by using the weight coefficients of the corresponding indexes to obtain the weighted scores of the corresponding elements in the criterion layer, and performing weighted summation on the weighted scores of all elements in the alignment layer by using the weight coefficients of the corresponding elements to obtain the final score of the security strength of the blockchain platform of the target layer of the blockchain platform, which is used as the result of evaluating the security strength of the blockchain platform, comprises the steps of:
obtaining subjective scores of all indexes in index layers of a plurality of different block chain platforms, carrying out weighted summation on the subjective scores of all indexes in the index layers corresponding to all elements in the criterion layer by using weight coefficients of the corresponding indexes aiming at each block chain platform to obtain weighted scores of the corresponding elements in the criterion layer, carrying out weighted summation on the weighted scores of all elements in the criterion layer by using the weight coefficients of the corresponding elements aiming at the weighted scores of all elements in the criterion layer to obtain a final score of the block chain platform safety strength of a target layer of the corresponding block chain platform, and taking the final score as a safety strength evaluation result of the corresponding block chain platform.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the steps of the method according to any of claims 1 to 7 are implemented when the processor executes the program.
10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 8.
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