Method and system for evaluating stability of bank network system

Abstract

The invention relates to a bank network system stability evaluation method and system, and the method comprises the steps: analyzing a top event of a bank network system based on a fault tree analysis method, and determining a plurality of basic factors affecting the stability of the bank network system to construct a fault tree; wherein the top event is an event related to service interruption of the bank network system, and the plurality of basic factors include risk factors affecting all aspects of stability of the bank network system; analyzing the fault tree based on a similarity aggregation method to determine the failure probability of the plurality of basic factors; analyzing a plurality of basic factors of the fault tree and mutual influence and dependency among the plurality of basic factors based on a network analysis method (ANP); and based on an ANP analysis result and the failure probabilities of the plurality of basic factors, determining a key factor sequence of the top event.

Description

Technical Field

[0001] The disclosed embodiments relate to the field of computer technology, and in particular, to a method and system for evaluating the stability of a banking network system. Background Art

[0002] Currently in the financial sector, the stability of the bank network system is crucial to ensure the continuity and security of financial services. However, the existing bank network assessment methods mostly employ assessment agencies to refer to the network security level protection assessment requirements to judge the stability of the network system. Since the assessment standards focus more on technical risks and architectural risks, and ignore operational risks, personnel risks, and management risks, they cannot fully cover the potential risk points that affect the stability of the bank network system, and therefore cannot fully and accurately reflect the true stability of the system. In the face of an increasingly complex financial environment and ever-changing systemic risks, it is becoming increasingly important to provide banks with practical risk management and decision-making support. Therefore, how to comprehensively and accurately assess the stability of the bank network system has become an important issue that needs to be solved urgently. Summary of the invention

[0003] In order to solve the above technical problems or at least partially solve the above technical problems, the embodiments of the present disclosure provide a banking network system stability assessment method and system.

[0004] In a first aspect, an embodiment of the present disclosure provides a method for evaluating the stability of a banking network system, comprising:

[0005] Analyze the top events of the bank network system based on the fault tree analysis method, determine multiple basic factors that affect the stability of the bank network system to construct a fault tree; wherein the top event is an event related to the service interruption of the bank network system, and the multiple basic factors include risk factors of all aspects that affect the stability of the bank network system;

[0006] Analyze the fault tree based on similarity aggregation method to determine the failure probability of the multiple basic factors; analyze the multiple basic factors of the fault tree and the mutual influence and dependency relationship between the multiple basic factors based on network analysis method ANP;

[0007] Based on the ANP analysis results and the failure probabilities of the multiple basic factors, the ranking of key factors for the occurrence of the top event is determined.

[0008] In one embodiment, the similarity aggregation method is used to analyze the fault tree to determine the failure probability of the multiple basic factors, including:

[0009] Constructing an expert opinion fuzzy number, wherein the expert opinion fuzzy number includes converting the comments given by multiple experts on each basic factor in the fault tree, which indicate the probability of occurrence of an event, into a fuzzy number in advance;

[0010] Based on the expert opinion fuzzy number, the similarity between any two expert opinions is calculated, the average consistency of the experts is calculated based on the similarity, and the relative consistency of each expert is calculated based on the average consistency; the aggregation result of the expert opinion fuzzy number, i.e., the target fuzzy number, is calculated based on the relative consistency of each expert; the target fuzzy number is defuzzified to obtain a fuzzy possibility score, and the fuzzy possibility score is converted into a failure probability.

[0011] In one embodiment, the calculation of the aggregated result of the expert opinion fuzzy number based on the relative consistency of each expert, i.e., the target fuzzy number, includes:

[0012] Obtaining weight values ​​corresponding to each of the plurality of experts that are preset; calculating a weight coefficient for each expert based on the weight values ​​corresponding to each of the plurality of experts and the relative consistency of each expert;

[0013] The target fuzzy number is obtained by weighted summing up the weight coefficients of each expert and the fuzzy numbers given by each expert for each basic factor.

[0014] In one embodiment, the method further comprises:

[0015] Analyze and determine the minimum cut set and / or minimum path set of the fault tree; based on the minimum cut set and / or minimum path set, determine a plurality of key factors ranked first in importance among the plurality of basic factors;

[0016] Analyze the multiple key factors and the mutual influence and dependency between the multiple key factors based on the network analysis method ANP;

[0017] The key factor ranking of the top event is determined based on the ANP key factor analysis result and / or the failure probability of the multiple basic factors.

[0018] In one embodiment, the method further comprises:

[0019] Based on the failure probabilities of the multiple basic factors, determining multiple key factors that cause the top event to occur;

[0020] Analyze the multiple key factors and the mutual influence and dependency between the multiple key factors based on the network analysis method ANP;

[0021] The key factor ranking of the top event is determined based on the ANP key factor analysis result and / or the failure probability of the multiple basic factors.

[0022] In one embodiment, the method of analyzing the top events of the bank network system based on the fault tree analysis method and determining multiple basic factors affecting the stability of the bank network system to construct a fault tree includes:

[0023] Based on the top event, events at the physical layer, data link layer, network layer and transport layer involved in the OSI model of the bank network system are taken as sub-top events. By analyzing the risk of the top event and the sub-top event affecting the stability of the bank network system, multiple basic factor fault trees corresponding to specific multiple basic factors are obtained.

[0024] In one embodiment, the risk factors include risk factors related to technical risk, architectural risk, operational risk, personnel risk and management risk involved in the bank's network system.

[0025] In a second aspect, the present disclosure provides a banking network system stability assessment system, including:

[0026] A construction module is used to analyze the top event of the bank network system based on the fault tree analysis method, determine multiple basic factors that affect the stability of the bank network system to construct a fault tree; wherein the top event is an event related to the service interruption of the bank network system, and the multiple basic factors include risk factors of all aspects that affect the stability of the bank network system;

[0027] An analysis module is used to analyze the fault tree based on a similarity aggregation method to determine the failure probability of the multiple basic factors; and to analyze the multiple basic factors of the fault tree and the mutual influence and dependency relationship between the multiple basic factors based on an analysis network method (ANP);

[0028] The evaluation module is used to determine the ranking of key factors for the occurrence of the top event based on the ANP analysis results and the failure probabilities of the multiple basic factors.

[0029] In a third aspect, an embodiment of the present disclosure provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the banking network system stability assessment method of any of the above-mentioned embodiments.

[0030] In a fourth aspect, an embodiment of the present disclosure provides an electronic device, including:

[0031] Processor; and

[0032] Memory for storing computer programs;

[0033] Wherein, the processor is configured to execute the banking network system stability assessment method of any of the above-mentioned embodiments by executing the computer program.

[0034] Compared with the prior art, the technical solution provided by the embodiments of the present disclosure has the following advantages:

[0035] The bank network system stability assessment method and system provided by the disclosed embodiment analyzes the top events of the bank network system based on the fault tree analysis method FTA, determines multiple basic factors affecting the stability of the bank network system to construct a fault tree; wherein the top event is an event related to the service interruption of the bank network system, and the multiple basic factors include risk factors of all aspects affecting the stability of the bank network system; analyzes the fault tree based on the similarity aggregation method SAM to determine the failure probability of the multiple basic factors; analyzes the multiple basic factors of the fault tree and the mutual influence and dependency between the multiple basic factors based on the network analysis method ANP; and determines the ranking of key factors for the occurrence of the top event based on the ANP analysis results and the failure probability of the multiple basic factors. In this embodiment, the fault tree analysis method FTA is used to comprehensively analyze various factors that may cause instability of the bank network system from the top events that affect stability, ensure the comprehensiveness of the assessment data, and introduce the similarity aggregation method SAM) to accurately calculate the failure probability of the basic factors, thereby improving the accuracy of the bank network system stability assessment results as a whole. Then the network analysis method ANP is used to analyze the mutual influence and dependency between various factors, that is, taking into account the dynamic changes within the system, so that the evaluation results are more in line with the actual situation, that is, the accuracy of the bank network system stability evaluation results is higher. By verifying the ranking of the causes of sudden production events, it is ensured that the evaluation method can provide practical and accurate risk management and decision-making support for banks, which provides a more scientific, accurate and practical bank network system stability evaluation plan to cope with the increasingly complex financial environment and changing system risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0037] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0038] Figure 1 This is a flow chart of a method for evaluating the stability of a bank network system according to an embodiment of the present disclosure;

[0039] Figure 2 is a schematic diagram of risk factors of an embodiment of the present disclosure;

[0040] Figure 3 A schematic diagram of a fault tree analysis process according to an embodiment of the present disclosure;

[0041] Figure 4A schematic diagram of a bank network system stability assessment system according to an embodiment of the present disclosure;

[0042] Figure 5 Schematic diagram of an electronic device according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0043] In order to more clearly understand the above-mentioned objectives, features and advantages of the present disclosure, the scheme of the present disclosure will be further described below. It should be noted that the embodiments of the present disclosure and the features in the embodiments can be combined with each other without conflict.

[0044] In the following description, many specific details are set forth to facilitate a full understanding of the present disclosure, but the present disclosure may also be implemented in other ways different from those described herein; it is obvious that the embodiments in the specification are only part of the embodiments of the present disclosure, rather than all of the embodiments.

[0045] It should be understood that, in the following, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the associated objects before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0046] Most existing assessment methods involve hiring assessment agencies to refer to the network security level protection assessment requirements to determine the stability of network systems. However, since the assessment standards focus more on technical risks and architectural risks, and ignore operational risks, personnel risks, and management risks, they are unable to fully cover all potential risk points that affect the stability of bank network systems, and therefore are unable to fully and accurately reflect the true stability of the system.

[0047] Figure 1 This is a flow chart of a bank network system stability assessment method according to an embodiment of the present disclosure. The bank network system stability assessment method may be executed by a computing device, which may be a computing terminal in the bank network system or a computing terminal connected to the bank network system in communication. Specifically, the following steps may be included:

[0048] Step S101: Analyze the top event of the bank network system based on the fault tree analysis method, determine multiple basic factors that affect the stability of the bank network system to construct a fault tree; wherein the top event is an event related to the service interruption of the bank network system, and the multiple basic factors include risk factors of all aspects that affect the stability of the bank network system;

[0049] Exemplarily, in one embodiment, the risk factors include risk factors related to technical risk, architectural risk, operational risk, personnel risk and management risk involved in the bank network system. Figure 2 As shown in, for example, according to the five elements of "man, machine, material, method and environment" in security management theory, all risk factors in the production and operation process of the bank network system are identified and judged. Among them, man (Man): refers to the maintenance personnel, operators, operators, etc. involved, including their skills, experience and work attitude. Machine refers to the production network and security equipment of the network system, including the maintenance, performance and reliability of the equipment. Material refers to the relevant lines, modules, etc. Method refers to the production method or process flow, including operating procedures, process routes and production technology. Environment refers to the operating environment of the network system, including the physical environment, security threat environment, policy and regulatory environment, natural disaster environment and supply chain environment. The bank network system can be, for example, a bank backbone network system. Top events can be events related to service interruption of the bank network system, such as service interruption events caused by any of the risk factors in the above five elements. The system will record these events, such as through the log system.

[0050] As for the fault tree analysis method FTA, refer to Figure 3 As shown in , the fault tree takes a certain fault as the top event, deduces various possible causes of the fault, and then continues to reason and analyze until all basic factors (basic events) are found. Taking a bank's backbone network system as an example, the network system instability leads to sudden production events such as service interruption events as the top event, and the events of the physical layer, data link layer, network layer and transport layer involved in the OSI model are taken as the secondary top events. The stability risk analysis of the bank's network system is carried out to obtain a specific sudden production event fault tree, for example, 69 basic events or basic factors are obtained.

[0051] Step S102: Analyze the fault tree based on similarity aggregation method to determine the failure probability of the multiple basic factors; analyze the multiple basic factors of the fault tree and the mutual influence and dependency relationship between the multiple basic factors based on network analysis method ANP.

[0052] Exemplarily, the similarity aggregation method SAM is a technology used for data analysis and pattern recognition, which calculates the similarity between data points and aggregates the data points with high similarity to reveal the potential structure and relationship in the data. The network analysis method ANP is a decision analysis method that expands the traditional hierarchical analysis method (AHP) and provides a more comprehensive decision analysis framework by considering the dependencies and feedbacks between elements. ANP not only considers the independence between elements, but also the dependencies and feedbacks between elements, which makes it more flexible and effective in dealing with complex problems. In this embodiment, the fault tree analysis method (FTA), similarity aggregation method (SAM) and network hierarchy method (ANP) are used in combination to evaluate the stability of the bank network system.

[0053] Among them, the fault tree analysis (FTA) method starts from the top event and comprehensively analyzes various factors that may cause the instability of the bank network system. For example, in one example, the top event may be "bank network system service interruption", and through fault tree analysis, multiple causes of this event can be identified, such as hardware failure, software defects, human operation errors, external attacks, etc. By constructing a fault tree, these factors can be systematically identified and analyzed to ensure the comprehensiveness of the assessment.

[0054] Step S103: Based on the ANP analysis result and the failure probabilities of the multiple basic factors, determine the ranking of key factors for the occurrence of the top event.

[0055] Exemplarily, the ANP analysis result can be some of the key factors that are most likely to cause the top event to occur, determined from the multiple basic factors that cause the top event to occur, and the key factor ranking is achieved by combining the failure probabilities of some key factors. In this embodiment, the similarity aggregation method is introduced to accurately calculate the failure probabilities of the basic factors, and the network hierarchy method (ANP) is used to analyze the mutual influence and dependency between the factors. For example, in a bank network system, there are often complex interactions between different factors. For example, hardware failures may affect the normal operation of the software, and software defects may increase the risk of external attacks. The network hierarchy method provides a more comprehensive and accurate decision analysis result by considering the dependencies and feedback between these factors. Combining these methods can improve the accuracy of the bank network system stability assessment results.

[0056] By comprehensively using the fault tree analysis method (FTA), the similarity aggregation method (SAM) and the network hierarchy method (ANP), we can cope with the complexity and challenges of the stability assessment of the bank network system in the financial field, especially in the face of the growing risk of network attacks and system failures, and can scientifically and accurately assess and improve the stability of the bank network system. In addition, by verifying the ranking of the causes of sudden production events, that is, the ranking of key factors, we ensure that the assessment method can provide practical risk management and decision support for banks. For example, this method can identify the key reasons for the interruption of the bank network system service and rank them according to their importance. In this way, those problems that have the greatest impact on system stability can be solved first, thereby effectively improving the stability and security of the network system.

[0057] In one embodiment, in step S102, the fault tree is analyzed based on a similarity aggregation method to determine the failure probabilities of the multiple basic factors, including: constructing an expert opinion fuzzy number, the expert opinion fuzzy number includes converting the comments given by multiple experts on each basic factor in the fault tree, which indicate the likelihood of an event, into a fuzzy number in advance; calculating the similarity of any two expert opinions based on the expert opinion fuzzy number, calculating the average consistency of the experts based on the similarity, and calculating the relative consistency of each expert based on the average consistency; calculating the aggregation result of the expert opinion fuzzy number, i.e., the target fuzzy number, based on the relative consistency of each expert; defuzzifying the target fuzzy number to obtain a fuzzy possibility score, and converting the fuzzy possibility score into a failure probability.

[0058] In one embodiment, the above-mentioned calculation of the aggregation result of the expert opinion fuzzy number based on the relative consistency of each expert, that is, the target fuzzy number, specifically includes the following steps:

[0059] Obtaining weight values ​​corresponding to each of the plurality of experts that are preset; calculating a weight coefficient for each expert based on the weight values ​​corresponding to each of the plurality of experts and the relative consistency of each expert;

[0060] The target fuzzy number is obtained by weighted summing up the weight coefficients of each expert and the fuzzy numbers given by each expert for each basic factor.

[0061] For example, by constructing the expert positive trapezoidal fuzzy number E, such as quantifying each basic factor of the fault tree with natural language, the expert's comments on the probability of occurrence of the basic factors are divided into 5 levels: [very low (VL), low (L), medium (M), high (H), very high (VH)]. The comment level is introduced, and the expert judges each basic factor and gives a specific level, and then the fuzzy value of the corresponding level is determined by the fuzzy membership function, as shown in Table 1. Table 1 shows the positive trapezoidal fuzzy numbers E of 3 different experts for 3 basic factors.

[0062] Table 1

[0063]

[0064] The similarity between any two experts is calculated using the following formula:

[0065]

[0066] Among them, S(E i ,E j )∈[0,1]; E i =(e i1 ,e i2 ,e i3 ,e i4 ), E j =(e j1 ,e j2 ,e j3 ,e j4 ), respectively representing the trapezoidal fuzzy numbers of the i-th and j-th experts for u (=1, 2, 3, 4) basic factors;

[0067] Calculate the average agreement of experts [average agreement, A(E i )]:

[0068]

[0069] Among them, (Ei, Ej) is S(Ei, Ej), and N is the total number of experts.

[0070] Calculate the relative agreement degree of each expert [relative agreement degree, R(E i )]

[0071]

[0072] Since each expert has differences in professional titles, working time and education level, the scores also have different reference values. Therefore, the expert's weight coefficient is assigned by judging the expert's professional title level, education level, years of work, age and other factors to represent the relative importance of each expert's opinion.

[0073] Specifically, the importance w(E i ) is the weight value. Based on the weight value w(E i ) and the relative agreement of experts R(E i ), multiply the expert's weight coefficient C(E i )=w(E i )R(E i); then based on the weight coefficient C(E i ) and the fuzzy number E given by expert i for each basic factor i The target fuzzy number is obtained by weighted summation, that is, the target fuzzy number is obtained by summarizing the fuzzy opinions:

[0074]

[0075] Where: (·) is the fuzzy multiplication operator; E X is the fuzzy aggregation result of the basic factor X, that is, the target fuzzy number. The defuzzification process obtains the fuzzy possibility score (F)

[0076]

[0077] The fuzzy number of the triangle or trapezoid is converted into the fuzzy possibility score of the event to facilitate the analysis of the problem. F is the defuzzification result, i.e., the fuzzy possibility score, u(x) is the fuzzy membership function, and x is the input variable, i.e., the target fuzzy number. This method is derived from the center area method and the gravity center method and is used to defuzzify subjective evaluation. In practical applications, specifically, for the basic factor X, the fuzzy possibility score is the failure possibility score F X It is calculated as follows:

[0078]

[0079] In the fuzzy fault tree analysis method, since the failure probability of each basic factor is expressed by a fuzzy number such as a fuzzy probability score, there is a difference between the actual probability data and the calculated probability score. To solve this problem, the failure probability score F of the basic factor X (such as Xi, i = 1, 2, 3, ...) can be calculated by the following formula: X Converted into failure probability:

[0080] Here, K is determined by the fuzzy likelihood score.

[0081] In actual applications, multiple experts may give different opinions on the failure probability of the same basic factor. The similarity aggregation method calculates the similarity between these opinions and reasonably aggregates them to obtain a more objective and accurate estimate of the failure probability. For example, if three experts give different assessments of the failure probability of a certain hardware fault, the similarity aggregation method can calculate the similarity of these assessments and aggregate them into a comprehensive failure probability, thereby further improving the accuracy of the bank network system stability assessment results.

[0082] In one embodiment, the method further includes: analyzing and determining the minimum cut set and / or minimum path set of the fault tree; determining multiple key factors ranked first in importance among the multiple basic factors based on the minimum cut set and / or minimum path set; analyzing the multiple key factors and the mutual influence and dependency between the multiple key factors based on the network analysis method ANP; and determining the key factor ranking for the occurrence of the top event based on the ANP key factor analysis results and / or the failure probability of the multiple basic factors.

[0083] Exemplarily, for example, the fault tree can be analyzed qualitatively. The basic method is mainly to simplify the fault tree, find the minimum cut set and the minimum path set of the fault tree, and then compare the structural importance of each basic factor. The minimum cut set is the set of basic factors that cause the top event to occur, that is, the minimum cut set is the set of the minimum basic factors that must cause the top event to occur, which can be completed through a series of logical operations and analyses. For example, Boolean algebra is used to simplify the fault tree. Boolean algebra provides a set of rules to simplify logical expressions, such as De Morgan's law, absorption law, distribution law, etc., simplify all the cut sets obtained, find those cut sets that do not contain other cut sets, these are the minimum cut sets. The specific method of finding the minimum cut set and the minimum path set can be understood by referring to the prior art, and will not be repeated here. Through the minimum cut set and the minimum path set, multiple key factors ranked in the front of the importance of the multiple basic factors can be determined, such as the basic factors ranked in the front of the structural importance as the selected basic factors, that is, multiple key factors. Based on ANP analysis of multiple key factors and the mutual influence and dependency between multiple key factors, the key factor ranking of the top event is finally determined based on the ANP key factor analysis results and / or the failure probability of the multiple basic factors. In this way, the key factors leading to the occurrence of the top event can be accurately determined, thereby improving the accuracy of the stability assessment results of the bank network system, making the management and risk control of the stability of the bank network system more accurate.

[0084] After the initial identification of risk factors for the stability of the bank network system is completed in this embodiment, an evaluation procedure is established, which is mainly divided into several stages: constructing a fault tree diagram; introducing expert comments to construct fuzzy numbers; determining the weight coefficient of each expert; defuzzifying the fuzzy opinions of basic factors to obtain the failure probability of basic factors; analyzing the minimum cut set and minimum path set of the fault tree; finally, comprehensively evaluating the fault tree based on the data obtained from the previous analysis, thereby improving the accuracy of the stability evaluation results of the bank network system.

[0085] Among them, the sudden production incident of the bank that caused service interruption to the stability of the network system was taken as the top event. The occurrence of sudden production incidents was deduced from the five factors of man-machine-material-method-environment in safety management theory, and a fault tree diagram was drawn layer by layer to find out all possible basic factors and basic factor sets (i.e., the minimum cut set); secondly, according to SAM-FTA, the different opinions of multiple experts were reasonably aggregated to obtain the failure probability of each basic factor in the fault tree. Finally, ANP analysis was used to evaluate the failure of the bank network system, and the events with greater influence in the causes, i.e., the key factors, were further studied to improve the accuracy of the stability assessment results of the bank network system.

[0086] In one embodiment, the method further includes: determining multiple key factors that lead to the occurrence of the top event based on the failure probabilities of the multiple basic factors; analyzing the multiple key factors and the mutual influence and dependency between the multiple key factors based on the network analysis method (ANP); and determining the ranking of key factors for the occurrence of the top event based on the ANP key factor analysis results and / or the failure probabilities of the multiple basic factors.

[0087] Exemplarily, after determining the failure probability of the basic factors (basic events), the probability of occurrence of the top events can be quantitatively analyzed and determined based on this, and then the probability importance of each basic factor (basic event) is determined based on the probability of occurrence of the top events, and multiple key factors that lead to the occurrence of the top events are determined based on the probability importance ranking, that is, some basic factors among the multiple basic factors (basic events) are used as multiple key factors. Then, based on ANP, multiple key factors and the mutual influence and dependency between multiple key factors are analyzed, and finally, based on the ANP key factor analysis results and / or the failure probability of the multiple basic factors, the key factor ranking of the occurrence of the top events is comprehensively determined. In this way, the key factors that lead to the occurrence of the top events can be accurately determined, thereby improving the accuracy of the stability assessment results of the bank network system.

[0088] The solution of this embodiment innovatively combines a variety of advanced analysis methods to form a comprehensive, accurate and scientific banking network system stability assessment system. This method can not only comprehensively identify and analyze various factors that affect the stability of banking networks, but also accurately calculate the failure probability of these factors, and by analyzing the mutual influence between these factors, it provides banks with a practical risk management and decision support tool. The innovation and practicality of this assessment method are of great significance to the financial field, especially to the stability management and risk control of banking network systems. Through the application of actual cases, the solution demonstrates its significant advantages in improving the scientificity and effectiveness of banking network system stability assessment.

[0089] It should be noted that, although the steps of the method in the present disclosure are described in a specific order in the accompanying drawings, this does not require or imply that the steps must be performed in this specific order, or that all the steps shown must be performed to achieve the desired results. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step for execution, and / or one step may be decomposed into multiple steps for execution, etc. In addition, it is also easy to understand that these steps may be, for example, executed synchronously or asynchronously in multiple modules / processes / threads.

[0090] like Figure 4 As shown, the embodiment of the present disclosure provides a banking network system stability assessment system, including:

[0091] A construction module 401 is used to analyze the top event of the bank network system based on the fault tree analysis method, determine multiple basic factors that affect the stability of the bank network system to construct a fault tree; wherein the top event is an event related to the service interruption of the bank network system, and the multiple basic factors include risk factors of all aspects that affect the stability of the bank network system;

[0092] The analysis module 402 is used to analyze the fault tree based on the similarity aggregation method to determine the failure probability of the multiple basic factors; and to analyze the multiple basic factors of the fault tree and the mutual influence and dependency relationship between the multiple basic factors based on the network analysis method ANP;

[0093] The evaluation module 403 is used to determine the ranking of key factors for the occurrence of the top event based on the ANP analysis result and the failure probabilities of the multiple basic factors.

[0094] In one embodiment, the analysis module analyzes the fault tree based on a similarity aggregation method to determine the failure probabilities of the multiple basic factors, including:

[0095] Constructing an expert opinion fuzzy number, wherein the expert opinion fuzzy number includes converting the comments given by multiple experts on each basic factor in the fault tree, which indicate the probability of occurrence of an event, into a fuzzy number in advance;

[0096] Based on the expert opinion fuzzy number, the similarity between any two expert opinions is calculated, the average consistency of the experts is calculated based on the similarity, and the relative consistency of each expert is calculated based on the average consistency; the aggregation result of the expert opinion fuzzy number, i.e., the target fuzzy number, is calculated based on the relative consistency of each expert; the target fuzzy number is defuzzified to obtain a fuzzy possibility score, and the fuzzy possibility score is converted into a failure probability.

[0097] In one embodiment, the analysis module calculates the aggregated result of the expert opinion fuzzy number, i.e., the target fuzzy number, based on the relative consistency of each expert, including:

[0098] Obtaining weight values ​​corresponding to each of the plurality of experts that are preset; calculating a weight coefficient for each expert based on the weight values ​​corresponding to each of the plurality of experts and the relative consistency of each expert;

[0099] The target fuzzy number is obtained by weighted summing up the weight coefficients of each expert and the fuzzy numbers given by each expert for each basic factor.

[0100] In one embodiment, the system also includes a first determination module, which is used to: analyze and determine the minimum cut set and / or minimum path set of the fault tree; based on the minimum cut set and / or minimum path set, determine multiple key factors with the highest importance ranking among the multiple basic factors; the analysis module can also be used to analyze the multiple key factors and the mutual influence and dependency between the multiple key factors based on the network analysis method ANP; the evaluation module can also be used to determine the key factor ranking of the top event based on the ANP key factor analysis results and / or the failure probability of the multiple basic factors.

[0101] In one embodiment, the system also includes a second determination module, which is used to: determine the multiple key factors that lead to the occurrence of the top event based on the failure probability of the multiple basic factors; the analysis module can also be used to analyze the multiple key factors and the mutual influence and dependency between the multiple key factors based on the network analysis method ANP; the evaluation module can also be used to determine the ranking of the key factors for the occurrence of the top event based on the ANP key factor analysis results and / or the failure probability of the multiple basic factors.

[0102] In one embodiment, the construction module analyzes the top event of the bank network system based on the fault tree analysis method, determines multiple basic factors that affect the stability of the bank network system to construct a fault tree, including: based on the top event, events of the physical layer, data link layer, network layer and transport layer involved in the OSI model of the bank network system are used as secondary top events, and multiple basic factor fault trees corresponding to specific multiple basic factors are obtained by performing risk analysis on the top event and the secondary top event on the impact on the stability of the bank network system.

[0103] In one embodiment, the risk factors include risk factors related to technical risk, architectural risk, operational risk, personnel risk and management risk involved in the bank's network system.

[0104] Regarding the system in the above embodiment, the specific manner in which each module performs operations and the corresponding technical effects brought about have been described in detail in the embodiment of the method, and will not be elaborated here.

[0105] It should be noted that although several modules or units of the device for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiment of the present disclosure, the features and functions of two or more modules or units described above can be concretized in one module or unit. Conversely, the features and functions of a module or unit described above can be further divided into multiple modules or units for concretization. The components displayed as modules or units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the disclosed solution. Those of ordinary skill in the art can understand and implement it without paying creative work.

[0106] The embodiments of the present disclosure also provide a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the method for evaluating the stability of a banking network system according to any of the above embodiments is implemented.

[0107] Exemplarily, the readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0108] The computer readable storage medium may include a data signal propagated in a baseband or as part of a carrier wave, wherein a readable program code is carried. This propagated data signal may take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. The readable storage medium may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by an instruction execution system, an apparatus, or a device or used in combination with it. The program code contained on the readable storage medium may be transmitted with any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the above.

[0109] The disclosed embodiment also provides an electronic device, comprising a processor and a memory, wherein the memory is used to store a computer program, wherein the processor is configured to execute the banking network system stability assessment method in any of the above embodiments by executing the computer program.

[0110] Refer to the following Figure 5The electronic device 600 according to this embodiment of the present invention is described. Figure 5 The electronic device 600 shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0111] like Figure 5 As shown, the electronic device 600 is in the form of a general computing device. The components of the electronic device 600 may include, but are not limited to: at least one processing unit 610, at least one storage unit 620, a bus 630 connecting different system components (including the storage unit 620 and the processing unit 610), a display unit 640, etc.

[0112] The storage unit stores program codes, which can be executed by the processing unit 610, so that the processing unit 610 performs the steps according to various exemplary embodiments of the present invention described in the above method embodiment section of this specification. For example, the processing unit 610 can perform the following steps: Figure 1 The steps of the method shown in .

[0113] The storage unit 620 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 6201 and / or a cache memory unit 6202 , and may further include a read-only memory unit (ROM) 6203 .

[0114] The storage unit 620 may also include a program / utility 6204 having a set (at least one) of program modules 6205, such program modules 6205 including but not limited to: an operating system, one or more application programs, other program modules and program data, each of which or some combination may include the implementation of a network environment.

[0115] Bus 630 may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0116] The electronic device 600 may also communicate with one or more external devices 700 (e.g., keyboards, pointing devices, Bluetooth devices, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 600, and / or communicate with any device that enables the electronic device 600 to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed via an input / output (I / O) interface 650. Furthermore, the electronic device 600 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via a network adapter 660. The network adapter 660 may communicate with other modules of the electronic device 600 via a bus 630. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0117] Through the description of the above implementation methods, it is easy for those skilled in the art to understand that the example implementation methods described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the implementation methods of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, or a network device, etc.) to execute the method steps of the above-mentioned embodiments according to the implementation methods of the present disclosure.

[0118] It should be noted that, in this article, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0119] The above description is only a specific embodiment of the present disclosure, so that those skilled in the art can understand or implement the present disclosure. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present disclosure. Therefore, the present disclosure will not be limited to the embodiments described herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A banking network system stability assessment method, characterized in that: include: Analyze the top events of the bank network system based on the fault tree analysis method, determine multiple basic factors that affect the stability of the bank network system to construct a fault tree; wherein the top event is an event related to the service interruption of the bank network system, and the multiple basic factors include risk factors of all aspects that affect the stability of the bank network system; Analyze the fault tree based on similarity aggregation method to determine the failure probability of the multiple basic factors; analyze the multiple basic factors of the fault tree and the mutual influence and dependency relationship between the multiple basic factors based on network analysis method ANP; Based on the ANP analysis results and the failure probabilities of the multiple basic factors, the ranking of key factors for the occurrence of the top event is determined.

2. The method according to claim 1, characterized in that The similarity aggregation method is used to analyze the fault tree to determine the failure probability of the multiple basic factors, including: Constructing an expert opinion fuzzy number, wherein the expert opinion fuzzy number includes converting the comments given by multiple experts on each basic factor in the fault tree, which indicate the probability of occurrence of an event, into a fuzzy number in advance; Based on the expert opinion fuzzy number, the similarity between any two expert opinions is calculated, the average consistency of the experts is calculated based on the similarity, and the relative consistency of each expert is calculated based on the average consistency; the aggregation result of the expert opinion fuzzy number, i.e., the target fuzzy number, is calculated based on the relative consistency of each expert; the target fuzzy number is defuzzified to obtain a fuzzy possibility score, and the fuzzy possibility score is converted into a failure probability.

3. The method according to claim 2, characterized in that The aggregation result of the expert opinion fuzzy number calculated based on the relative consistency of each expert, i.e., the target fuzzy number, includes: Obtaining weight values ​​corresponding to each of the plurality of experts that are preset; calculating a weight coefficient for each expert based on the weight values ​​corresponding to each of the plurality of experts and the relative consistency of each expert; The target fuzzy number is obtained by weighted summing up the weight coefficients of each expert and the fuzzy numbers given by each expert for each basic factor.

4. The method according to claim 2 or 3, characterized in that: The method further includes: Analyze and determine the minimum cut set and / or minimum path set of the fault tree; based on the minimum cut set and / or minimum path set, determine a plurality of key factors ranked first in importance among the plurality of basic factors; Analyze the multiple key factors and the mutual influence and dependency between the multiple key factors based on the network analysis method ANP; The key factor ranking of the top event is determined based on the ANP key factor analysis result and / or the failure probability of the multiple basic factors.

5. The method according to claim 2 or 3, characterized in that: The method further includes: Based on the failure probabilities of the multiple basic factors, determining multiple key factors that cause the top event to occur; Analyze the multiple key factors and the mutual influence and dependency between the multiple key factors based on the network analysis method ANP; The key factor ranking of the top event is determined based on the ANP key factor analysis result and / or the failure probability of the multiple basic factors.

6. The method according to any one of claims 1 to 3, characterized in that: The method of analyzing the top events of the bank network system based on the fault tree analysis method determines multiple basic factors affecting the stability of the bank network system to construct a fault tree, including: Based on the top event, events at the physical layer, data link layer, network layer and transport layer involved in the OSI model of the bank network system are taken as sub-top events. By analyzing the risk of the top event and the sub-top event affecting the stability of the bank network system, multiple basic factor fault trees corresponding to specific multiple basic factors are obtained.

7. The method according to any one of claims 1 to 3, characterized in that: The risk factors include risk factors related to technical risks, architectural risks, operational risks, personnel risks and management risks involved in the bank's network system.

8. A banking network system stability assessment system, characterized in that: include: A construction module is used to analyze the top event of the bank network system based on the fault tree analysis method, determine multiple basic factors that affect the stability of the bank network system to construct a fault tree; wherein the top event is an event related to the service interruption of the bank network system, and the multiple basic factors include risk factors of all aspects that affect the stability of the bank network system; An analysis module, configured to analyze the fault tree based on a similarity aggregation method to determine failure probabilities of the multiple basic factors; Analyzing multiple basic factors of the fault tree and the mutual influence and dependency between the multiple basic factors based on the network analysis method ANP; The evaluation module is used to determine the ranking of key factors for the occurrence of the top event based on the ANP analysis results and the failure probabilities of the multiple basic factors.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the banking network system stability assessment method according to any one of claims 1 to 7 is implemented.

10. An electronic device, characterized in that: include: processor; as well as Memory for storing computer programs; Wherein, the processor is configured to execute the banking network system stability assessment method according to any one of claims 1 to 7 by executing the computer program.

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