CN114499957A - Network information security dynamic evaluation system and method thereof - Google Patents

Network information security dynamic evaluation system and method thereof Download PDF

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CN114499957A
CN114499957A CN202111599422.1A CN202111599422A CN114499957A CN 114499957 A CN114499957 A CN 114499957A CN 202111599422 A CN202111599422 A CN 202111599422A CN 114499957 A CN114499957 A CN 114499957A
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information security
index
attribute
weighting
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张德方
叶其革
唐宗顺
姚灏
严晓玲
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Guangzhou Electric Power Design Institute Co ltd
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Abstract

The invention relates to a system and a method for dynamically evaluating network information security, which aim to solve the technical problems that the prior similar technology lacks an evaluation mechanism for evaluating whether network information can be safely utilized in real time and cannot dynamically evaluate the network information security. The method is characterized in that a network information security dynamic evaluation model is obtained by adopting a method of comprehensive weighting and introducing symmetrical interaction entropy multi-attribute sequencing, the model carries out attribute definition on key network information security evaluation indexes through information confidentiality, information integrity, information threat, information weakness and information security control measures, then carries out weighting calculation on relative closeness as a network information security index through a symmetrical interaction entropy multi-attribute sequencing method, and finally evaluates network information security according to the size range of the network information security index.

Description

Network information security dynamic evaluation system and method thereof
Technical Field
The invention relates to a network information security dynamic evaluation system, in particular to a network information security dynamic evaluation system and a network information security dynamic evaluation method.
Background
The network information security is a comprehensive discipline relating to various disciplines such as computer disciplines, network technologies, communication technologies, cryptographic technologies, information security technologies, application mathematics and the like, and mainly means that hardware, software and data in a network system are protected and are not damaged, changed and leaked due to accidental or malicious reasons, the system continuously, reliably and normally operates, and network service is not interrupted. The main characteristics of the network information security include integrity, confidentiality, availability, non-repudiation and controllability, and the model framework comprises a network security model and an information security framework, and a network information data evaluation result is output through a model and an algorithm. The network information security dynamic evaluation system refers to a system for dynamically analyzing and tracking network information security, such as application number 201810567756.2 disclosed in chinese patent literature, application publication date 2018.12.07, and the invention name "a network information security detection system and a detection method thereof"; however, the existing network security industry still lacks an evaluation mechanism for real-time safe utilization of network information, and cannot dynamically evaluate the network information security.
Disclosure of Invention
In order to overcome the above disadvantages, the present invention provides a system and a method for dynamically evaluating network information security in the field, so as to mainly solve the technical problem that the existing similar technologies lack an evaluation mechanism for evaluating whether network information can be safely utilized in real time, and cannot dynamically evaluate network information security. The purpose is realized by the following technical scheme.
A method for a network information security dynamic evaluation system is characterized in that the method adopts a comprehensive weighting and symmetrical interaction entropy multi-attribute sequencing method to obtain a network information security dynamic evaluation model, the model carries out attribute definition on key network information security evaluation indexes through key network information security evaluation indexes of information confidentiality, information integrity, information threat, information weak point and information security control measures, then carries out weighting calculation on relative closeness through a symmetrical interaction entropy multi-attribute sequencing method to serve as a network information security index, and finally evaluates network information security according to the size range of the network information security index.
The process of the symmetrical interaction entropy multi-attribute sorting method is as follows:
setting n evaluation indexes and m schemes in a multi-attribute decision problem, wherein the scheme set comprises the following steps: a ═ A1，A2，…，Am) The index set is as follows: b ═ B (B)1，B2，…，Bm) Scheme AiFor the index BjThe value of (d) is noted as: y isij(i＝1，2，…，m；j＝1，2，…，n)，
Then an objective decision matrix is formed:
Figure BDA0003432684760000021
from a decision matrix Y Yij]m×nA normalized decision matrix is established according to:
Figure BDA0003432684760000022
the weighting of the symmetrical interaction entropy multi-attribute sorting method flow adopts a combined weighting method combining an analytic hierarchy process and an objective weighting method to determine the index weight, index BjWeight ω of (d)jComprises the following steps:
Figure BDA0003432684760000023
in equation (5): alpha is alphajWeights obtained by the analytic hierarchy process; beta is a betajWeighting the objective weighting method;
to construct the weighted normalization matrix and determine the ideal solution thereof, the dimensionless decision matrix is multiplied by the weights of the indexes to obtain the weighted decision matrix equation as follows:
xij-wjxij (6)
separately calculating a positive ideal solution vector X for a larger and a smaller optimal model+And a negative ideal solution vector X-The larger the size, the more preferred the type isThe positive ideal solution vector X refers to an information index with positive attribute of safety control measure, the smaller the more optimal the value is, the more negative the value is+And negative ideal solution vector X-The formula of (1) is as follows:
positive:
Figure BDA0003432684760000024
negative:
Figure BDA0003432684760000025
the formula for calculating the symmetric interaction entropy is as follows:
Figure BDA0003432684760000026
segment of
Figure BDA0003432684760000027
And
Figure BDA0003432684760000028
respective scheme AiWith the ideal case that the ith column vector and solution vector of matrix X are
Figure BDA0003432684760000029
With negative ideality, i.e. solution vector of
Figure BDA00034326847600000210
The symmetric interaction entropy yields:
Figure BDA00034326847600000211
Figure BDA0003432684760000031
calculating relative closeness CiRelative closeness C of each solution to the ideal solutioniBy the following formula:
Figure BDA0003432684760000032
is calculated to obtainiThe closer the value is to 1, the closer the ith evaluation scheme is to the optimal scheme; ciThe smaller the value, the worse the i-th evaluation scheme.
The weight beta obtained by the objective weighting methodjThe formula flow is as follows: passing index C'jThe formula:
Figure BDA0003432684760000033
in the formula (1), σjIs the standard deviation of the j index, and r is the random number of (0, 1); weight β of j-th indexjThe formula is as follows:
Figure BDA0003432684760000034
obtaining a weight beta obtained by an objective weighting methodjWeight β from objective weightingjIs the CRITIC method weight.
The CRITIC method weight is used as a weight of the analytic hierarchy process scale, and the other weight is s0/6～s8/6The scale serves as an importance scale for the indices of the analytic hierarchy process.
According to the method, the system adopts a comprehensive weighting and symmetrical interaction entropy multi-attribute sequencing method to obtain a network information security dynamic evaluation model, the model carries out attribute definition on key network information security evaluation indexes through key network information security evaluation indexes of information confidentiality, information integrity, information threat, information weakness and information security control measures, and then carries out weighting calculation on relative closeness through a symmetrical interaction entropy multi-attribute sequencing method to serve as a network information security index, and the network information security dynamic evaluation model is established.
The modeling mode of the invention is scientific, the network information is evaluated in real time and dynamically evaluated, and the invention has scientificity, high efficiency and high precision; the method is suitable for being used as a network information security dynamic evaluation system and a method thereof, and the technical improvement of the similar system and method thereof.
Detailed Description
The specific embodiments are now grouped together to further describe the specific implementation steps of the present invention. The method of the network information security dynamic evaluation system adopts comprehensive weighting and a method of introducing symmetrical interaction entropy multi-attribute sequencing to obtain a network information security dynamic evaluation model, the model obtains key network information security evaluation indexes of information security, information integrity, information threat, information weakness and information security control measures through attribute definition of the key network information security evaluation indexes, carries out weighting calculation on relative closeness degree through the symmetrical interaction entropy multi-attribute sequencing method to serve as a network information security index, and finally evaluates network information security according to the size range of the network information security index. The method and the system obtain a network information security dynamic evaluation result through the following four parts of network information security evaluation index analysis, analytic hierarchy process scale, CRITIC method weight and symmetrical interaction entropy multi-attribute sequencing method processes, and specifically comprise the following steps:
1. analyzing the network information safety evaluation index,
table 1: key index for network information safety evaluation
Information index Privacy Integrity of Threat property Weak point of weakness Safety control measures
Properties Negative pole Negative pole Negative pole Negative pole Is just
Table 2: information privacy assignment
Figure BDA0003432684760000041
Table 3: information integrity assignment
Figure BDA0003432684760000042
Table 4: information threat valuation
Assignment of value Definition of
1 The probability of the threatened situation is 0 to 1 percent and is very low
2 The probability of the threatened situation is less than 20 percent, and generally the situation can not occur
3 The probability of the threatened situation is 20% -50%, and the situation may occur but is not found
4 The probability of the threatened situation is 50 to 90 percent
5 The probability of the threatened condition is more than 90 percent and the threatened condition occurs for a plurality of times
Table 5: information vulnerability assignment
Figure BDA0003432684760000043
Figure BDA0003432684760000051
Table 6: information security control measure assignment
Figure BDA0003432684760000052
2. On an analytical hierarchy scale, the analytical hierarchy is,
the analytic hierarchy process is a systematic analysis process combining qualitative and quantitative methods, and is used to decompose complex problems into several layers and several factors, and compare every two factors to obtain different weight of problem solving scheme0/6～e8/6The scale is used as an importance scale for the indices of the analytic hierarchy process:
Figure BDA0003432684760000053
the above importance scale and the following optimal solution have the following logical relationship: the importance scale is a weight, the system has two weights (importance identification and CRITIC method), hereinafter "optimal solution" means "ideal evaluation result", the system considers five indexes of network information: information confidentiality, information integrity, information threat, information weak point and information security control measures; ideally, the system would expect the better the confidentiality for information security is disclosed, the lower the integrity loss, the better the threat probability, the lower the vulnerability is exploited, the better the security control measures.
3. The weight of the CRITIC method is used,
the basic idea of the CRITIC method is based on contrast strength and conflict when constructing weights; the contrast intensity is expressed in the form of standard deviation, and the larger the standard deviation of the index is, the larger the difference of the evaluation objects is; the conflict is expressed by the correlation coefficient between the indexes, and if the correlation between the indexes is strong, the conflict is weak. Index C'jThe formula is as follows:
Figure BDA0003432684760000061
in equation (1): sigmajIs the standard deviation of the jth index; r is a random number of (0, 1);
weight β of j-th indexjThe formula is as follows:
Figure BDA0003432684760000062
obtaining a weight beta obtained by an objective weighting methodj。
4. A symmetrical interaction entropy multi-attribute sequencing method flow,
setting n evaluation indexes and m schemes in a multi-attribute decision problem, wherein the scheme set comprises the following steps: a ═ A1，A2，…，Am) The index set is: b ═ B (B)1，B2，…，Bm) Scheme AiFor the index BjThe value of (d) is noted as: y isij(i＝1，2，…，m；j＝12，…，n)，
Then an objective decision matrix is formed:
Figure BDA0003432684760000063
from a decision matrix Y Yij]m×nA normalized decision matrix is established according to:
Figure BDA0003432684760000064
the weighting of the symmetrical interaction entropy multi-attribute sorting method flow adopts a combined weighting method combining an analytic hierarchy process and an objective weighting method to determine the index weight, index BjWeight ω of (d)jComprises the following steps:
Figure BDA0003432684760000065
in equation (5): alpha is alphajWeights obtained by the analytic hierarchy process; beta is ajThe weights obtained by the objective weighting method.
To construct the weighted normalization matrix and determine the ideal solution thereof, the dimensionless decision matrix is multiplied by the weights of the indexes to obtain the weighted decision matrix equation as follows:
xij-ωj2ij (6)
separately calculating a positive ideal solution vector X for a larger and more optimal model and a smaller and more optimal model+And a negative ideal solution vector X-The larger the optimal type is, the more positive the attribute of the security control measure is, the smaller the optimal type is, the more negative the attribute of the information confidentiality, the information integrity, the information threat, and the information weakness is, and the above-mentioned positive ideal solution vector X is+And a negative ideal solution vector X-The formula of (1) is as follows:
positive:
Figure BDA0003432684760000071
negative:
Figure BDA0003432684760000072
the formula for calculating the symmetric interaction entropy is as follows:
Figure BDA0003432684760000073
is provided with
Figure BDA0003432684760000074
And
Figure BDA0003432684760000075
respective scheme AiWith the ideal case that the ith column vector and solution vector of matrix X are
Figure BDA0003432684760000076
With negative ideality, i.e. solution vector of
Figure BDA0003432684760000077
The symmetric interaction entropy yields:
Figure BDA0003432684760000078
Figure BDA0003432684760000079
calculating relative closeness CiRelative closeness of each solution to the ideal solution CiBy the following formula:
Figure BDA00034326847600000710
is calculated to obtainiThe closer the value is to 1, the closer the ith evaluation scheme is to the optimal scheme; ciThe smaller the value, the worse the i-th evaluation scheme.
The above evaluation scheme is excellent and poor, and is measured by the following criteria: with CiThe value is measured, formula (12) calculates CiIf C is presentiWhen the network information is 1, the network information is absolutely safe, i.e. the "optimal solution". In the actual evaluation, CiThe closer to 1, the more secure the network information is; ciSmaller or further away from 1, this means that the network information is more dangerous, i.e. the evaluation scheme is worse.
In conclusion, the evaluation system fully considers key indexes of network information security, such as information confidentiality, information integrity, information weak point, information threat, information security control measures and the like, comprehensively weights by adopting a method of combining an analytic hierarchy process and an objective weighting method, further establishes a network information security dynamic evaluation model based on a symmetric interaction entropy multi-attribute decision sorting method, and evaluates the network information security through the model. The model is operated in a single direction, and an evaluation result is output when network data is input, wherein the dynamic state refers to the real-time input of the network data so as to realize real-time and dynamic evaluation on the network data.

Claims (5)

1. A method for a network information security dynamic evaluation system is characterized in that the method adopts a comprehensive weighting and symmetrical interaction entropy multi-attribute sequencing method to obtain a network information security dynamic evaluation model, the model carries out attribute definition on key network information security evaluation indexes through key network information security evaluation indexes of information confidentiality, information integrity, information threat, information weak point and information security control measures, then carries out weighting calculation on relative closeness through a symmetrical interaction entropy multi-attribute sequencing method to serve as a network information security index, and finally evaluates network information security according to the size range of the network information security index.
2. The method of the network information security dynamic evaluation system according to claim 1, wherein the flow of the symmetric interaction entropy multiattribute ranking method is specifically as follows:
setting n evaluation indexes and m schemes in a multi-attribute decision problem, wherein the scheme set comprises the following steps: a ═ A (A)1，A2，…，Am) The index set is as follows: b ═ B (B)1，B2，…，Bm) Scheme AiFor the index BjThe value of (d) is noted as: y isij(i＝1，2，…，m；j＝1，2，…，n)，
Then an objective decision matrix is formed:
Figure FDA0003432684750000011
from a decision matrix Y Yij]m×nA normalized decision matrix is established according to:
Figure FDA0003432684750000012
the weighting of the symmetrical interaction entropy multi-attribute sorting method flow adopts a combined weighting method combining an analytic hierarchy process and an objective weighting method to determine the index weight, index BjWeight ω of (c)jComprises the following steps:
Figure FDA0003432684750000013
in equation (5): alpha is alphajWeights obtained by the analytic hierarchy process; beta is ajWeighting the objective weighting method;
to construct the weighted normalization matrix and determine the ideal solution thereof, the dimensionless decision matrix is multiplied by the weights of the indexes to obtain the weighted decision matrix equation as follows:
xij-ωjzij (6)
separately calculating a positive ideal solution vector X for a larger and a smaller optimal model+And a negative ideal solution vector X-The larger the optimal type is, the more positive the attribute of the security control measure is, the smaller the optimal type is, the more negative the attribute of the information confidentiality, the information integrity, the information threat, and the information weakness is, and the above-mentioned positive ideal solution vector X is+And a negative ideal solution vector X-The formula of (1) is as follows:
positive:
Figure FDA0003432684750000021
negative:
Figure FDA0003432684750000022
the formula for calculating the symmetric interaction entropy is as follows:
Figure FDA0003432684750000023
is provided with
Figure FDA0003432684750000024
And
Figure FDA0003432684750000025
respective scheme AiWith the ideal case that the ith column vector and solution vector of matrix X are
Figure FDA0003432684750000026
With negative ideality, i.e. solution vectors of
Figure FDA0003432684750000027
The symmetric interaction entropy yields:
Figure FDA0003432684750000028
Figure FDA0003432684750000029
calculating relative closeness CiRelative closeness C of each solution to the ideal solutioniBy the following formula:
Figure FDA00034326847500000210
is calculated to obtainiThe closer the value is to 1, the more the ith evaluation scheme is illustratedThe closer to the optimal solution; ciThe smaller the value, the worse the i-th evaluation scheme.
3. The method of claim 2, wherein the objective weighting method obtains the weight βjThe formula flow is as follows: passing index C'jThe formula:
Figure FDA00034326847500000211
in the formula (1), σjIs the standard deviation of the j index, and r is the random number of (0, 1); weight β of j-th indexjThe formula is as follows:
Figure FDA00034326847500000212
obtaining a weight beta obtained by an objective weighting methodjWeight β from objective weightingjIs the CRITIC method weight.
4. The method of claim 3, wherein the CRITIC method weight is used as a weight of the analytic hierarchy process scale, and another weight is e0/6～e8/6The scale serves as an importance scale for the indices of the analytic hierarchy process.
5. The system according to claim 1, wherein the system employs a method of comprehensive weighting and introducing symmetric entropy multiattribute ordering to obtain a network information security dynamic evaluation model, and the model establishes the network information security dynamic evaluation model by performing attribute definition on key network information security evaluation indexes through key network information security evaluation indexes of information confidentiality, information integrity, information threat, information weakness and information security control measures, and performing weighted calculation on relative closeness as a network information security index through the symmetric entropy multiattribute ordering method.
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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116015894A (en) * 2022-12-28 2023-04-25 深圳市神飞致远技术有限公司 Information security management method and system
CN117118717A (en) * 2023-09-01 2023-11-24 湖北顺安伟业科技有限公司 User information threat analysis method and system
CN117118717B (en) * 2023-09-01 2024-05-31 湖北顺安伟业科技有限公司 User information threat analysis method and system

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116015894A (en) * 2022-12-28 2023-04-25 深圳市神飞致远技术有限公司 Information security management method and system
CN117118717A (en) * 2023-09-01 2023-11-24 湖北顺安伟业科技有限公司 User information threat analysis method and system
CN117118717B (en) * 2023-09-01 2024-05-31 湖北顺安伟业科技有限公司 User information threat analysis method and system

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