CN116415836A - Security evaluation method for intelligent power grid information system - Google Patents
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Abstract
The invention discloses a security evaluation method for a smart grid information system, which comprises the following steps: s1, constructing a security evaluation framework based on model adaptation; s2, determining a safety evaluation model of the intelligent power grid information system; s3, collecting data of a security evaluation index system of an evaluation object; s4, based on the information security measurement model library, comprehensive security evaluation algorithm is applied to carry out comprehensive measurement on the security risk and security of the information system; and S5, obtaining an adaptive security evaluation model for the type of evaluation object based on a security evaluation model rationality verification method with consistent head-to-tail sequence. The invention establishes a main body security target formalization mechanism based on mapping, and establishes a strategy and a reference mechanism for the security target decomposition and establishment of an evaluation object.
Description
Technical Field
The invention belongs to the technical field of power systems, and particularly relates to a security evaluation method for a smart grid information system.
Background
With the development and continuous perfection of the intelligent power grid information system, the system adopts different safety protection measures to provide safety capability for the system, and the normal operation of the information system is maintained. With the development of attack technology, uncertainty of safety effect provided by traditional security measures such as a firewall and an intrusion detection system is increased, and safety evaluation operation needs to be carried out on a power grid information system regularly.
The information system safety evaluation is a comprehensive system safety embodiment, and the information safety evaluation process is dynamic aiming at an information system evaluation object. The security state of the information system is subjected to complex evolution along with the change of the external environment and the change of the self value, and the occurrence of attack events and the selection of security reinforcement strategies both affect the security state of the information system. Meanwhile, the information security evaluation data sources which can be provided by different information systems are different, and the requirements of decision makers of the information systems on the real-time performance and the like of the system security evaluation are also different.
Aiming at the requirement of developing safety evaluation of the intelligent power grid information system, the existing safety evaluation method develops researches from different angles, such as a correlation analysis evaluation method based on an attack graph, a safety evaluation method based on machine learning and the like. The data sources required by the security evaluation method are different, and the calculated system security quantitative values cannot be directly compared due to the lack of a unified security calculation baseline, so that a conclusion of which model has a better evaluation effect is obtained.
Disclosure of Invention
The technical problem to be solved by the embodiment of the invention is to provide a security evaluation method for a smart grid information system so as to improve the comparability among different security evaluation methods and select an optimal security evaluation model for a specific security evaluation object.
In order to solve the technical problems, the invention provides a security evaluation method for a smart grid information system, which comprises the following steps:
s1, constructing a security evaluation framework based on model adaptation;
s2, determining a safety evaluation model of the intelligent power grid information system;
s3, collecting data of a security evaluation index system of an evaluation object;
s4, based on the information security measurement model library, comprehensive security evaluation algorithm is applied to carry out comprehensive measurement on the security risk and security of the information system;
and S5, obtaining an adaptive security evaluation model for the type of evaluation object based on a security evaluation model rationality verification method with consistent head-to-tail sequence.
Further, in the step S1, constructing a security evaluation framework based on model adaptation specifically includes: and determining a system security target formalization mechanism based on mapping, an adaptation information security measurement model and a security evaluation model based on consistent head-to-tail sequence for rationality verification.
Further, a system security target formalization mechanism based on mapping is determined, and in particular, the security target of the intelligent power grid information system is formalized into four primary indexes and eleven secondary indexes, and a security evaluation index system of the intelligent power grid information system is formalized and constructed; the four primary indexes comprise threat information, assets, vulnerability and management; eleven secondary metrics include system protection rate, attack detection rate, confidentiality, availability, controllability, recognizability, number of vulnerabilities, vulnerability hazard level, employee organization, enterprise level, and service continuity.
Further, the step S2 specifically includes:
analyzing the characteristics of the evaluation object to strategically decompose the safety target to form a safety evaluation index system;
selecting a corresponding information security evaluation measurement method, and quantitatively calculating a comprehensive security score and a risk value of the system;
the safety of the intelligent power grid information system is enhanced by taking the risk continuous consistency and the residual risk as feedback;
the reasonability verification method of the safety evaluation model based on the consistency of the head-to-tail sequences judges the reasonability of the evaluation model by using the consistency of the head-to-tail sequences and the amplitude reduction ratio.
Further, in the step S3, an expert scoring method is adopted to score the mutual influence relationship among the 9 secondary indexes of the evaluation object; the 9 secondary indexes comprise confidentiality, availability, controllability, identifiability, vulnerability quantity, vulnerability hazard level, employee organization, enterprise level and service continuity; and (3) judging the safety states of the 9 secondary indexes by adopting an expert scoring method to obtain an expert scoring table, and further obtaining an evaluation membership matrix.
Further, in the step S4, the information security measurement model library integrates multiple information security evaluation models, including a model based on association analysis, a model based on mathematical principles, a model based on attack graphs, a model based on game theory, and a model based on analytic hierarchy process; and carrying out safety evaluation of the information system by using 3 or more information safety evaluation models, and calculating to obtain a safety value and a system risk value of the information system.
Further, the method for calculating the safety value and the system risk value of the information system specifically adopts an improved comprehensive safety evaluation method based on ideal solution similarity sorting, and comprises two stages of index right determination and comprehensive quantification.
Further, the method for calculating the safety value and the system risk value of the information system by adopting the improved comprehensive safety evaluation method based on the ideal solution similarity ordering specifically comprises the following steps:
setting five levels of ideal solutions according to five security levels of an information system, and determining five security levels of an original data matrix and an optimal solution matrix;
calculating the evaluation scores of the indexes and the optimal solutions of the five grades;
calculating Euclidean distances from the evaluation object to the five-level optimal solutions respectively;
normalizing the five euclidean distances to construct a weight matrix;
the security value s of the information system is calculated according to the following formula:
s＝λ 1 ·B×M′+λ 2 ·K·F measure
wherein lambda is 1 And lambda (lambda) 2 Representing the weight ratio of an external threat information index system and an internal system information index system, wherein K is attack protection rate and F is measure Is attack detection rate;
calculating a system risk value r according to the following formula:
r=1-s. Further, the step S5 specifically includes: and carrying out evaluation operation on all the same type of evaluation objects by adopting different comprehensive security evaluation algorithms in the information security measurement model library, and analyzing to obtain an adaptive security evaluation model for the type of evaluation objects by utilizing a security evaluation model rationality verification method based on consistent head-to-tail sequence.
Further, the risk sustaining consistency RCC calculation formula of the evaluation object is as follows:
wherein X represents a residual risk value obtained by calculation after each safety evaluation is completed, and the residual risk value refers to an absolute difference value between the residual risk value obtained after a series of safety evaluation and an ideal risk value;
ideal value representing residual risk, the result interval of RCC is [0,1]。
The implementation of the invention has the following beneficial effects: aiming at the problems that the existing evaluation work faces the lack of scientific guidance and basis for constructing an evaluation index system in the process of demand analysis and index construction, the invention establishes a main body security target formalization mechanism based on mapping, and establishes a strategy and a reference mechanism for the security target decomposition and establishment of an evaluation object. Meanwhile, a security evaluation model rationality verification method based on consistent head-to-tail sequence is constructed, and a foundation is laid for selecting an adaptation model. The invention provides parameters such as risk continuous consistency, head-to-tail sequence consistency, amplitude reduction ratio, standard deviation and the like, which are used as judgment basis to provide support for selecting an adaptive model for an evaluation object, and is suitable for comparing various safety evaluation models, and selecting an optimal safety evaluation model for the evaluation object.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flow chart of a security evaluation method for a smart grid information system according to an embodiment of the present invention.
Fig. 2 is a schematic diagram of a security assessment framework based on model adaptation constructed in an embodiment of the present invention.
FIG. 3 is a schematic diagram of a mapping-based subject security target formalization mechanism in an embodiment of the invention.
Fig. 4 is a schematic diagram of a security evaluation index system of a smart grid information system constructed according to an embodiment of the present invention.
FIG. 5 is a schematic diagram of an information security comprehensive evaluation model based on multi-level decomposition feedback in an embodiment of the invention.
Fig. 6 is a schematic flow chart of a security evaluation model rationality verification method based on consistent head-to-tail sequence in the embodiment of the invention.
Fig. 7 is a schematic diagram of evaluation result distribution at different reduction ratios in the embodiment of the present invention.
Fig. 8 is a schematic diagram of ranking the evaluation results of three information security evaluation schemes in the embodiment of the invention.
Detailed Description
The following description of embodiments refers to the accompanying drawings, which illustrate specific embodiments in which the invention may be practiced.
Aiming at the problems of insufficient comparability of the existing safety evaluation method and model for the intelligent power grid information system, the invention provides the safety evaluation method for the intelligent power grid information system, which can be applied to actual evaluation work of the intelligent power grid information system. The mapping-based main body security target formalization mechanism provides a basis for constructing a system security evaluation index system; a security evaluation model rationality verification method based on consistent head-to-tail sequence lays a foundation for selecting an adaptation model.
Thus, referring to fig. 1, an embodiment of the present invention provides a security evaluation method for a smart grid information system, including:
s1, constructing a security evaluation framework based on model adaptation;
s2, determining a safety evaluation model of the intelligent power grid information system;
s3, collecting data of a security evaluation index system of an evaluation object;
s4, based on the information security measurement model library, comprehensive security evaluation algorithm is applied to carry out comprehensive measurement on the security risk and security of the information system;
and S5, obtaining an adaptive security evaluation model for the type of evaluation object based on a security evaluation model rationality verification method with consistent head-to-tail sequence.
Specifically, referring to fig. 2, in step S1, constructing a security evaluation framework based on model adaptation specifically includes: and determining a system security target formalization mechanism based on mapping, an adaptation information security measurement model and a security evaluation model based on consistent head-to-tail sequence for rationality verification.
The determining of the mapping-based system security target formalization mechanism is based on the premise and the basis in the security evaluation framework of model adaptation, and provides support for security target formalization of the evaluation object, as shown in fig. 3.
The formalization of the security target of the evaluation object oriented to the intelligent power grid information system is based on the following principle: aiming at the safety target and the safety environment of the evaluation main body, carrying out safety mechanism decomposition, determining the safety target, establishing a mapping table according to the rule guidance of the evaluation index system dividing of the information safety standard, classifying and mapping the evaluation index set, and thus constructing the information system safety evaluation index system.
As shown in fig. 4, in view of the ambiguous security target of the smart grid information system, the security target of the smart grid information system is formalized into four primary indexes and eleven secondary indexes according to the characteristics and the requirements of the evaluation object based on the main security target formalization mechanism of the mapping, and the smart grid information system security evaluation index system is formalized and constructed.
The four primary indicators include threat information, assets, vulnerability, and management. Eleven secondary metrics include system protection rate, attack detection rate, confidentiality, availability, controllability, recognizability, number of vulnerabilities, vulnerability hazard level, employee organization, enterprise level, and service continuity.
Step S2 determines a security evaluation model of the smart grid information system, wherein the calculation of the information security comprehensive evaluation model based on the multi-stage decomposition feedback is taken as an example, and the model is shown in fig. 5.
Firstly, analyzing the characteristics of an evaluation object, and strategically decomposing a safety target to form a safety evaluation index system.
Then, a corresponding information security evaluation measurement method is selected, and quantitative calculation is carried out on the comprehensive security score and the risk value (Security and Risk Score, S &) of the system.
Parameters such as Risk continuous consistency (Risk Continuous Consistency, RCC) and Residual Risk (RR) are utilized to guide a safety decision maker to construct a safety strengthening strategy, and the safety strengthening strategy is used as feedback to strengthen the safety of the intelligent power grid information system.
Further, the rationality verification method of the security evaluation model based on the consistency of the Head-to-tail sequences judges the rationality of the evaluation model by utilizing indexes such as Head-to-Tail Sequential Consistency (SC), reduction Ratio (DR) and the like.
Step S3, data acquisition of a safety evaluation index system of an evaluation object is carried out, and indexes related to an information safety comprehensive evaluation model based on multi-stage decomposition feedback are taken as an example.
(1) System protection rate calculation
The system protection rate K refers to the protection degree that can be achieved by analyzing the existing protection measures of the smart grid information system. It is calculated from the ratio of the number of security measures taken to the total number of protection measures currently known to be used, the calculation formula is as follows:
wherein, the liquid crystal display device comprises a liquid crystal display device,
representing the weight of the protective measures, g representing the protective measures taken, Ω representing a set of protective measures currently known to have been taken.
(2) Attack detection rate comprehensive index calculation
Attack detection rate comprehensive index F measure Is the comprehensive evaluation of attack detection. For the protection measures in the intelligent power grid information system, the attack detection system is an important component of the intelligent power grid security protection measures, and the point needs to be considered when the security score of the whole intelligent power grid information system is calculated.
Attack detection in the smart grid information system is regarded as two classifiers, and the decision results are divided into four types, as shown in table 1.
TABLE 1 attack detection decision results
Wherein, true Positive (TP) indicates that the system can correctly detect the attack state, and False Negative (FN) indicates that the system cannot correctly detect the attack state. False Positive (FP) indicates that the system wrongly judges the normal behavior as an attack behavior, and True Negative (TN) indicates that the system can correctly judge the non-attack behavior of the system.
Based on harmonic mean F measure The integrated score of (2) can be used to measure the quality of the result, and the calculation formula is as follows:
and scoring the mutual influence relationship among the 9 secondary indexes of the evaluation object by adopting an expert scoring method. Specifically, the 9 secondary indicators include confidentiality, availability, controllability, recognizability, vulnerability count, vulnerability hazard level, employee organization, enterprise level, and service continuity.
And (3) judging the safety states of the 9 secondary indexes by adopting an expert scoring method to obtain an expert scoring table, and further obtaining an evaluation membership matrix.
And S4, based on the information security measurement model library, carrying out comprehensive measurement on the security risk and the security of the information system by using a comprehensive security evaluation algorithm.
In the information security measurement model library, the existing and feasible various information security evaluation models are integrated, including a model based on association analysis, a model based on mathematical principles, a model based on attack graphs, a model based on game theory, a model based on analytic hierarchy process and the like.
And taking a certain intelligent power grid metering information system as a research object, carrying out information system safety evaluation by using 3 or more information safety evaluation models, and calculating to obtain a safety value and a system risk value of the information system.
Taking an improved comprehensive security evaluation algorithm (Improved Technique for Order Preference by Similarity to an Ideal Solution, IM-TOPSIS) based on ideal solution similarity ordering as an example, the calculation process is described.
The comprehensive safety evaluation algorithm comprises two stages of index right determination and comprehensive quantization.
(1) The index right is calculated according to a right determining method based on index relevance evaluation ranking.
The method for determining the weights based on index relevance evaluation sequencing utilizes the basic principle of a Markov chain and relevance among indexes, combines the idea of PageRank, determines the influence degree among the indexes according to the collective evaluation result of an expert, and finally reaches a stable state through continuous iterative calculation so as to determine the weight of the indexes. The index weight is determined by the algorithm, the relevance among indexes is considered, and the evaluation results of the plurality of experts are integrated, so that the effects of more evaluation people and more objective evaluation results are achieved, and the subjectivity problem of single expert evaluation is eliminated.
The weight calculation of the weight algorithm based on the index relevance evaluation ranking is based on the following assumption: if the other point-in weights received by one node are larger, the node is more important, meanwhile, the quality of the point-in nodes pointing to the node A is different, and the node with high quality transmits more weights to other nodes through links, so that the node with higher quality points to the node A, and the node A is more important.
The formula for calculating the PR value of each node is:
wherein, the liquid crystal display device comprises a liquid crystal display device,
is all p pairs i The nodes have a node set of outgoing links, +.>
Is node p j Number of outgoing chains, NThe total number of nodes, alpha is the probability that a user randomly arrives at one node, generally 0.85 is taken, PR values of each node can be calculated according to the above formula, and when iteration tends to be stable, the final result is set as index weight.
(2) And calculating a final security evaluation result by using an improved comprehensive security evaluation algorithm based on the ideal solution similarity ordering.
TOPSIS is a sort method that sorts different objects according to their distance from an ideal target, with two reference points: positive ideal solution and negative ideal solution. In the traditional method, the approach to the positive ideal solution and the separation from the negative ideal solution are the optimal solutions. The IM-TOPSIS security assessment algorithm sets five levels of ideal solutions according to five security levels of the information system and calculates the final result based on the distances from the five levels of ideal solutions. The specific evaluation steps are as follows:
(1) determining five security levels of an original data matrix and an optimal solution matrix
According to the evaluation index system, defining an evaluation index set as X= { X 1 ，x 2 ，...，x n And n represents the index number. The evaluation level set is defined as c= { C 1 ，c 2 ，...，c 5 High, medium, low).
The comprehensive safety evaluation algorithm evaluates the applicability of each index under each safety level by using a delfei survey method. The applicability evaluation rule is as follows: the evaluation value interval is [0,1]. The higher the fitness, the closer the evaluation value is to 1, the lower the fitness, and the closer the evaluation value is to 0.
The original evaluation membership matrix P can be obtained by a Delphi method n×5 The following are provided:
the optimal matrix is defined as a matrix belonging to a high security level, i.e. when each index reaches the optimal level, an evaluation membership matrix is formed, expressed as follows:
similarly, a suboptimal matrix is defined as a matrix belonging to a higher rank, i.e. when each index reaches a suboptimal rank, an evaluation membership matrix is formed, expressed as:
by analogy, the worst case matrix is defined as the matrix when the security level is low, i.e. when each index reaches the worst level, an evaluation membership matrix is formed as follows:
(2) calculating the evaluation scores of the indexes and the optimal solutions of five grades
Setting the proportion of the safety grade evaluation to u j (j＝1，2，3，4，5；u 1 ≠u 5 ) Constitutes a specific gravity set u= [ U ] 1 ，u 2 ，u 3 ，u 4 ，u 5 ]. The index weight set calculated by the ranking-based deterministic weight algorithm is: w= [ W ] 1 ，w 2 ，...，w n ]。
Order the
The set of evaluation scores obtained for each index is:
S＝W′×P×U T ＝[s 1 ，s 2 ，…，s n ] T
the high-level ideal solution is:
similarly, the higher-level ideal solution is:
with this, the low-level ideal solution is:
(3) calculating Euclidean distances from the evaluation object to five-level optimal solutions respectively
The embodiment of the invention calculates Euclidean distance from an evaluation object to five-level optimal solutions according to the following formula:
(4) normalizing five Euclidean distances to construct a weight matrix
First, by the calculation of (3), a vector Z is obtained:
finally, a normalized weight vector is obtained using a softmax function and b= [ B ] is obtained 1 ，b 2 ，...，b 5 ]Wherein, the method comprises the steps of, wherein,
calculating the safety value and the risk value of the evaluation object:
the security level and security value interval settings are shown in table 2.
Table 2 security level and security value interval setting table
| Security level | Low and low | Lower level | Medium and medium | Higher height | High height |
| Safety value interval | [0，0.2] | (0.2，0.4] | (0.4，0.6] | (0.6，0.8] | (0.8，1] |
The vector formed by the upper limit of the interval is
Therefore, the calculation formula of the Security Score(s) is as follows:
s＝λ 1 ·B×M′+λ 2 ·K·F measure
wherein lambda is 1 And lambda (lambda) 2 The weight ratio representing the external threat information indicator system and the internal system information indicator system is generally set as lambda 1 ＝λ 2 =0.5. K is attack protection rate, F measure Is the attack detection rate.
The calculation formula of the Risk value (Risk Score, r) is as follows:
r＝1-s
where s represents the total calculated security value of the information system.
And step S5, based on a security evaluation model rationality verification method with consistent head-to-tail sequence, obtaining an adaptive security evaluation model for the type of evaluation object.
And carrying out evaluation operation on m evaluation objects of the same type by adopting different comprehensive security evaluation algorithms in an information security measurement model library, and analyzing to obtain an adaptive security evaluation model for the evaluation objects of the same type by utilizing a security evaluation model rationality verification method based on consistent head-to-tail sequence. The flow chart of the method is shown in fig. 6.
In the security evaluation model rationality verification method based on head-to-tail consistency, parameters such as risk continuous consistency, head-to-tail sequence consistency, amplitude reduction ratio, standard deviation and the like are provided as judgment basis, and support is provided for selecting an adaptation model for an evaluation object.
The safety evaluation based on the multistage decomposition feedback safety evaluation model can judge the stability of the safety evaluation object according to the continuous consistency of the residual risks. The residual risk is the absolute difference between the residual risk value obtained after a series of safety evaluations and the ideal risk value. The system security evaluation is in units of times. After multiple evaluations, the evaluation target risk continuous consistency (Risk Continuous Consistency, RCC) was calculated as follows:
wherein X represents a residual risk value obtained by calculation after each safety evaluation is completed;
an ideal value representing the residual risk is typically set to 0. The result interval of RCC is [0,1]The closer the result is to 0, the better the representative state. And drawing a curve of the continuous consistency of risks to obtain the closeness trend between the evaluation target and the ideal state.
Assuming that m evaluation objects exist, different evaluation methods are used to obtain a ranking of results. Then, the number n of common agreement in the optimal 40% object is found by descending order h ToAnd the worst number of common agreement n in 40% of the objects f The Head-to-Tail Consistency (HC) is calculated as follows:
obviously, the closer the result of the above formula is to 1, the better the effect.
Since the order of the entities appearing in the front and rear 40% at the same time may be arbitrarily arranged, the order consistency thereof is further examined. The Head-to-Tail Sequential Consistency (SC) consistency is calculated as follows:
wherein d i The distance difference of the same entity in the two arrangements is represented, the maximum value is m-1, the minimum value is 0, and m is the total number of the entities to be evaluated. The sorting consistency test result can be obtained by combining the two formulas, and the calculation formula is as follows:
C rank ＝λHC+(1-λ)SC
wherein λ is set to 0.8.
For a Decline Ratio (DR) index, if there are m evaluation objects, the m evaluation objects are arranged in descending order according to the level of the score f. Then, the function f (N) on the sorting object number N is a monotonically decreasing function. The best object coordinate of the evaluation result is (1, f (1)), and the worst object coordinate of the evaluation result is (m, f (m)). Therefore, the formula for calculating the amplitude-reduction ratio is as follows:
in order to eliminate the influence of different dimension values on the reduction ratio result, the evaluation score was standardized. Let [0, m ] be the set of processed fractional intervals, where (1, m) and (m, 0) represent the maximum point coordinates and the minimum point coordinates, respectively. The normalization formula is as follows:
wherein V 'is' i Represents the evaluation result before normalization, V i The normalized evaluation results are shown. Therefore, the normalized step down ratio calculation formula is as follows:
the amplitude reduction ratio is the ratio of the sum of Euclidean distances of two adjacent points in the evaluation result to the distance between the first point and the last point. Fig. 7 shows a comparison between a high reduction ratio and a low reduction ratio. If the value is relatively large, the dispersion distribution is star-shaped in the figure. This will result in a partial set of spaced points, some of which are scattered. Further, in the concentrated value range, the evaluation result is blurred. If the value is close to 1, this means that all data show a consistent downward trend, while representing that the evaluation model has better performance.
The standard deviation (σ) is an index of the degree of dispersion of each object in the statistical data set, and the arithmetic square root of the square of the distance between all data in the sample and the average of the sample is taken as a calculation formula, specifically, the calculation formula is as follows:
wherein, the liquid crystal display device comprises a liquid crystal display device,
represents the average number of sample objects and n represents the total number of samples.
Specific index data processing and experimental results will be described next.
(1) Attack protection rate and attack detection rate
The system protection data and attack detection data are collected for the evaluation object, and the actual protection measure data are shown in table 3 through objective data and delta analysis, and the attack protection rate k=0.6 can be calculated according to the content of table 3.
Table 3 information system protection measures inventory
1000 simulated attack tests were performed on the system protection IDS, and the determination results are shown in table 4.
TABLE 4 attack detection decision results
F can be calculated based on the formula measure ＝0.91。
(2) Index weight calculation
In this section, the weights of the 9 secondary indexes of the evaluation object are calculated according to a ranking-based deterministic weight algorithm. Specifically, the 9 secondary indicators include confidentiality, availability, controllability, recognizability, vulnerability count, vulnerability hazard level, employee organization, enterprise level, and service continuity. Based on the scores of the 20 experts, an expert scoring table was constructed as shown in table 5.
TABLE 5 expert scoring of impact relationship between secondary indicators
Thus, the first and second substrates are bonded together,the adjacency matrix Q constructed is:
the probability transition matrix H is:
the final probability transition matrix G is calculated by the following equation:
where U is an n×n-order all-1 matrix, and N represents the number of index nodes.
According to the final transfer matrix G, calculating weight vector P after repeated iteration n The calculation formula is as follows:
P n+1 ＝G·P n
wherein P is n Is a column vector consisting of probability values after the nth iteration.
Final P n The values form a weight vector W of each secondary index, and W= [0.151,0.146,0.158,0.150,0.064,0.053,0.071,0.049,0.158 ] is calculated]。
(3) Calculating comprehensive evaluation results of information system
In this section, based on 9 secondary metrics, the security level and security score of the information system are comprehensively calculated according to the IM-TOPSIS security level metric algorithm. The expert scoring table is shown in Table 6, and the evaluation membership matrix P is obtained according to the expert scoring result 9×5 。
Table 6 expert scoring Table
Thus, the membership matrix is evaluated as
The subjective weight of the different security levels determined by the delta method is u= [0.07,0.13,0.2,0.27,0.33]. Then, euclidean distances between the results of the evaluation objects and the five-level ideal solutions are calculated. The euclidean distance is normalized to obtain a membership vector b= [0.187,0.197,0.208,0.176,0.231]. Finally, the calculated information system security value is s=0.580.
(4) Metric index
According to the calculation, the information system security risk score is obtained as follows: r=1-s=0.420. Thus, the evaluation object has a medium risk.
In order to better verify the validity of the constructed framework, an IM-TOPSIS safety comprehensive evaluation algorithm, a fuzzy comprehensive evaluation algorithm and a subjective weighting method are adopted for comprehensively comparing a certain intelligent power grid information system. The subjective weighting method is also called as Delphi method, and is an evaluation method reflecting subjective ideas. And the evaluation flow is simple, the calculation process is simple and convenient, and the application range is wide. The evaluator is composed of experts in the evaluation object field, and according to the knowledge accumulation and actual experience in the professional field, the evaluator assigns corresponding weight to each index, and according to the input data, the evaluator multiplies the weight matrix to obtain the security evaluation result of the information system.
And taking the intelligent power grid information systems in ten different areas as evaluation objects, carrying out security evaluation work on the evaluation objects, and analyzing the evaluation results. The security scores and ranking comparison results obtained by the three information system security evaluation methods are shown in table 7.
TABLE 7 comparison of safety values and rank-ordered evaluation results
The ranking of the results obtained by the evaluation by the three information security evaluation methods is shown in fig. 8.
The three information security evaluation methods use consistent scales, and for simple calculation of the reduction ratio index, the evaluation score is converted into [0, 10 ]]Calculating the consistency SC of the head-to-tail sequence of each evaluation object and the final consistency test result C of the sequencing rank The reduction ratio DR and standard deviation σ, and the results are shown in table 8.
Table 8 comparison of evaluation results such as the amplitude reduction ratio
The calculation results show that the sequencing consistency test result of the three methods is 0.693, which shows that the obtained results have consistency when different evaluation methods are adopted for evaluation. Meanwhile, the amplitude reduction ratio of the IM-TOPSIS evaluation algorithm model is relatively low and is closer to 1. Therefore, the model provided by the embodiment of the invention has better effectiveness.
The decreasing amplitude ratio and standard deviation index represent the dispersion degree of the evaluation result, wherein the decreasing amplitude ratio means the ratio of the sum of the adjacent point distances of the decreasing order scatter diagram to the distance of the extreme point, and generally, the closer the value is to 1, the more stable the decreasing order arrangement of the evaluation result is; the more this value exceeds 1, the more the partial region of the descending order scatter diagram may be in the case of a blurred ordering result due to an error caused by the evaluation result. Standard deviation represents the degree of dispersion between the sample data and the average. The standard deviation can represent the dispersibility of the data set, but cannot represent the local dense condition possibly occurring in the data set; the amplitude-reduction ratio may indicate whether the data set is distributed over various value ranges, but may not distinguish between the data set. Therefore, when the sequencing consistency test result reaches the standard, the stability of the evaluation model can be analyzed and judged by combining the two indexes of the amplitude reduction ratio and the standard deviation.
As can be seen from the above description, compared with the prior art, the invention has the following beneficial effects: aiming at the problems that the existing evaluation work faces the lack of scientific guidance and basis for constructing an evaluation index system in the process of demand analysis and index construction, the invention establishes a main body security target formalization mechanism based on mapping, and establishes a strategy and a reference mechanism for the security target decomposition and establishment of an evaluation object. Meanwhile, a security evaluation model rationality verification method based on consistent head-to-tail sequence is constructed, and a foundation is laid for selecting an adaptation model. The invention provides parameters such as risk continuous consistency, head-to-tail sequence consistency, amplitude reduction ratio, standard deviation and the like, which are used as judgment basis to provide support for selecting an adaptive model for an evaluation object, and is suitable for comparing various safety evaluation models, and selecting an optimal safety evaluation model for the evaluation object.
The foregoing disclosure is illustrative of the present invention and is not to be construed as limiting the scope of the invention, which is defined by the appended claims.
Claims (10)
1. The security evaluation method for the intelligent power grid information system is characterized by comprising the following steps of:
s1, constructing a security evaluation framework based on model adaptation;
s2, determining a safety evaluation model of the intelligent power grid information system;
s3, collecting data of a security evaluation index system of an evaluation object;
s4, based on the information security measurement model library, comprehensive security evaluation algorithm is applied to carry out comprehensive measurement on the security risk and security of the information system;
and S5, obtaining an adaptive security evaluation model for the type of evaluation object based on a security evaluation model rationality verification method with consistent head-to-tail sequence.
2. The method according to claim 1, wherein in the step S1, constructing a security assessment framework based on model adaptation specifically includes: and determining a system security target formalization mechanism based on mapping, an adaptation information security measurement model and a security evaluation model based on consistent head-to-tail sequence for rationality verification.
3. The method according to claim 2, wherein a mapping-based system security target formalization mechanism is determined, specifically, the security target of the intelligent power grid information system is formalized into four primary indexes and eleven secondary indexes, and a intelligent power grid information system security evaluation index system is formalized and constructed; the four primary indexes comprise threat information, assets, vulnerability and management; eleven secondary metrics include system protection rate, attack detection rate, confidentiality, availability, controllability, recognizability, number of vulnerabilities, vulnerability hazard level, employee organization, enterprise level, and service continuity.
4. The method according to claim 1, wherein the step S2 specifically comprises:
analyzing the characteristics of the evaluation object to strategically decompose the safety target to form a safety evaluation index system;
selecting a corresponding information security evaluation measurement method, and quantitatively calculating a comprehensive security score and a risk value of the system;
the safety of the intelligent power grid information system is enhanced by taking the risk continuous consistency and the residual risk as feedback;
the reasonability verification method of the safety evaluation model based on the consistency of the head-to-tail sequences judges the reasonability of the evaluation model by using the consistency of the head-to-tail sequences and the amplitude reduction ratio.
5. The method according to claim 1, wherein in the step S3, an expert scoring method is used to score the correlation between the 9 secondary indexes of the evaluation object; the 9 secondary indexes comprise confidentiality, availability, controllability, identifiability, vulnerability quantity, vulnerability hazard level, employee organization, enterprise level and service continuity; and (3) judging the safety states of the 9 secondary indexes by adopting an expert scoring method to obtain an expert scoring table, and further obtaining an evaluation membership matrix.
6. The method according to claim 1, wherein in the step S4, the information security metric model library integrates a plurality of information security evaluation models, including a model based on association analysis, a model based on mathematical principles, a model based on attack graphs, a model based on game theory, and a model based on analytic hierarchy process; and carrying out safety evaluation of the information system by using 3 or more information safety evaluation models, and calculating to obtain a safety value and a system risk value of the information system.
7. The method according to claim 6, characterized in that the calculation of the security value and the system risk value of the information system is performed by means of a comprehensive security evaluation method based on an improved ideal solution similarity ranking, comprising two phases of index validation and comprehensive quantification.
8. The method of claim 7, wherein the computing of the security value and the system risk value for the information system using the improved comprehensive security assessment method based on the ideal solution similarity ranking, comprises:
setting five levels of ideal solutions according to five security levels of an information system, and determining five security levels of an original data matrix and an optimal solution matrix;
calculating the evaluation scores of the indexes and the optimal solutions of the five grades;
calculating Euclidean distances from the evaluation object to the five-level optimal solutions respectively;
normalizing the five euclidean distances to construct a weight matrix;
the security value s of the information system is calculated according to the following formula:
s＝λ 1 ·×M ′ + 2 ·· measure
wherein lambda is 1 And lambda (lambda) 2 The weight ratio of the external threat information index system and the internal system information index system is representedImpact protection rate, F measure Is attack detection rate;
calculating a system risk value r according to the following formula:
r＝1-s。
9. the method according to claim 8, wherein the step S5 specifically includes: and carrying out evaluation operation on all the same type of evaluation objects by adopting different comprehensive security evaluation algorithms in the information security measurement model library, and analyzing to obtain an adaptive security evaluation model for the type of evaluation objects by utilizing a security evaluation model rationality verification method based on consistent head-to-tail sequence.
10. The method of claim 9, wherein the risk sustaining consistency RCC calculation formula for the subject is as follows:
wherein X represents a residual risk value obtained by calculation after each safety evaluation is completed, and the residual risk value refers to an absolute difference value between the residual risk value obtained after a series of safety evaluation and an ideal risk value;
ideal value representing residual risk, the result interval of RCC is [0,1]。
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