CN106230773A - Risk evaluating system based on fuzzy matrix analytic hierarchy process (AHP) - Google Patents

Abstract

The invention discloses risk evaluating system based on fuzzy matrix analytic hierarchy process (AHP), building module, fuzzy overall evaluation result computing module and risk evaluation module including evaluation criteria system generation module, opinion rating system generation module, quantification of targets module, index weights computing module, subordinated-degree matrix, wherein risk evaluation module includes risk profiles assessment submodule, risk analysis submodule and risk control submodule.Present invention risk evaluating system based on fuzzy matrix analytic hierarchy process (AHP), the application in risk assessment by fuzzy matrix and analytic hierarchy process (AHP), the various uncertain factors, the index that occur during risk assessment can be analyzed.

Description

Risk evaluating system based on fuzzy matrix analytic hierarchy process (AHP)

Technical field

The present invention relates to technical field of network security, be specifically related to risk assessment system based on fuzzy matrix analytic hierarchy process (AHP) System.

Background technology

In correlation technique, in the risk assessment processes of network security, there are the character of a lot of influence factor and the activity cannot Carrying out quantitative description by numeral, result cannot judge by single criterion, therefore at many employing fuzzy mathematics methods Reason.American scholar L.A.Zadeth proposes the concept of fuzzy set first in nineteen sixty-five, and fuzzy behavior and activity are set up model. Fuzzy mathematics is transferred to continuous logic on the basis of two-valued function, and absolute "Yes" and " non-" are become the most flexibly Thing, goes to process blooming with strict mathematical method.

Additionally, analytic hierarchy process (AHP) (Analytic Hierarchy Process is called for short AHP) is also conventional means, it is A kind of simplicity that the eighties in 20th century is proposed by U.S. operational research professor T.L.Satty, flexible and practical multiple criteria decision making (MCDM) Method, PROBLEM DECOMPOSITION is different compositing factors according to the character of problem and target to be reached by it, and according to factor between Interrelated impact and membership are pressed different levels and are assembled combination, form a multi-level analytical structure model, Then pressing layer analysis, final acquisition lowermost layer factor is for the importance weight of top (general objective).

In correlation technique, in the risk assessment of network security, conventional Forecasting Methodology mainly has:

Expert prediction method, refers to that being formed expert group by multiple experts carries out a kind of method of risk profile, can be divided into two kinds Mode: one is that tissue relevant expert investigates, and then the conclusion drawing prediction is discussed by the way of forum；Two are Delphi approach, when transporting in this way, by coordinator with note formats, sends problem table to relevant expert, it is desirable to expert is to asking Problem listed by topic table is clearly answered, after the test paper coordinated person induction-arrangement of results and analysis, then by result with letters Form be sent to expert.But, expert opinion may not necessarily reflect objective reality, and the responsibility of each expert is more disperseed, and estimates The flexible strategy of meter should not determine, therefore, is normally only applicable to the prediction of total value, and reliable when region, customer base, product category Property is poor.

LEC risk assessment method, full name operation risk analysis method, is a kind of dangerous quantitative calculation method.According to dangerous matter sources Identification record, the risk that each dangerous matter sources of quantitative Analysis is brought, determine and do Risks, list inventory and issue, planned Control risk.The computational methods used: D=L × E × C, wherein: D is value-at-risk, L is the probability size having an accident, E For being exposed to the frequent degree of hazardous environment, C is the consequence produced that has an accident.But, LEC risk assessment method, to danger The division of grade, is by virtue of experience to judge to a certain extent, has its limitation, and the local that it is a kind of operation is commented during application Valency, therefore can not generally be suitable for.

Summary of the invention

For the problems referred to above, the present invention provides risk evaluating system based on fuzzy matrix analytic hierarchy process (AHP).

The purpose of the present invention realizes by the following technical solutions:

Risk evaluating system based on fuzzy matrix analytic hierarchy process (AHP), including:

(1) evaluation criteria system generation module, for generating the evaluation criteria system being directed to dangerous matter sources, described evaluation refers to Mark system is formulated by the expert group evaluating dangerous matter sources, and it includes that destination layer, rule layer and indicator layer, described destination layer are defined as treating The dangerous matter sources of assessment, described rule layer include probability that dangerous matter sources causes danger, dangerous matter sources cause danger after influence degree and Three female indexs of uncontrollability, described indicator layer includes the every sub-index corresponding to female index, wherein considers that system is taked Safety measure or strategy are to the abatement of risk and control action, and definition uncontrollability is to make strategy fails after dangerous matter sources is caused danger Ability characteristics；

(2) opinion rating system generation module, for generating the opinion rating system corresponding to evaluation criteria system, described Opinion rating system is formulated by the expert group evaluating dangerous matter sources, and it includes multiple opinion rating, each opinion rating correspondence one Individual grade fuzzy subset；

(3) quantification of targets module, for gathering described every sub-index and according to the impact on corresponding female index of the sub-index Degree quantifies；

(4) index weights computing module, calculates female index and the weight vectors of sub-index for reference level fractional analysis；

(5) subordinated-degree matrix builds module, for according to described evaluation criteria system, calculates dangerous matter sources respectively to described etc. The degree of membership of level fuzzy subset, builds the subordinated-degree matrix of female index；

(6) fuzzy overall evaluation result computing module, for according to subordinated-degree matrix and weight vector computation fuzzy synthesis Evaluation result；

(7) risk evaluation module, including risk profiles assessment submodule, risk analysis submodule and risk control submodule Block, described risk profiles assessment submodule for calculating the wind of destination layer according to weight vectors and fuzzy overall evaluation result vector Danger degree；Described risk analysis submodule, is analyzed for the risk assessment situation obtaining risk evaluation module, finds risk Node that propagation path, Risk of Communication ability are stronger and easy infected node also export analysis result；Described risk control Module, for adjusting the access control policy of network and susceptible node security strategy according to described analysis result, strengthens being felt The safety management of dye node.

Preferably, described opinion rating includes probability opinion rating, severity ratings grade；Described probability evaluation etc. It is extremely impossible, rarely possible, possible, quite possible, entirely possible that level includes；Described severity ratings grade include negligible, Slight, serious, dangerous, disaster.

Preferably, in the described every sub-index corresponding to female index, corresponding to the son of the probability that dangerous matter sources is caused danger Index includes threatening motivation, attacking ability, attack complexity, vulnerability exploit rate and assets captivation, occurs corresponding to dangerous matter sources The sub-index of the influence degree after danger includes confidentiality impact, integrity impact, availability impact and relatedness impact, corresponding Sub-index in uncontrollability includes disguised power of test, multiformity power of test and prominent anti-defensive ability/resistance ability.

Wherein, specifically perform during the running of described quantification of targets module:

If P, D, C represent respectively probability that dangerous matter sources causes danger, dangerous matter sources cause danger after influence degree and can not Control property, the expert group evaluating dangerous matter sources carries out, to the quantized value of the sub-index of x-th of female index y, the quantized value that n evaluation obtains Collection isThe final quantization value of the sub-index of x-th of female index y is:

${I_x^y}^{\′}=\frac{{\mathrm{\Σ}}_i^n{I}_{xi}^y}{n},{I_x^y}^{\′}\∈\[0,1\]$

Wherein, described subordinated-degree matrix builds module when calculating dangerous matter sources to the degree of membership of described grade fuzzy subset, tool Body operates below performing:

Definition grade fuzzy subset is { v_j, j=1,2 ..., 5}, and define the influence degree equity for describing female index The membership function of the degree of membership of level fuzzy subset:

Wherein, ρ is the final quantization of the sub-index of x-th of female index y determined by the expert group expert evaluating dangerous matter sources Value,For grade fuzzy subset { v_j, j=1,2 ..., standard value corresponding for 5},μ is to evaluate dangerous matter sources Expert group's certainty factor to described final quantization value；

According to described membership function, construct P, the subordinated-degree matrix R of tri-female indexs of D, C respectively_P,R_D,R_C:

$R_P=\left\{\begin{array}{cccc}{f}_{v_1}\left({I_1^P}^{\′}\right)& f_{v_2}\left({I_1^P}^{\′}\right)& \mathrm{...}& f_{v_5}\left({I_1^P}^{\′}\right)\\ f_{v_1}\left({I_2^P}^{\′}\right)& f_{v_2}\left({I_2^P}^{\′}\right)& \mathrm{...}& f_{v_5}\left({I_2^P}^{\′}\right)\\ .& .& & .\\ .& .& & .\\ .& .& & .\\ f_{v_1}\left({I_{N_P}^P}^{\′}\right)& f_{v_2}\left({I_{N_P}^P}^{\′}\right)& \mathrm{...}& f_{v_5}\left({I_{N_P}^P}^{\′}\right)\end{array}\right.$

$R_D=\left\{\begin{array}{cccc}{f}_{v_1}\left({I_1^D}^{\′}\right)& f_{v_2}\left({I_1^D}^{\′}\right)& \mathrm{...}& f_{v_5}\left({I_1^D}^{\′}\right)\\ f_{v_1}\left({I_2^D}^{\′}\right)& f_{v_2}\left({I_2^D}^{\′}\right)& \mathrm{...}& f_{v_5}\left({I_2^D}^{\′}\right)\\ .& .& & .\\ .& .& & .\\ .& .& & .\\ f_{v_1}\left({I_{N_D}^D}^{\′}\right)& f_{v_2}\left({I_{N_D}^D}^{\′}\right)& \mathrm{...}& f_{v_5}\left({I_{N_D}^D}^{\′}\right)\end{array}\right.$

$R_C=\left\{\begin{array}{cccc}{f}_{v_1}\left({I_1^C}^{\′}\right)& f_{v_2}\left({I_1^C}^{\′}\right)& \mathrm{...}& f_{v_5}\left({I_1^C}^{\′}\right)\\ f_{v_1}\left({I_2^C}^{\′}\right)& f_{v_2}\left({I_2^C}^{\′}\right)& \mathrm{...}& f_{v_5}\left({I_2^C}^{\′}\right)\\ .& .& & .\\ .& .& & .\\ .& .& & .\\ f_{v_1}\left({I_{N_C}^C}^{\′}\right)& f_{v_2}\left({I_{N_C}^C}^{\′}\right)& \mathrm{...}& f_{v_5}\left({I_{N_C}^C}^{\′}\right)\end{array}\right.$

Wherein, N_PRepresent the sub-index number that female index P comprises, N_DRepresent the sub-index number that female index D comprises, N_CTable Show the sub-index number that female index C comprises；

Wherein, the computing formula of described fuzzy overall evaluation result computing module calculating fuzzy overall evaluation result M is:

$M=W*\left(\begin{array}{c}{m}_P*R_P\\ m_D*R_D\\ m_C*R_C\end{array}\right)=(L_1,L_2,...,L_5)$

Wherein, if the weight fuzzy subset corresponding to female index P obtained according to weight vectors, D, C is W={ w_P,w_D, w_C, the weight fuzzy subset corresponding to sub-index set under female index P of obtaining according to weight vectors, D, C is respectively m_P、m_D、 m_C, * represents that generalized fuzzy synthesizes computing；

Wherein, when calculating described risk, if grade corresponding to grade fuzzy subset is entered as { H_j, j=1,2 .., 5}, I.e. grade v_jCorresponding numerical value H_j, and grade v_jFrom low paramount time described H_jValue is incremented by, and the computing formula of described risk FD is: $\mathrm{FD}=\frac{{\mathrm{\Σ}}_{j=1}^5{H}_j\×L_j}{{\mathrm{\Σ}}_{j=1}^5{L}_j}.$

The invention have the benefit that

(1) risk evaluating system based on fuzzy matrix analytic hierarchy process (AHP), is existed by fuzzy matrix and analytic hierarchy process (AHP) Application in risk assessment, can be analyzed the various uncertain factors, the index that occur during risk assessment；

(2) membership function of the degree of membership of influence degree In Grade fuzzy subset for describing female index is defined, and Utilize described membership function to carry out subordinated-degree matrix structure, calculated degree of membership Normal Distribution, more conformed to reality, kept away Exempt from the impact of artificial subjective factor, enhance the objectivity of assessment result；

(3) proposing the computing formula of fuzzy overall evaluation result and risk, this computing formula is examined the most all sidedly Consider the factor affecting risk, highlighted the impact of safety measure Usefulness Pair systematic risk degree, it is achieved that the thing to dangerous matter sources Later evaluation.

Accompanying drawing explanation

The invention will be further described to utilize accompanying drawing, but the embodiment in accompanying drawing does not constitute any limit to the present invention System, for those of ordinary skill in the art, on the premise of not paying creative work, it is also possible to obtain according to the following drawings Other accompanying drawing.

Fig. 1 is the connection diagram of each module of the present invention；

Fig. 2 is the structural representation of risk evaluation module of the present invention.

Reference:

Evaluation criteria system generation module 1, opinion rating system generation module 2, quantification of targets module 3, index weights meter Calculate module 4, subordinated-degree matrix builds module 5, fuzzy overall evaluation result computing module 6, risk evaluation module 7, risk profiles Assessment submodule 71, risk analysis submodule 72, risk control submodule 73.

Detailed description of the invention

The invention will be further described with the following Examples.

Embodiment 1

See Fig. 1, Fig. 2, the present embodiment risk evaluating system based on fuzzy matrix analytic hierarchy process (AHP), including:

(1) evaluation criteria system generation module 1, for generating the evaluation criteria system being directed to dangerous matter sources, described evaluation Index system is formulated by the expert group evaluating dangerous matter sources, and it includes that destination layer, rule layer and indicator layer, described destination layer are defined as Dangerous matter sources to be assessed, described rule layer include probability that dangerous matter sources causes danger, dangerous matter sources cause danger after influence degree With three female indexs of uncontrollability, described indicator layer includes the every sub-index corresponding to female index, wherein considers that system is adopted Taking safety measure or strategy to the abatement of risk and control action, definition uncontrollability is to make strategy mistake after dangerous matter sources is caused danger The ability characteristics of effect；

(2) opinion rating system generation module 2, for generating the opinion rating system corresponding to evaluation criteria system, institute Commentary valency hierarchical system is formulated by the expert group evaluating dangerous matter sources, and it includes multiple opinion rating, and each opinion rating is corresponding One grade fuzzy subset；

(3) quantification of targets module 3, for gathering described every sub-index and according to the impact on corresponding female index of the sub-index Degree quantifies；

(4) index weights computing module 4, calculates female index and the weight vectors of sub-index for reference level fractional analysis；

(5) subordinated-degree matrix builds module 5, for according to described evaluation criteria system, calculates dangerous matter sources respectively to described The degree of membership of grade fuzzy subset, builds the subordinated-degree matrix of female index；

(6) fuzzy overall evaluation result computing module 6, for according to subordinated-degree matrix and weight vector computation fuzzy synthesis Evaluation result；

(7) risk evaluation module 7, including risk profiles assessment submodule 71, risk analysis submodule 72 and risk control Submodule 73, described risk profiles assessment submodule 71 is for calculating mesh according to weight vectors and fuzzy overall evaluation result vector The risk of mark layer；Described risk analysis submodule 72, is carried out point for the risk assessment situation that obtains risk evaluation module Analysis, finds node that Risk of Communication path, Risk of Communication ability are stronger and easy infected node and exports analysis result；Described Risk control submodule 73, for adjusting the access control policy of network and susceptible node security plan according to described analysis result Slightly, the safety management of the most infected node is strengthened.

The present embodiment risk evaluating system based on fuzzy matrix analytic hierarchy process (AHP), by fuzzy matrix and step analysis Method application in risk assessment, can be analyzed the various uncertain factors, the index that occur during risk assessment.

Embodiment 2

See Fig. 1, Fig. 2, the present embodiment risk evaluating system based on fuzzy matrix analytic hierarchy process (AHP), including:

(1) evaluation criteria system generation module 1, for generating the evaluation criteria system being directed to dangerous matter sources, described evaluation Index system is formulated by the expert group evaluating dangerous matter sources, and it includes that destination layer, rule layer and indicator layer, described destination layer are defined as Dangerous matter sources to be assessed, described rule layer include probability that dangerous matter sources causes danger, dangerous matter sources cause danger after influence degree With three female indexs of uncontrollability, described indicator layer includes the every sub-index corresponding to female index, wherein considers that system is adopted Taking safety measure or strategy to the abatement of risk and control action, definition uncontrollability is to make strategy mistake after dangerous matter sources is caused danger The ability characteristics of effect；

(2) opinion rating system generation module 2, for generating the opinion rating system corresponding to evaluation criteria system, institute Commentary valency hierarchical system is formulated by the expert group evaluating dangerous matter sources, and it includes multiple opinion rating, and each opinion rating is corresponding One grade fuzzy subset；

(3) quantification of targets module 3, for gathering described every sub-index and according to the impact on corresponding female index of the sub-index Degree quantifies, and specifically performs during the running of described quantification of targets module:

If P, D, C represent respectively probability that dangerous matter sources causes danger, dangerous matter sources cause danger after influence degree and can not Control property, the expert group evaluating dangerous matter sources carries out, to the quantized value of the sub-index of x-th of female index y, the quantized value that n evaluation obtains Collection isThe final quantization value of the sub-index of x-th of female index y is:

${I_x^y}^{\′}=\frac{{\mathrm{\Σ}}_i^n{I}_{xi}^y}{n},{I_x^y}^{\′}\∈\[0,1\]$

(4) index weights computing module 4, calculates female index and the weight vectors of sub-index for reference level fractional analysis；

(5) subordinated-degree matrix builds module 5, for according to described evaluation criteria system, calculates dangerous matter sources respectively to described The degree of membership of grade fuzzy subset, builds the subordinated-degree matrix of female index, and wherein, described subordinated-degree matrix builds module 5 and calculates When dangerous matter sources is to the degree of membership of described grade fuzzy subset, concrete perform following operation:

Definition grade fuzzy subset is { v_j, j=1,2 ..., 5}, and define the influence degree equity for describing female index The membership function of the degree of membership of level fuzzy subset:

Wherein, ρ is the final quantization of the sub-index of x-th of female index y determined by the expert group expert evaluating dangerous matter sources Value,For grade fuzzy subset { v_j, j=1,2 ..., 5 } corresponding standard value,μ is to evaluate dangerous matter sources Expert group's certainty factor to described final quantization value；

According to described membership function, construct P, the subordinated-degree matrix R of tri-female indexs of D, C respectively_P,R_D,R_C:

$R_P=\left\{\begin{array}{cccc}{f}_{v_1}\left({I_1^P}^{\′}\right)& f_{v_2}\left({I_1^P}^{\′}\right)& \mathrm{...}& f_{v_5}\left({I_1^P}^{\′}\right)\\ f_{v_1}\left({I_2^P}^{\′}\right)& f_{v_2}\left({I_2^P}^{\′}\right)& \mathrm{...}& f_{v_5}\left({I_2^P}^{\′}\right)\\ .& .& & .\\ .& .& & .\\ .& .& & .\\ f_{v_1}\left({I_{N_P}^P}^{\′}\right)& f_{v_2}\left({I_{N_P}^P}^{\′}\right)& \mathrm{...}& f_{v_5}\left({I_{N_P}^P}^{\′}\right)\end{array}\right.$

$R_D=\left\{\begin{array}{cccc}{f}_{v_1}\left({I_1^D}^{\′}\right)& f_{v_2}\left({I_1^D}^{\′}\right)& \mathrm{...}& f_{v_5}\left({I_1^D}^{\′}\right)\\ f_{v_1}\left({I_2^D}^{\′}\right)& f_{v_2}\left({I_2^D}^{\′}\right)& \mathrm{...}& f_{v_5}\left({I_2^D}^{\′}\right)\\ .& .& & .\\ .& .& & .\\ .& .& & .\\ f_{v_1}\left({I_{N_D}^D}^{\′}\right)& f_{v_2}\left({I_{N_D}^D}^{\′}\right)& \mathrm{...}& f_{v_5}\left({I_{N_D}^D}^{\′}\right)\end{array}\right.$

$R_C=\left\{\begin{array}{cccc}{f}_{v_1}\left({I_1^C}^{\′}\right)& f_{v_2}\left({I_1^C}^{\′}\right)& \mathrm{...}& f_{v_5}\left({I_1^C}^{\′}\right)\\ f_{v_1}\left({I_2^C}^{\′}\right)& f_{v_2}\left({I_2^C}^{\′}\right)& \mathrm{...}& f_{v_5}\left({I_2^C}^{\′}\right)\\ .& .& & .\\ .& .& & .\\ .& .& & .\\ f_{v_1}\left({I_{N_C}^C}^{\′}\right)& f_{v_2}\left({I_{N_C}^C}^{\′}\right)& \mathrm{...}& f_{v_5}\left({I_{N_C}^C}^{\′}\right)\end{array}\right.$

Wherein, N_PRepresent the sub-index number that female index P comprises, N_DRepresent the sub-index number that female index D comprises, N_CTable Show the sub-index number that female index C comprises；

(6) fuzzy overall evaluation result computing module 6, for according to subordinated-degree matrix and weight vector computation fuzzy synthesis Evaluation result；

(7) risk evaluation module 7, including risk profiles assessment submodule 71, risk analysis submodule 72 and risk control Submodule 73, described risk profiles assessment submodule 71 is for calculating mesh according to weight vectors and fuzzy overall evaluation result vector The risk of mark layer；Described risk analysis submodule 72, is carried out point for the risk assessment situation that obtains risk evaluation module Analysis, finds node that Risk of Communication path, Risk of Communication ability are stronger and easy infected node and exports analysis result；Described Risk control submodule 73, for adjusting the access control policy of network and susceptible node security plan according to described analysis result Slightly, the safety management of the most infected node is strengthened.

What the present embodiment defined the degree of membership of the influence degree In Grade fuzzy subset for describing female index is subordinate to letter Number, and utilize described membership function to carry out subordinated-degree matrix structure, calculate degree of membership Normal Distribution, more conform to reality Border, it is to avoid the impact of artificial subjective factor, enhances the objectivity of assessment result.

Embodiment 3

See Fig. 1, Fig. 2, the present embodiment risk evaluating system based on fuzzy matrix analytic hierarchy process (AHP), including:

(1) evaluation criteria system generation module 1, for generating the evaluation criteria system being directed to dangerous matter sources, described evaluation Index system is formulated by the expert group evaluating dangerous matter sources, and it includes that destination layer, rule layer and indicator layer, described destination layer are defined as Dangerous matter sources to be assessed, described rule layer include probability that dangerous matter sources causes danger, dangerous matter sources cause danger after influence degree With three female indexs of uncontrollability, described indicator layer includes the every sub-index corresponding to female index, wherein considers that system is adopted Taking safety measure or strategy to the abatement of risk and control action, definition uncontrollability is to make strategy mistake after dangerous matter sources is caused danger The ability characteristics of effect；

(2) opinion rating system generation module 2, for generating the opinion rating system corresponding to evaluation criteria system, institute Commentary valency hierarchical system is formulated by the expert group evaluating dangerous matter sources, and it includes multiple opinion rating, and each opinion rating is corresponding One grade fuzzy subset；

(3) quantification of targets module 3, for gathering described every sub-index and according to the impact on corresponding female index of the sub-index Degree quantifies, and specifically performs during the running of described quantification of targets module:

If P, D, C represent respectively probability that dangerous matter sources causes danger, dangerous matter sources cause danger after influence degree and can not Control property, the expert group evaluating dangerous matter sources carries out, to the quantized value of the sub-index of x-th of female index y, the quantized value that n evaluation obtains Collection isThe final quantization value of the sub-index of x-th of female index y is:

${I_x^y}^{\′}=\frac{{\mathrm{\Σ}}_i^n{I}_{xi}^y}{n},{I_x^y}^{\′}\∈\[0,1\]$

(4) index weights computing module 4, calculates female index and the weight vectors of sub-index for reference level fractional analysis；

(5) subordinated-degree matrix builds module 5, for according to described evaluation criteria system, calculates dangerous matter sources respectively to described The degree of membership of grade fuzzy subset, builds the subordinated-degree matrix of female index, and wherein, described subordinated-degree matrix builds module 5 and calculates When dangerous matter sources is to the degree of membership of described grade fuzzy subset, concrete perform following operation:

Definition grade fuzzy subset is { v_j, j=1,2 ..., 5 }, and define the influence degree equity for describing female index The membership function of the degree of membership of level fuzzy subset:

Wherein, ρ is the final quantization of the sub-index of x-th of female index y determined by the expert group expert evaluating dangerous matter sources Value,For grade fuzzy subset { v_j, j=1,2 ..., standard value corresponding for 5},μ is to evaluate dangerous matter sources Expert group's certainty factor to described final quantization value；

According to described membership function, construct P, the subordinated-degree matrix R of tri-female indexs of D, C respectively_P,R_D,R_C:

$R_P=\left\{\begin{array}{cccc}{f}_{v_1}\left({I_1^P}^{\′}\right)& f_{v_2}\left({I_1^P}^{\′}\right)& \mathrm{...}& f_{v_5}\left({I_1^P}^{\′}\right)\\ f_{v_1}\left({I_2^P}^{\′}\right)& f_{v_2}\left({I_2^P}^{\′}\right)& \mathrm{...}& f_{v_5}\left({I_2^P}^{\′}\right)\\ .& .& & .\\ .& .& & .\\ .& .& & .\\ f_{v_1}\left({I_{N_P}^P}^{\′}\right)& f_{v_2}\left({I_{N_P}^P}^{\′}\right)& \mathrm{...}& f_{v_5}\left({I_{N_P}^P}^{\′}\right)\end{array}\right.$

$R_D=\left\{\begin{array}{cccc}{f}_{v_1}\left({I_1^D}^{\′}\right)& f_{v_2}\left({I_1^D}^{\′}\right)& \mathrm{...}& f_{v_5}\left({I_1^D}^{\′}\right)\\ f_{v_1}\left({I_2^D}^{\′}\right)& f_{v_2}\left({I_2^D}^{\′}\right)& \mathrm{...}& f_{v_5}\left({I_2^D}^{\′}\right)\\ .& .& & .\\ .& .& & .\\ .& .& & .\\ f_{v_1}\left({I_{N_D}^D}^{\′}\right)& f_{v_2}\left({I_{N_D}^D}^{\′}\right)& \mathrm{...}& f_{v_5}\left({I_{N_D}^D}^{\′}\right)\end{array}\right.$

$R_C=\left\{\begin{array}{cccc}{f}_{v_1}\left({I_1^C}^{\′}\right)& f_{v_2}\left({I_1^C}^{\′}\right)& \mathrm{...}& f_{v_5}\left({I_1^C}^{\′}\right)\\ f_{v_1}\left({I_2^C}^{\′}\right)& f_{v_2}\left({I_2^C}^{\′}\right)& \mathrm{...}& f_{v_5}\left({I_2^C}^{\′}\right)\\ .& .& & .\\ .& .& & .\\ .& .& & .\\ f_{v_1}\left({I_{N_C}^C}^{\′}\right)& f_{v_2}\left({I_{N_C}^C}^{\′}\right)& \mathrm{...}& f_{v_5}\left({I_{N_C}^C}^{\′}\right)\end{array}\right.$

Wherein, N_PRepresent the sub-index number that female index P comprises, N_DRepresent the sub-index number that female index D comprises, N_CTable Show the sub-index number that female index C comprises；

(6) fuzzy overall evaluation result computing module 6, for according to subordinated-degree matrix and weight vector computation fuzzy synthesis Evaluation result, wherein, the computing formula that described fuzzy overall evaluation result computing module 6 calculates fuzzy overall evaluation result M is:

$M=W*\left(\begin{array}{c}{m}_P*R_P\\ m_D*R_D\\ m_C*R_C\end{array}\right)=(L_1,L_2,...,L_5)$

Wherein, if the weight fuzzy subset corresponding to female index P obtained according to weight vectors, D, C is W={ w_P,w_D, w_C, the weight fuzzy subset corresponding to sub-index set under female index P of obtaining according to weight vectors, D, C is respectively m_P、m_D、 m_C, * represents that generalized fuzzy synthesizes computing；

(7) risk evaluation module 7, including risk profiles assessment submodule 71, risk analysis submodule 72 and risk control Submodule 73, described risk profiles assessment submodule 71 is for calculating mesh according to weight vectors and fuzzy overall evaluation result vector The risk of mark layer；Described risk analysis submodule 72, is carried out point for the risk assessment situation that obtains risk evaluation module Analysis, finds node that Risk of Communication path, Risk of Communication ability are stronger and easy infected node and exports analysis result；Described Risk control submodule 73, for adjusting the access control policy of network and susceptible node security plan according to described analysis result Slightly, the safety management of the most infected node is strengthened.

Wherein, when calculating described risk, if grade corresponding to grade fuzzy subset is entered as { H_j, j=1,2 .., 5 }, I.e. grade v_jCorresponding numerical value H_j, and grade v_jFrom low paramount time described H_jValue is incremented by, and the computing formula of described risk FD is: $\mathrm{FD}=\frac{{\mathrm{\Σ}}_{j=1}^5{H}_j\×L_j}{{\mathrm{\Σ}}_{j=1}^5{L}_j}.$

What the present embodiment defined the degree of membership of the influence degree In Grade fuzzy subset for describing female index is subordinate to letter Number, and utilize described membership function to carry out subordinated-degree matrix structure, calculate degree of membership Normal Distribution, more conform to reality Border, it is to avoid the impact of artificial subjective factor, enhances the objectivity of assessment result；Propose fuzzy overall evaluation result and wind The computing formula of danger degree, this computing formula considers the factor affecting risk the most all sidedly, highlights safety measure effective The property impact on systematic risk degree, it is achieved that the after-action review to dangerous matter sources.

Embodiment 4

(1) evaluation criteria system generation module 1, for generating the evaluation criteria system being directed to dangerous matter sources, described evaluation Index system is formulated by the expert group evaluating dangerous matter sources, and it includes that destination layer, rule layer and indicator layer, described destination layer are defined as Dangerous matter sources to be assessed, described rule layer include probability that dangerous matter sources causes danger, dangerous matter sources cause danger after influence degree With three female indexs of uncontrollability, described indicator layer includes the every sub-index corresponding to female index, wherein considers that system is adopted Taking safety measure or strategy to the abatement of risk and control action, definition uncontrollability is to make strategy mistake after dangerous matter sources is caused danger The ability characteristics of effect；

(2) opinion rating system generation module 2, for generating the opinion rating system corresponding to evaluation criteria system, institute Commentary valency hierarchical system is formulated by the expert group evaluating dangerous matter sources, and it includes multiple opinion rating, and each opinion rating is corresponding One grade fuzzy subset；

(3) quantification of targets module 3, for gathering described every sub-index and according to the impact on corresponding female index of the sub-index Degree quantifies, and specifically performs during the running of described quantification of targets module:

If P, D, C represent respectively probability that dangerous matter sources causes danger, dangerous matter sources cause danger after influence degree and can not Control property, the expert group evaluating dangerous matter sources carries out, to the quantized value of the sub-index of x-th of female index y, the quantized value that n evaluation obtains Collection isThe final quantization value of the sub-index of x-th of female index y is:

${I_x^y}^{\′}=\frac{{\mathrm{\Σ}}_i^n{I}_{xi}^y}{n},{I_x^y}^{\′}\∈\[0,1\]$

(4) index weights computing module 4, calculates female index and the weight vectors of sub-index for reference level fractional analysis；

(5) subordinated-degree matrix builds module 5, for according to described evaluation criteria system, calculates dangerous matter sources respectively to described The degree of membership of grade fuzzy subset, builds the subordinated-degree matrix of female index, and wherein, described subordinated-degree matrix builds module 5 and calculates When dangerous matter sources is to the degree of membership of described grade fuzzy subset, concrete perform following operation:

Definition grade fuzzy subset is { v_j, j=1,2 ..., 5 }, and define the influence degree equity for describing female index The membership function of the degree of membership of level fuzzy subset:

Wherein, ρ is the final quantization of the sub-index of x-th of female index y determined by the expert group expert evaluating dangerous matter sources Value,For grade fuzzy subset { v_j, j=1,2 ..., standard value corresponding for 5},μ is the special of evaluation dangerous matter sources Family's group certainty factor to described final quantization value；

According to described membership function, construct P, the subordinated-degree matrix R of tri-female indexs of D, C respectively_P,R_D,R_C:

$R_P=\left\{\begin{array}{cccc}{f}_{v_1}\left({I_1^P}^{\′}\right)& f_{v_2}\left({I_1^P}^{\′}\right)& \mathrm{...}& f_{v_5}\left({I_1^P}^{\′}\right)\\ f_{v_1}\left({I_2^P}^{\′}\right)& f_{v_2}\left({I_2^P}^{\′}\right)& \mathrm{...}& f_{v_5}\left({I_2^P}^{\′}\right)\\ .& .& & .\\ .& .& & .\\ .& .& & .\\ f_{v_1}\left({I_{N_P}^P}^{\′}\right)& f_{v_2}\left({I_{N_P}^P}^{\′}\right)& \mathrm{...}& f_{v_5}\left({I_{N_P}^P}^{\′}\right)\end{array}\right.$

$R_D=\left\{\begin{array}{cccc}{f}_{v_1}\left({I_1^D}^{\′}\right)& f_{v_2}\left({I_1^D}^{\′}\right)& \mathrm{...}& f_{v_5}\left({I_1^D}^{\′}\right)\\ f_{v_1}\left({I_2^D}^{\′}\right)& f_{v_2}\left({I_2^D}^{\′}\right)& \mathrm{...}& f_{v_5}\left({I_2^D}^{\′}\right)\\ .& .& & .\\ .& .& & .\\ .& .& & .\\ f_{v_1}\left({I_{N_D}^D}^{\′}\right)& f_{v_2}\left({I_{N_D}^D}^{\′}\right)& \mathrm{...}& f_{v_5}\left({I_{N_D}^D}^{\′}\right)\end{array}\right.$

$R_C=\left\{\begin{array}{cccc}{f}_{v_1}\left({I_1^C}^{\′}\right)& f_{v_2}\left({I_1^C}^{\′}\right)& \mathrm{...}& f_{v_5}\left({I_1^C}^{\′}\right)\\ f_{v_1}\left({I_2^C}^{\′}\right)& f_{v_2}\left({I_2^C}^{\′}\right)& \mathrm{...}& f_{v_5}\left({I_2^C}^{\′}\right)\\ .& .& & .\\ .& .& & .\\ .& .& & .\\ f_{v_1}\left({I_{N_C}^C}^{\′}\right)& f_{v_2}\left({I_{N_C}^C}^{\′}\right)& \mathrm{...}& f_{v_5}\left({I_{N_C}^C}^{\′}\right)\end{array}\right.$

Wherein, N_PRepresent the sub-index number that female index P comprises, N_DRepresent the sub-index number that female index D comprises, N_CTable Show the sub-index number that female index C comprises；

(6) fuzzy overall evaluation result computing module 6, for according to subordinated-degree matrix and weight vector computation fuzzy synthesis Evaluation result, wherein, the computing formula that described fuzzy overall evaluation result computing module 6 calculates fuzzy overall evaluation result M is:

$M=W*\left(\begin{array}{c}{m}_P*R_P\\ m_D*R_D\\ m_C*R_C\end{array}\right)=(L_1,L_2,...,L_5)$

Wherein, if the weight fuzzy subset corresponding to female index P obtained according to weight vectors, D, C is W={w_P,w_D, w_C, the weight fuzzy subset corresponding to sub-index set under female index P of obtaining according to weight vectors, D, C is respectively m_P、m_D、 m_C, * represents that generalized fuzzy synthesizes computing；

(7) risk evaluation module 7, including risk profiles assessment submodule 71, risk analysis submodule 72 and risk control Submodule 73, described risk profiles assessment submodule 71 is for calculating mesh according to weight vectors and fuzzy overall evaluation result vector The risk of mark layer；Described risk analysis submodule 72, is carried out point for the risk assessment situation that obtains risk evaluation module Analysis, finds node that Risk of Communication path, Risk of Communication ability are stronger and easy infected node and exports analysis result；Described Risk control submodule 73, for adjusting the access control policy of network and susceptible node security plan according to described analysis result Slightly, the safety management of the most infected node is strengthened.

Wherein, when calculating described risk, if grade corresponding to grade fuzzy subset is entered as { H_j, j=1,2 .., 5}, I.e. grade v_jCorresponding numerical value H_j, and grade v_jFrom low paramount time described H_jValue is incremented by, and the computing formula of described risk FD is: $\mathrm{FD}=\frac{{\mathrm{\Σ}}_{j=1}^5{H}_j\×L_j}{{\mathrm{\Σ}}_{j=1}^5{L}_j}.$

Wherein, described opinion rating includes probability opinion rating, severity ratings grade；Described probability opinion rating Including extremely impossible, rarely possible, possible, quite possible, entirely possible；Described severity ratings grade includes negligible, light Micro-, serious, dangerous, disaster.

What the present embodiment defined the degree of membership of the influence degree In Grade fuzzy subset for describing female index is subordinate to letter Number, and utilize described membership function to carry out subordinated-degree matrix structure, calculate degree of membership Normal Distribution, more conform to reality Border, it is to avoid the impact of artificial subjective factor, enhances the objectivity of assessment result；Propose fuzzy overall evaluation result and wind The computing formula of danger degree, this computing formula considers the factor affecting risk the most all sidedly, highlights safety measure effective The property impact on systematic risk degree, it is achieved that the after-action review to dangerous matter sources, and define the content of opinion rating, improve The completeness of risk evaluating system.

Embodiment 5

(1) evaluation criteria system generation module 1, for generating the evaluation criteria system being directed to dangerous matter sources, described evaluation Index system is formulated by the expert group evaluating dangerous matter sources, and it includes that destination layer, rule layer and indicator layer, described destination layer are defined as Dangerous matter sources to be assessed, described rule layer include probability that dangerous matter sources causes danger, dangerous matter sources cause danger after influence degree With three female indexs of uncontrollability, described indicator layer includes the every sub-index corresponding to female index, wherein considers that system is adopted Taking safety measure or strategy to the abatement of risk and control action, definition uncontrollability is to make strategy mistake after dangerous matter sources is caused danger The ability characteristics of effect；

(2) opinion rating system generation module 2, for generating the opinion rating system corresponding to evaluation criteria system, institute Commentary valency hierarchical system is formulated by the expert group evaluating dangerous matter sources, and it includes multiple opinion rating, and each opinion rating is corresponding One grade fuzzy subset；

(3) quantification of targets module 3, for gathering described every sub-index and according to the impact on corresponding female index of the sub-index Degree quantifies, and specifically performs during the running of described quantification of targets module:

If P, D, C represent respectively probability that dangerous matter sources causes danger, dangerous matter sources cause danger after influence degree and can not Control property, the expert group evaluating dangerous matter sources carries out, to the quantized value of the sub-index of x-th of female index y, the quantized value that n evaluation obtains Collection isThe final quantization value of the sub-index of x-th of female index y is:

${I_x^y}^{\′}=\frac{{\mathrm{\Σ}}_i^n{I}_{xi}^y}{n},{I_x^y}^{\′}\∈\[0,1\]$

(4) index weights computing module 4, calculates female index and the weight vectors of sub-index for reference level fractional analysis；

(5) subordinated-degree matrix builds module 5, for according to described evaluation criteria system, calculates dangerous matter sources respectively to described The degree of membership of grade fuzzy subset, builds the subordinated-degree matrix of female index, and wherein, described subordinated-degree matrix builds module 5 and calculates When dangerous matter sources is to the degree of membership of described grade fuzzy subset, concrete perform following operation:

Definition grade fuzzy subset is { v_j, j=1,2 ..., 5}, and define the influence degree equity for describing female index The membership function of the degree of membership of level fuzzy subset:

Wherein, ρ is the final quantization of the sub-index of x-th of female index y determined by the expert group expert evaluating dangerous matter sources Value,For grade fuzzy subset { v_j, j=1,2 ..., standard value corresponding for 5},μ is to evaluate dangerous matter sources Expert group's certainty factor to described final quantization value；

According to described membership function, construct P, the subordinated-degree matrix R of tri-female indexs of D, C respectively_P,R_D,R_C:

$R_P=\left\{\begin{array}{cccc}{f}_{v_1}\left({I_1^P}^{\′}\right)& f_{v_2}\left({I_1^P}^{\′}\right)& \mathrm{...}& f_{v_5}\left({I_1^P}^{\′}\right)\\ f_{v_1}\left({I_2^P}^{\′}\right)& f_{v_2}\left({I_2^P}^{\′}\right)& \mathrm{...}& f_{v_5}\left({I_2^P}^{\′}\right)\\ .& .& & .\\ .& .& & .\\ .& .& & .\\ f_{v_1}\left({I_{N_P}^P}^{\′}\right)& f_{v_2}\left({I_{N_P}^P}^{\′}\right)& \mathrm{...}& f_{v_5}\left({I_{N_P}^P}^{\′}\right)\end{array}\right.$

$R_D=\left\{\begin{array}{cccc}{f}_{v_1}\left({I_1^D}^{\′}\right)& f_{v_2}\left({I_1^D}^{\′}\right)& \mathrm{...}& f_{v_5}\left({I_1^D}^{\′}\right)\\ f_{v_1}\left({I_2^D}^{\′}\right)& f_{v_2}\left({I_2^D}^{\′}\right)& \mathrm{...}& f_{v_5}\left({I_2^D}^{\′}\right)\\ .& .& & .\\ .& .& & .\\ .& .& & .\\ f_{v_1}\left({I_{N_D}^D}^{\′}\right)& f_{v_2}\left({I_{N_D}^D}^{\′}\right)& \mathrm{...}& f_{v_5}\left({I_{N_D}^D}^{\′}\right)\end{array}\right.$

$R_C=\left\{\begin{array}{cccc}{f}_{v_1}\left({I_1^C}^{\′}\right)& f_{v_2}\left({I_1^C}^{\′}\right)& \mathrm{...}& f_{v_5}\left({I_1^C}^{\′}\right)\\ f_{v_1}\left({I_2^C}^{\′}\right)& f_{v_2}\left({I_2^C}^{\′}\right)& \mathrm{...}& f_{v_5}\left({I_2^C}^{\′}\right)\\ .& .& & .\\ .& .& & .\\ .& .& & .\\ f_{v_1}\left({I_{N_C}^C}^{\′}\right)& f_{v_2}\left({I_{N_C}^C}^{\′}\right)& \mathrm{...}& f_{v_5}\left({I_{N_C}^C}^{\′}\right)\end{array}\right.$

Wherein, N_PRepresent the sub-index number that female index P comprises, N_DRepresent the sub-index number that female index D comprises, N_CTable Show the sub-index number that female index C comprises；

(6) fuzzy overall evaluation result computing module 6, for according to subordinated-degree matrix and weight vector computation fuzzy synthesis Evaluation result, wherein, the computing formula that described fuzzy overall evaluation result computing module 6 calculates fuzzy overall evaluation result M is:

$M=W*\left(\begin{array}{c}{m}_P*R_P\\ m_D*R_D\\ m_C*R_C\end{array}\right)=(L_1,L_2,...,L_5)$

Wherein, if the weight fuzzy subset corresponding to female index P obtained according to weight vectors, D, C is W={w_P,w_D, w_C, the weight fuzzy subset corresponding to sub-index set under female index P of obtaining according to weight vectors, D, C is respectively m_P、m_D、 m_C, * represents that generalized fuzzy synthesizes computing；

(7) risk evaluation module 7, including risk profiles assessment submodule 71, risk analysis submodule 72 and risk control Submodule 73, described risk profiles assessment submodule 71 is for calculating mesh according to weight vectors and fuzzy overall evaluation result vector The risk of mark layer；Described risk analysis submodule 72, is carried out point for the risk assessment situation that obtains risk evaluation module Analysis, finds node that Risk of Communication path, Risk of Communication ability are stronger and easy infected node and exports analysis result；Described Risk control submodule 73, for adjusting the access control policy of network and susceptible node security plan according to described analysis result Slightly, the safety management of the most infected node is strengthened.

Wherein, when calculating described risk, if grade corresponding to grade fuzzy subset is entered as { H_j, j=1,2 .., 5}, I.e. grade v_jCorresponding numerical value H_j, and grade v_jFrom low paramount time described H_jValue is incremented by, and the computing formula of described risk FD is: $\mathrm{FD}=\frac{{\mathrm{\Σ}}_{j=1}^5{H}_j\×L_j}{{\mathrm{\Σ}}_{j=1}^5{L}_j}.$

Wherein, described opinion rating includes probability opinion rating, severity ratings grade；Described probability opinion rating Including extremely impossible, rarely possible, possible, quite possible, entirely possible；Described severity ratings grade includes negligible, light Micro-, serious, dangerous, disaster.

Wherein, in the described every sub-index corresponding to female index, the son of the probability caused danger corresponding to dangerous matter sources refers to Mark includes threatening motivation, attacking ability, attack complexity, vulnerability exploit rate and assets captivation, endangers corresponding to dangerous matter sources The sub-index of the influence degree behind danger includes confidentiality impact, integrity impact, availability impact and relatedness impact, corresponds to The sub-index of uncontrollability includes disguised power of test, multiformity power of test and prominent anti-defensive ability/resistance ability.

What the present embodiment defined the degree of membership of the influence degree In Grade fuzzy subset for describing female index is subordinate to letter Number, and utilize described membership function to carry out subordinated-degree matrix structure, calculate degree of membership Normal Distribution, more conform to reality Border, it is to avoid the impact of artificial subjective factor, enhances the objectivity of assessment result；Propose fuzzy overall evaluation result and wind The computing formula of danger degree, this computing formula considers the factor affecting risk the most all sidedly, highlights safety measure effective The property impact on systematic risk degree, it is achieved that the after-action review to dangerous matter sources, and define opinion rating and every sub-index Content, improve the completeness of risk evaluating system.

Last it should be noted that, above example is only in order to illustrate technical scheme, rather than the present invention is protected Protecting the restriction of scope, although having made to explain to the present invention with reference to preferred embodiment, those of ordinary skill in the art should Work as understanding, technical scheme can be modified or equivalent, without deviating from the reality of technical solution of the present invention Matter and scope.

Claims (7)

1. risk evaluating system based on fuzzy matrix analytic hierarchy process (AHP), it is characterised in that including:

(1) evaluation criteria system generation module, for generating the evaluation criteria system being directed to dangerous matter sources, described evaluation index system Uniting and formulated by the expert group evaluating dangerous matter sources, it includes that destination layer, rule layer and indicator layer, described destination layer are defined as to be assessed Dangerous matter sources, described rule layer include probability that dangerous matter sources causes danger, dangerous matter sources cause danger after influence degree and can not Three female indexs of control property, described indicator layer includes the every sub-index corresponding to female index, wherein considers the taked safety of system Measure or strategy are to the abatement of risk and control action, and definition uncontrollability is to make the energy of strategy fails after dangerous matter sources is caused danger Force characteristic；

(2) opinion rating system generation module, for generating the opinion rating system corresponding to evaluation criteria system, described evaluation Hierarchical system is formulated by the expert group evaluating dangerous matter sources, and it includes multiple opinion rating, corresponding one of each opinion rating etc. Level fuzzy subset；

(3) quantification of targets module, for gathering described every sub-index and according to the sub-index influence degree to corresponding female index Quantify；

(4) index weights computing module, calculates female index and the weight vectors of sub-index for reference level fractional analysis；

(5) subordinated-degree matrix builds module, for according to described evaluation criteria system, calculates dangerous matter sources respectively to described grade mould Stick with paste the degree of membership of subset, build the subordinated-degree matrix of female index；

(6) fuzzy overall evaluation result computing module, for according to subordinated-degree matrix and weight vector computation fuzzy overall evaluation Result；

(7) risk evaluation module, including risk profiles assessment submodule, risk analysis submodule and risk control submodule, institute State risk profiles assessment submodule for calculating the risk of destination layer according to weight vectors and fuzzy overall evaluation result vector； Described risk analysis submodule, is analyzed for the risk assessment situation obtaining risk evaluation module, finds Risk of Communication Node that path, Risk of Communication ability are stronger and easy infected node also export analysis result；Described risk control submodule, For adjusting the access control policy of network and susceptible node security strategy according to described analysis result, strengthen the most infected node Safety management.

Risk evaluating system based on fuzzy matrix analytic hierarchy process (AHP) the most according to claim 1, it is characterised in that described Opinion rating includes probability opinion rating, severity ratings grade；Described probability opinion rating includes extremely impossible, few Possible, possible, quite possible, entirely possible；Described severity ratings grade includes negligible, slight, serious, dangerous , disaster.

Risk evaluating system based on fuzzy matrix analytic hierarchy process (AHP) the most according to claim 1, it is characterised in that described In every sub-index corresponding to female index, the sub-index of the probability caused danger corresponding to dangerous matter sources includes threatening motivation, attacking Hit ability, attack complexity, vulnerability exploit rate and assets captivation, the son of the influence degree after causing danger corresponding to dangerous matter sources Index includes confidentiality impact, integrity impact, availability impact and relatedness impact, corresponding to the sub-index of uncontrollability Including disguised power of test, multiformity power of test and prominent anti-defensive ability/resistance ability.

Risk evaluating system based on fuzzy matrix analytic hierarchy process (AHP) the most according to claim 1, it is characterised in that described Specifically perform during the running of quantification of targets module:

If P, D, C represent respectively probability that dangerous matter sources causes danger, dangerous matter sources cause danger after influence degree and uncontrollability, The quantized value of the sub-index of x-th of female index y is carried out n time evaluating the set of quantized obtained by the expert group of evaluation dangerous matter sourcesY=P, D, C, the final quantization value of the sub-index of x-th of female index y is:

。

Risk evaluating system based on fuzzy matrix analytic hierarchy process (AHP) the most according to claim 4, it is characterised in that described Subordinated-degree matrix builds module when calculating dangerous matter sources to the degree of membership of described grade fuzzy subset, concrete performs following operation:

Definition grade fuzzy subset is { v_j, j=1,2 ..., 5}, and define the influence degree In Grade mould for describing female index The membership function of the degree of membership of paste subset:

Wherein, ρ is the final quantization value of the sub-index of x-th of the female index y determined by the expert group expert evaluating dangerous matter sources, For grade fuzzy subset { v_j, j=1,2 ..., standard value corresponding for 5},μ is the expert group evaluating dangerous matter sources Certainty factor to described final quantization value；

According to described membership function, construct P, the subordinated-degree matrix R of tri-female indexs of D, C respectively_P,R_D,R_C:

Wherein, N_PRepresent the sub-index number that female index P comprises, N_DRepresent the sub-index number that female index D comprises, N_CRepresent mother The sub-index number that index C comprises.

Risk evaluating system based on fuzzy matrix analytic hierarchy process (AHP) the most according to claim 5, it is characterised in that described Fuzzy overall evaluation result computing module calculates the computing formula of fuzzy overall evaluation result M:

Wherein, if the weight fuzzy subset corresponding to female index P obtained according to weight vectors, D, C is W={w_P,w_D,w_C, root The weight fuzzy subset corresponding to sub-index set under female index P of obtaining according to weight vectors, D, C is respectively m_P、m_D、m_C, * table Show that generalized fuzzy synthesizes computing.

Risk evaluating system based on fuzzy matrix analytic hierarchy process (AHP) the most according to claim 6, it is characterised in that calculate During described risk, if grade corresponding to grade fuzzy subset is entered as { H_j, j=1,2 .., 5}, i.e. grade v_jCorresponding numerical value H_j, and grade v_jFrom low paramount time described H_jValue is incremented by, and the computing formula of described risk FD is: