Method for assessing access risk of information system based on access context logical reasoning

Abstract

The application discloses a kind of information system access risk assessment methods based on access context logical inference, according to the security element of access subject context, the accessible asset set of access subject is obtained by logical inference of trust calculation and access policy set, then the risk degree of single access subject's comprehensive trust degree, access environment and its actual access asset value degree are carried out fuzzy processing, fuzzy inference and defuzzification, obtain the session access risk of single subject, after processing, the session access risk of entire information system is obtained.The application obtains the access permission of access subject by using trust calculation and logical inference, and introduces access context and reasoning mechanism into risk assessment, carries out fuzzy processing to these contexts related to access, such as environmental risk degree, asset value degree, by quantifying each element of access context process in information system, combined with logical inference and fuzzy inference, the comprehensive evaluation of information system session access risk is realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of information security, and particularly relates to an information system access risk assessment method based on access context logical reasoning. BACKGROUND

[0002] At present, common information security risk assessment methods mainly focus on the risk caused by information system threats and vulnerabilities, and it is difficult to directly evaluate the session security degree. The present application provides an access risk assessment method based on access context, which can provide assistance for conventional information system security risk management. SUMMARY

[0003] The present application aims at the deficiency that the prior art cannot dynamically calculate and analyze the risk according to the change of access context and environment and other related security elements, and provides an information system access risk assessment method based on access context logical reasoning. The access authority of an access subject is obtained by using trust calculation and logical reasoning, and the access context and reasoning mechanism are introduced into risk assessment. The environment risk degree, asset value degree and other access-related contexts are fuzzified, each element in the access context process in the information system is quantified, and the comprehensive assessment of the information system session access risk is realized by combining logical reasoning and fuzzy reasoning.

[0004] The present application is implemented by the following technical scheme:

[0005] The present application relates to an information system access risk assessment method based on access context logical reasoning. According to the security elements of the access subject context, the accessible asset set of the access subject is obtained by logical reasoning through trust calculation and access policy set, the comprehensive trust degree of a single access subject, the risk degree of the access environment and the value degree of the actual access asset are fuzzified, fuzzily reasoned and de-fuzzified, the session access risk of a single subject is obtained, and the session access risk of the entire information system is obtained through the summary processing.

[0006] The application relates to an information system access risk assessment system for realizing the above method, comprising a subject trust value calculation and logical reasoning module, an access environment risk degree calculation module, an access asset value calculation module and a subject risk fuzzy calculation and access risk integration module, wherein: the subject trust value calculation and logical reasoning module calculates an accessible asset set of an access subject according to a long-term trust value of the access subject, a weight vector of trust elements of the access subject and a trust score threshold value, the access environment risk degree calculation module calculates a fuzzy calculation value of an access risk degree according to an environment threat degree of information system security situation awareness and an environment protection measure level, the access asset value calculation module calculates a fuzzy calculation value of an accessed asset value according to a security level of the accessed asset from an access asset database of the information system and the importance of the accessed asset, and the subject risk fuzzy calculation and access risk integration module calculates an access risk of the whole system according to the trust value of the access subject from the subject trust value calculation and logical reasoning, the fuzzy calculation value of the access environment risk from the access environment risk degree calculation and the fuzzy calculation value of the multi-asset value from the access asset value calculation. The access subject trust element vector comes from each item of information related to the access subject in the access process, including the identity of the access subject, the access time, the device used for access and the certificate carried during access.

[0007] The long-term trust value of the access subject is stored in an access subject database and is obtained by the following method.

[0008] 1) Calculate an open relation number vector: according to whether the identity of the input access subject is legal, whether the access time is in compliance, whether the device used for access is in compliance and whether the certificate carried during access is trustworthy, the open relation number vector (r1, r2, r3, r4) is valued. When a certain value meets the regulation, the value of the corresponding open relation number is 1, otherwise, the value is -1.

[0009] 2) Calculate the modified trust value of the access subject: TrustAdjust = LongtermTrust + k * (x1, x2, x3, x4) * (r1, r2, r3, r4) T , wherein: TrustAdjust is the modified trust value of the access subject, LongtermTrust is the long-term trust value of the access subject, (x1, x2, x3, x4) is the weight vector of the input access subject trust elements, each value is between 0 and 1, and satisfies x1+x2+x3+x4=1, (r1, r2, r3, r4) is the open relation number corresponding to each trust element dimension, the value range is {-1, 1}, k is a discount coefficient, the value range is (0, 1).

[0010] 3) Calculate the instant session trust value of the access subject: after the modified trust value is calculated, the trust value is normalized by the following Sigmoid formula: Wherein: CurrentTrust is the normalized result, map CurrentTrust to 0-100 as the instant session trust value SessionTrust of the access subject = CurrentTrust*100.

[0011] 4) Determine the ticket obtained by the access subject in the session, that is, whether the access subject can obtain a ticket in the session and what level of ticket can be obtained:

[0012] By classifying the instant session trust value output in the previous step, the level of ticket obtained is determined in combination with the trust score threshold. The trust score threshold includes ordinary_score and special_score, which represent the required ordinary access trust score and privileged trust score of the system. When the trust value is less than ordinary_score, it is considered that the access subject does not obtain any ticket; when the trust value is greater than or equal to ordinary_score and less than special_score, it is considered that the access subject obtains a ticket of ordinary level; when the trust value is greater than or equal to special_score, it is considered that the access subject obtains a ticket of privileged level.

[0013] 5) Determine the asset set accessible to the access subject, that is, determine the range of asset set accessible to the access subject in the session through the logical reasoning rule set: input the identity of the access subject and the ticket obtained, and obtain the security level of the asset accessible to the access subject through logical reasoning of the logical reasoning rule set, and then obtain the asset set accessible to the access subject.

[0014] The logical reasoning rule set includes:

[0015] A) Any subject obtaining a ticket of ordinary level or privileged level can access assets with low security level;

[0016] B) A normal subject or administrator subject belonging to the system and obtaining a ticket of privileged level can access assets with medium security level;

[0017] C) An administrator subject belonging to the system and obtaining a ticket of ordinary level can access assets with medium security level;

[0018] D) An administrator subject belonging to the system and obtaining a ticket of privileged level can access assets with high security level.

[0019] The access risk degree fuzzy calculation value is obtained by the following steps:

[0020] 1) Fuzzy the threat degree of the environment: Calculate and obtain the membership degree of the threat degree of the access environment belonging to each class, and divide the threat degree of the access environment into three classes: Low_Threat, Medium_Threat and High_Threat classes, which represent the threat degree of the access environment as "low", "general" and "high", respectively, and specifically:

[0021]

[0022] Wherein: x represents the threat degree of the access environment, ranging from 0 to 100 in the closed interval.

[0023] 2) Fuzzy the level of environmental protection measures: Calculate and obtain the membership degree of the level of protection measures of the access environment belonging to each fuzzy class, and divide the level of protection measures of the access environment into three classes: Low_Protection, Medium_Protection and High_Protection classes, which represent the level of protection measures of the access environment as "low", "general" and "high", respectively, and specifically:

[0024]

[0025] Wherein: x represents the level of protection measures of the access environment, ranging from 0 to 100 in the closed interval.

[0026] 3) According to the fuzzy rules, implement fuzzy reasoning, calculate and obtain the output membership degree corresponding to each rule, specifically including:

[0027] 3.1) According to the membership degree of the threat degree of the access environment and the level of environmental protection measures corresponding to each fuzzy class, further divide the risk degree of the access environment into three classes: Low_Env_Risk, Medium_Env_Risk and High_Env_Risk, which represent the risk degree of the access environment as "low", "general" and "high", respectively, and specifically:

[0028]

[0029] Wherein: x represents the risk degree of the access environment, ranging from 0 to 100 in the closed interval.

[0030] 3.2) Based on the membership degrees of the environmental threat degree belonging to the three fuzzy classes of low environmental threat degree, medium environmental threat degree and high environmental threat degree and the membership degrees of the environmental protection measure level belonging to the three fuzzy classes of low environmental protection measure level, medium environmental protection measure level and high environmental protection measure level, fuzzy reasoning is implemented based on the rule set of accessing environmental risk degree, accessing environmental threat degree class and accessing environmental protection measure level class, 9 rules in the table are traversed, the membership degrees of the accessed environmental risk fuzzy class are calculated for the ignited rules respectively, and specifically, μ(env_risk∈Env_Risk) = min[μ(threat∈Threat), μ(protection∈Protection)], wherein μ(threat∈Threat) represents the membership degree of the environmental threat degree belonging to a fuzzy class (Low_Threat, Medium_Threat or High_Threat), μ(protection∈Protection) represents the membership degree of the environmental protection measure level belonging to a fuzzy class (Low_Protection, Medium_Protection or High_Protection), and μ(env_risk∈Env_Risk) represents the membership degree of the environmental risk degree belonging to a fuzzy class (Low_Env_Risk, Medium_Env_Risk or High_Env_Risk).

[0031] The rule set of accessing environmental risk degree is shown in Table 1.

[0032] Table 1

[0033]

[0034] 4) Calculate the membership degree of the risk degree of the access environment to each fuzzy class: combine the output membership degrees of each rule, and take the maximum value of the membership degrees of the same fuzzy class of each rule as the membership degree of the risk degree of the access environment to each fuzzy class, specifically: Low_Env_Risk_Deg = max{μ(env_risk∈Low_Env_Risk)in fired rules}, Medium_Env_Risk_Deg = max{μ(env_risk∈Medium_Env_Risk)in fired rules}, High_Env_Risk_Deg = max{μ(env_risk∈High_Env_Risk)in fired rules}, wherein: Low_Env_Risk_Deg represents the membership degree of the risk degree of the access environment to the "low" access environment risk degree, Medium_Env_Risk_Deg represents the membership degree of the risk degree of the access environment to the "general" access environment risk degree, and High_Env_Risk_Deg represents the membership degree of the risk degree of the access environment to the "high" access environment risk degree.

[0035] The fuzzy calculation value of the value of the accessed asset is specifically obtained by the following method:

[0036] 1) Fuzzy processing of the importance of the access asset: calculate and obtain the membership degree of the importance degree of the access asset to each fuzzy class, and divide the importance degree of the access asset into three classes: Low_Importance, Medium_Importance and High_Importance classes, which respectively represent "low", "general" and "high" importance of the access asset, specifically:

[0037] Wherein: x represents the importance degree of the access asset, and the range is in the closed interval of 0 to 100.

[0038] The security level of the access asset is a precise concept and does not need to be fuzzy processed. The security level of the access asset is divided into three classes: Low_SecurityLevel, Medium_SecurityLevel and High_SecurityLevel, which respectively represent "low", "general" and "high" security level of the asset.

[0039] 2) According to the fuzzy rule, fuzzy reasoning is implemented to obtain the output membership degree corresponding to each rule, specifically including:

[0040] 2.1) The asset value is divided into three categories: Low_Value, Medium_Value and High_Value, representing the value of the access asset "low", "average" and "high", respectively, as follows: wherein: x represents the value of the access asset, ranging from 0 to 100 in the closed interval.

[0041] 2.2) According to the membership degree of the security level of the access asset belonging to the three categories of low security level, medium security level and high security level, and the membership degree of the importance of the access asset belonging to the three fuzzy categories of low importance, medium importance and high importance; and according to the access asset value level rule set, combined with the security level of the access asset and the importance category of the access asset, fuzzy reasoning is implemented, and 9 rules in the table are traversed to calculate the membership degree of the value of the access asset, specifically: μ(Value∈Value) = min[μ(securitylevel∈SecurityLevel), μ(importance∈Importance)], wherein: μ(securitylevel∈SecurityLevel) represents the membership degree of the asset security level belonging to a certain category (Low_SecurityLevel, Medium_SecurityLevel or High_SecurityLevel), μ(importance∈Importance) represents the membership degree of the asset importance belonging to a certain fuzzy category (Low_Importance, Medium_Importance or High_Importance), and μ(value∈Value) represents the membership degree of the value of the access asset belonging to a certain fuzzy category (Low_Value, Medium_Value or High_Value).

[0042] The access asset value level rule set is shown in Table 2.

[0043] Table 2

[0044]

[0045] 3) Calculate the membership of the value of the accessed asset to each fuzzy class: according to the inference results of the fired fuzzy rules, take the maximum value of the membership of the same fuzzy class output by each rule as the membership of the value of the accessed asset to each fuzzy class, specifically Low_Value_Deg = max{μ(value∈Low_Value)in fired rules}, Medium_Value_Deg = max{μ(value∈Medium_Value)in fired rules}, High_Value_Deg = max{μ(value∈High_Value)in fired rules}, wherein: Low_Value_Deg represents the membership of the value of the accessed asset to the "low" value of the accessed asset, Medium_Value_Deg represents the membership of the value of the accessed asset to the "general" value of the accessed asset, and High_Value_Deg represents the membership of the value of the accessed asset to the "high" value of the accessed asset.

[0046] The access risk of the entire system is obtained by the following method:

[0047] 1) Fuzzy processing of the trust degree of the access subject: combining the instant session trust value obtained in step 1, calculate and obtain the membership of the access subject to each class of fuzzy trust level, and divide the trust degree of the subject into three categories: Low_Trust, Medium_Trust and High_Trust classes, which respectively represent three categories of "low", "general" and "high" security of the user subject, specifically: Wherein: x represents the session trust score of the access subject, ranging from 0 to 100 in the closed interval.

[0048] 2) According to the fuzzy rule, the fuzzy inference is carried out to obtain the output membership corresponding to each rule, specifically including:

[0049] 2.1) The risk brought by a single subject to the entire system is divided into five categories: Very_Low_Risk, Low_Risk,

[0050] Medium_Risk, High_Risk and Very_High_Risk classes, which respectively represent that the subject brings "very low", "low", "general", "high" and "very high" risk to the entire system, specifically: Wherein: x represents the threat degree of the access environment, ranging from 0 to 100 in the closed interval.

[0051] 2.2) According to the membership degrees of the subject trust level belonging to the three fuzzy classes of Low_Trust, Medium_Trust and High_Trust, the membership degrees of the environmental risk level belonging to the three fuzzy classes of Low_Env_Risk, Medium_Env_Risk and High_Env_Risk, and the membership degrees of the asset value belonging to the three fuzzy classes of Low_Value, Medium_Value and High_Value, the fuzzy inference is implemented according to the subject access risk fuzzy rule set, and the output membership degree of each fired rule is calculated: μ(risk ∈ Risk) = min[μ(trust ∈ Trust), μ(env_rish ∈ Env_Risk), μ(value ∈ Value)], wherein: μ(trust ∈ Trust) represents the membership degree of the subject trust level belonging to a certain class (Low_Trust, Medium_Trust or High_Trust class), μ(env_risk ∈ Env_Risk) represents the membership degree of the environmental risk level belonging to a certain fuzzy class (Low_Env_Risk, Medium_Env_Risk or High_Env_Risk), μ(value ∈ Value) represents the membership degree of the value of the accessed asset belonging to a certain fuzzy class (Low_Value, Medium_Value or High_Value), and μ(risk ∈ Risk) represents the membership degree of the access risk belonging to a certain fuzzy class (Very_Low_Risk, Low_Risk, Medium_Risk, High_Risk or Very_High_Risk class).

[0052] The subject access risk fuzzy rule set is shown in Table 3.

[0053] Table 3

[0054]

[0055] 3) The defuzzification is implemented by using the centroid method to obtain the access risk related to a single subject and a single accessed asset, and specifically includes:

[0056] 3.1) Combine the membership of the same fuzzy class according to the inference results of multiple fuzzy rules: divide the access risk into five categories: Very_Low_Risk, Low_Risk, Medium_Risk, High_Risk and Very_High_Risk, and combine the membership of three categories respectively according to the results calculated in each rule in the previous step to obtain the total membership of each category, which is: Very_Low_Risk_Sum = ∑μ(risk∈Very_Low_Risk), Low_Risk_Sum = ∑μ(risk∈Low_Risk), Medium_Risk_Sum = ∑μ(risk∈Medium_Risk), High_Risk_Sum = ∑μ(risk∈High_Risk), Very_High_Risk_Sum = ∑μ(risk∈Very_High_Risk).

[0057] 3.2) Calculate the asset value by using the centroid method according to the total membership of each category: the centroid value of Very_Low_Risk class is Very_Low_Risk_Center = 10; the centroid value of Low_Risk class is Low_Risk_Center = 30; the centroid value of Medium_Risk class is Medium_Risk_Center = 50; the centroid value of High_Risk class is High_Risk_Center = 70; the centroid value of Very_High_Risk class is Very_High_Risk_Center = 90; calculate the asset value from the overall perspective to obtain the access risk related to a single subject and a single access asset, which is:

[0058] 4) Build an access risk matrix to calculate the access risk of the entire system: calculate the access risk of the entire system according to the access risk calculation value of the two-way correlation between all subjects and all access assets in the system, which is:

[0059] 4.1) Build an access risk matrix: each access subject in the system corresponds to a row in the access risk matrix, and all access subjects in the system are traversed to calculate the access risk value between each other. When a certain access subject cannot access a certain asset, the access risk between them is considered to be 0. After obtaining the access risk of the two-way correlation between all subjects and all access assets, the following access risk matrix is constructed: Where: m represents the number of access subjects, n represents the number of access assets, r ijAccessRisk(IS) represents the access risk arising between access subject i and access asset j = ∑ 0<i≤m，0<j≤n r ij .

[0060] 4.2) Calculate the overall access risk of the system. The access risk (IS) of the entire system can be obtained by summing the elements in the access risk matrix.

[0061] Technical effect

[0062] The previously undisclosed technical means disclosed in this invention is as follows: four dimensions of access context security elements are introduced, and fuzzy reasoning and calculation are performed by combining the fuzzy security degree of the access environment and the fuzzy importance degree of the access assets, so as to realize the dynamic assessment of system access session security risks.

[0063] The previously undisclosed technical effects of the aforementioned technical means are: to achieve dynamic assessment of system access security risks based on the current security status of the system's access sessions, providing a useful supplement to conventional information security risk management.

[0064] By utilizing trust computation and logical reasoning, the access permissions of the accessing subjects were obtained. The access context-related factors, such as the degree of environmental risk and the degree of asset value, were fuzzified. Through fuzzy reasoning and integration, the session access risks of each accessing subject were finally integrated, thereby achieving the assessment of the access risks of the entire system.

[0065] This invention can apply corresponding calculation and reasoning methods to information systems. By combining access context and reasoning mechanisms, it can achieve access risk assessment of information systems, thereby providing assistance for the deployment, implementation and risk mitigation of information systems. Attached Figure Description

[0066] Figure 1 This is a flowchart of the present invention;

[0067] Figure 2 This is a relationship diagram for an example. Detailed Implementation

[0068] like Figure 1 and Figure 2 As shown in the figure, this embodiment relates to an information system access risk assessment system based on access context logical reasoning, including: a subject trust value calculation and logical reasoning module, an access environment risk level calculation module, an access asset value calculation module, and a subject risk fuzzy calculation and access risk integration module.

[0069] The main body trust value calculation and logical reasoning module comprises an open relationship coefficient vector calculation unit, a revised trust value calculation unit, an instant session trust value calculation unit and an access ticket granting unit, wherein: the open relationship coefficient vector calculation unit performs judgment and classification processing according to the security element information of the access context, and obtains the result after the open relationship coefficient vector is assigned; the revised trust value calculation unit performs corresponding vector multiplication calculation and addition processing according to the long-term trust degree of the access subject, the weight vector of the security elements of the access context, and the output information of the open relationship coefficient vector calculation unit, and obtains the revised trust value of the access subject; the instant session trust value calculation unit performs normalization processing according to the output information of the revised trust value calculation unit, and obtains the instant session trust value of the access subject; and the access ticket granting unit performs judgment and classification processing according to the output information of the instant session trust value calculation unit, and obtains the ticket that can be granted to the access subject.

[0070] The access environment risk degree calculation module comprises an access environment threat fuzzification processing unit, an access environment protection measure fuzzification processing unit and an access environment risk fuzzy reasoning unit, wherein: the access environment threat fuzzification processing unit performs fuzzification processing according to the threat degree information of the access environment, and obtains the fuzzy category of the access environment threat; the access environment protection measure fuzzification processing unit performs fuzzification processing according to the protection measure level information of the access environment, and obtains the fuzzy classification of the access environment protection measure; and the access environment risk fuzzy reasoning unit performs fuzzy reasoning according to the output information of the access environment threat fuzzification processing unit and the output information of the access environment protection measure fuzzification processing unit, and obtains the fuzzy classification result of the access environment risk.

[0071] The access asset value calculation module comprises an access asset importance fuzzification processing unit and an access asset value fuzzy reasoning unit, wherein: the access asset importance fuzzification processing unit performs fuzzification processing according to the importance value information of the access asset, and obtains the fuzzy classification result of the access asset importance; and the access asset value fuzzy reasoning unit performs fuzzy reasoning according to the information of the access asset importance fuzzification processing unit and the security level of the access asset, and obtains the fuzzy classification result of the access asset value.

[0072] The main body risk fuzzy calculation and access risk integration module comprises: an access subject trust degree fuzzy processing unit, a single subject access risk fuzzy reasoning unit, a centroid method defuzzification unit and an entire system access risk calculation unit, wherein: the access subject trust degree fuzzy processing unit performs fuzzy processing according to the instant session trust value information output by the instant session trust value calculation unit to obtain a fuzzy trust class to which the access subject belongs; the single subject access risk fuzzy reasoning unit performs fuzzy reasoning processing according to the fuzzy trust class to which the access subject belongs output by the access subject trust degree fuzzy processing unit, the access environment risk fuzzy class output by the access environment risk fuzzy reasoning unit and the access asset value classification information output by the access asset value fuzzy reasoning unit according to the provided fuzzy rule set to obtain a single subject access risk fuzzy class; the centroid method defuzzification unit performs centroid method defuzzification processing according to the single subject access risk fuzzy class information output by the single subject access risk fuzzy reasoning unit to obtain a single subject access security risk value; and the entire system access risk calculation unit constructs an access risk matrix and performs summary calculation processing according to the single subject access security risk value output by the centroid method defuzzification unit to obtain an overall system access security risk value.

[0073] Through specific actual experiments, under the setting of simulating two types of access subjects, three types of access assets and access environments of a real information system, with the access subject long-term trust value being 0.5, the access context trust element weight vector being (0.3, 0.3, 0.25, 0.15), the discount coefficient being 1, the ordinary_score being 60 points, the special_score being 75 points, the access environment threat degree being 45 and the environmental protection measure level being 75, the above method can obtain the experimental data that the overall system access security risk value is 92.95.

[0074] In this embodiment, the access subject long-term trust value is 0.5, the corresponding trust element weight vector is (0.3, 0.3, 0.25, 0.15) and the discount coefficient is 1. It is set that the ordinary_score is 60 points and the special_score is 75 points.

[0075] Without loss of generality, in this embodiment, two access subjects subjectA and subjectB and three access assets asset1, asset2 and asset3 are used as examples.

[0076] Set subjectA is a legal administrator user in the enterprise, access within the specified time, use legal equipment to access, no trusted certificate; Set subjectB is a legal ordinary user in the enterprise, no access within the specified time, no access using legal equipment, carrying trusted certificate.

[0077] Set the security level of asset1 to medium, the importance degree to 60; Set the security level of asset2 to high, the importance degree to 80; Set the security level of asset3 to low, the importance degree to 30;

[0078] Set the access environment threat degree to 45, the environment protection measure level to 75.

[0079] Step 1 subject trust value calculation and logical reasoning module

[0080] Step 1.1 calculate the open relationship number vector.

[0081] The open relationship number vector of subjectA is (1, 1, 1, -1) T ; The open relationship number vector of subjectB is (1, -1, -1, 1) T ;

[0082] Step 1.2 calculate the modified trust value of the access subject.

[0083] The modified trust value of subjectA is 0.5 + 1 * (0.3, 0.3, 0.25, 0.15) * (1, 1, 1, -1) T = 1.2; The modified trust value of subjectB is 0.5 + 1 * (0.3, 0.3, 0.25, 0.15) * (1, -1, -1, 1) T = 0.4.

[0084] Step 1.3 calculate the instant session trust value of the access subject.

[0085] The normalized trust value of subjectA is The instant session trust value is 76.9; The normalized trust value of subjectB is The instant session trust value is 59.9.

[0086] Step 1.4 determine the ticket situation obtained by the access subject in the session.

[0087] The instant session trust value of subjectA is greater than special_score, which can obtain the ticket of the privilege level; The instant session trust value of subjectB is less than ordinary_score, which cannot obtain any ticket.

[0088] Step 1.5 determines the set of assets accessible to the access subject.

[0089] subjectA can access all assets of all security levels, i.e. the set of assets it can access is {asset1, asset2, asset3}. While the set of assets accessible to subjectB is empty.

[0090] Step 2 access environment risk degree calculation module

[0091] Step 2.1 fuzzifies the environment threat degree.

[0092] In this embodiment, the membership degree of the environment threat degree belonging to the high threat degree is 0, the membership degree belonging to the medium threat degree is 1, and the membership degree belonging to the low threat degree is 0.

[0093] Step 2.2 fuzzifies the environment protection measure level.

[0094] In this embodiment, the membership degree of the environment protection measure level belonging to the high protection level is 0.25, the membership degree belonging to the medium protection level is 0.25, and the membership degree belonging to the low protection level is 0.

[0095] Step 2.3 performs fuzzy reasoning according to the fuzzy rules to obtain the output membership degree corresponding to each rule.

[0096] In this embodiment, only rule 3 and rule 4 are fired, and the corresponding output membership degrees are 0.25 and 0.25, respectively.

[0097] Step 2.4 calculates the membership degree of the risk degree of the access environment belonging to each fuzzy class.

[0098] In this embodiment, the values of Low_Env_Risk_Deg, Medium_Env_Risk_Deg, and High_Env_Risk_Deg are 0, 0.25, and 0, respectively. That is, the membership degrees of the risk degree of the access environment belonging to the access environment risk degree "low", "medium", and "high" are 0, 0.25, and 0, respectively.

[0099] Step 3 access asset value fuzzy calculation module

[0100] Step 3.1 fuzzifies the importance of the accessed asset.

[0101] In this embodiment, asset1 has a degree of importance of 60, a membership degree of high degree of importance of 0, a membership degree of middle degree of importance of 1, and a membership degree of low degree of importance of 0. Asset2 has a degree of importance of 80, a membership degree of high degree of importance of 0.5, a membership degree of middle degree of importance of 0, and a membership degree of low degree of importance of 0. Asset3 has a degree of importance of 30, a membership degree of high degree of importance of 0, a membership degree of middle degree of importance of 0.5, and a membership degree of low degree of importance of 0.

[0102] Step 3.2. Perform fuzzy inference according to fuzzy rules to obtain the output membership degree corresponding to each rule.

[0103] For asset1, only rule 4 is fired, and its corresponding output membership degree is 1; for asset2, only rule 1 is fired, and its corresponding output membership degree is 0.5; for asset3, only rule 5 is fired, and its corresponding output membership degree is 0.5.

[0104] Step 3.3. Calculate the membership degree of the value of the accessed asset to each fuzzy class.

[0105] For asset1, the values of Low_Value_Deg, Medium_Value_Deg, and High_Value_Deg are 0, 1, and 0, respectively. That is, the membership degrees of asset1 to the value of the accessed asset being "low", "medium", and "high" are 0, 1, and 0, respectively.

[0106] For asset2, the values of Low_Value_Deg, Medium_Value_Deg, and High_Value_Deg are 0, 0, and 0.5, respectively. That is, the membership degrees of asset2 to the value of the accessed asset being "low", "medium", and "high" are 0, 0, and 0.5, respectively.

[0107] For asset3, the values of Low_Value_Deg, Medium_Value_Deg, and High_Value_Deg are 0.5, 0, and 0, respectively. That is, the membership degrees of asset3 to the value of the accessed asset being "low", "medium", and "high" are 0.5, 0, and 0, respectively.

[0108] Step 4. Integration of subject risk fuzzy calculation and access risk

[0109] According to step 1.5, the set of assets that subjectB can access is empty, i.e., the row corresponding to subjectB in the access risk matrix is all 0, so in this embodiment, the calculation related to subjectB is not considered.

[0110] Step 4.1 Fuzzification of the trust degree of the access subject

[0111] The trust value of subjectA is 76.9, which belongs to the membership degree of high trust level of 0.345, the membership degree of trust level of 0.155, and the membership degree of low trust level of 0.

[0112] Step 4.2 Fuzzy reasoning is implemented according to the fuzzy rules to obtain the output membership degree corresponding to each rule.

[0113] For subjectA and asset1, rule 14 and rule 23 are fired, and the corresponding output membership degrees are 0.155 and 0.25 respectively; for subjectA and asset2, rule 13 and rule 22 are fired, and the corresponding output membership degrees are 0.155 and 0.25 respectively; for subjectA and asset2, rule 15 and rule 24 are fired, and the corresponding output membership degrees are 0.155 and 0.25 respectively.

[0114] Step 4.3 De-fuzzification is realized by using the centroid method to obtain the access risk related to a single subject and a single access asset.

[0115] For subjectA and asset1, the values of Very_Low_Risk_Sum, Low_Risk_Sum, Medium_Risk_Sum, High_Risk_Sum and Very_High_Risk_Sum are 0, 0.25, 0.155, 0, 0 respectively. The value of Risk_Center is (0*10+0.25*30+0.155*50+0*70+0*90) / (0+0.25+0.155+0+0) = 37.65. That is, the output access risk is 37.65.

[0116] For subjectA and asset2, the values of Very_Low_Risk_Sum, Low_Risk_Sum, Medium_Risk_Sum, High_Risk_Sum and Very_High_Risk_Sum are 0, 0.25, 0.155, 0, 0 respectively. The value of Risk_Center is (0*10+0.25*30+0.155*50+0*70+0*90) / (0+0.25+0.155+0+0) = 37.65. That is, the output access risk is 37.65.

[0117] For subject A and asset 3, the values of Very_Low_Risk_Sum, Low_Risk_Sum, Medium_Risk_Sum, High_Risk_Sum and Very_High_Risk_Sum are 0.25, 0.155, 0, 0, 0 respectively. The value of Risk_Center is (0.25*10+0.155*30+0*50+0*70+0*90) / (0.25+0.155+0+0+0)=17.65. That is, the output access risk is 17.65.

[0118] Step 4.5 constructs the access risk matrix and calculates the access risk of the whole system: the calculated access risk matrix Sum all the values in the access risk matrix to obtain the access risk of the whole information system, which is 92.95.

[0119] Compared with the prior art, the method combines the access context security elements, the threat degree and protection measures of the access environment, the value of the accessed asset and other elements in the form of fuzzy reasoning, deduces and calculates the access security risk of the information system from the perspective of user access security, and fills the gap of the lack of a dedicated system access security risk evaluation method in the past.

[0120] The above specific embodiments can be adjusted in different ways by those skilled in the art without departing from the principles and purposes of the present application. The protection scope of the present application is subject to the claims and is not limited by the above specific embodiments. Each implementation within the scope is subject to the constraints of the present application.

Claims

1. A method for access risk assessment of an information system based on logical inference of access context, characterized in that, According to the security elements of the subject context, the comprehensive trust degree of the single subject, the risk degree of the access environment and the value degree of the actual access asset are fuzzified, fuzzily reasoned and de-fuzzified to obtain the session access risk of the single subject, and the session access risk of the whole information system is obtained through the summary processing; The session access risk of the whole information system is obtained by the following method: 1) Fuzzy processing of the trust degree of the access subject: combined with the real-time session trust value, the membership of the access subject to each class of fuzzy trust level is calculated and obtained, and the trust degree of the subject is divided into three categories: Low_Trust, Medium_Trust and High_Trust classes, which respectively represent that the user subject security is low, general and high, and the specific is: , , , wherein: x represents the session trust score of the access subject, ranging from 0 to 100 in a closed interval; 2) According to the fuzzy rules, the fuzzy reasoning is implemented to obtain the output membership degree corresponding to each rule, specifically including: 2.1) The risk brought by a single subject to the whole system is classified into five categories: Very_Low_Risk, Low_Risk, Medium_Risk, High_Risk and Very_High_Risk, representing the subject brings very low, low, medium, high and very high risk to the whole system, respectively, which are defined as follows: , , , , wherein: x represents the threat degree of the access environment, ranging in the closed interval of 0 to 100; 2.2) According to the membership degrees of the subject trust level belonging to the low trust level, the medium trust level and the high trust level, the membership degrees of the environment risk level belonging to the low environment risk level, the medium environment risk level and the high environment risk level, and the membership degrees of the asset value belonging to the low asset value, the medium asset value and the high asset value, the fuzzy reasoning is implemented according to the subject access risk fuzzy rule set to calculate the output membership degree of each ignited rule: wherein: represents the degree of membership of the subject trust level to a certain class (Low_Trust, Medium_Trust or High_Trust class), represents the degree of membership of the environmental risk level to a certain fuzzy class (Low_Env_Risk, Medium_Env_Risk or High_Env_Risk), represents the degree of membership of the value of the asset being accessed to a certain fuzzy class (Low_Value, Medium_Value or High_Value), represents the degree of membership of the access risk to a certain fuzzy class (Very_Low_Risk, Low_Risk, Medium_Risk, High_Risk or Very_High_Risk class); 3) The centroid method is used to realize de-fuzzification to obtain the access risk related to the single subject and the single access asset, specifically including: 3.1) Combine the membership of the same fuzzy class according to the inference results of multiple fuzzy rules: divide the access risk into five categories: Very_Low_Risk, Low_Risk, Medium_Risk, High_Risk and Very_High_Risk, and combine the membership of three categories respectively according to the results calculated by each rule in the previous step to obtain the total membership of each category, which is as follows: , , , , ; 3.2) The centroid method is used to calculate the asset value in combination with the total membership degrees of various types: the centroid value of the Very_Low_Risk class is Very_Low_Risk_Center 10; the centroid value of the Low_Risk class is Low_Risk_Center 30; the centroid value of the Medium_Risk class is Medium_Risk_Center 50; the centroid value of the High_Risk class is High_Risk_Center 70; the centroid value of the Very_High_Risk class is Very_High_Risk_Center 90; the asset value is calculated from the overall perspective to obtain the access risk related to the single subject and the single access asset, specifically: ； 4) An access risk matrix is constructed to calculate the access risk of the whole system: the access risk of the whole system is calculated according to the access risk calculation values of the two-by-two correlation of all subjects of the system and all access assets accessible by the subjects, specifically: 4.1) Constructing the access risk matrix: Each access subject in the system corresponds to a row in the access risk matrix. For all access subjects in the system, the set of assets that each access subject can access is traversed, and the access risk value between each pair of access subjects and access assets is calculated. When an access subject cannot access an asset, the access risk between the two is considered to be 0. After obtaining the access risk between each pair of access subjects and access assets, the following access risk matrix is constructed: wherein: m represents the number of access subjects, n represents the number of access assets, and r ij represents the access risk generated between access subject i and access asset j ; 4.2) The overall access risk of the system is calculated, and the sum of each element in the access risk matrix is obtained, that is, the access risk of the whole system.

2. An information system access risk assessment system implementing the method of claim 1, characterized by Including: The main body trust value calculation and logical reasoning module, the access environment risk degree calculation module, the access asset value calculation module and the main body risk fuzzy calculation and access risk integration module, wherein: the main body trust value calculation and logical reasoning module calculates the access subject accessible asset set according to the access subject long-term trust value, the access subject trust element weight vector and the trust score threshold value, the access environment risk degree calculation module calculates the access risk degree fuzzy calculation value according to the information system security situation awareness environment threat degree and the environment protection measure level, the access asset value calculation module calculates the accessed asset value fuzzy calculation value according to the accessed asset security level from the information system access asset database and the accessed asset importance, and the main body risk fuzzy calculation and access risk integration module calculates the entire system access risk according to the access subject trust value from the main body trust value calculation and logical reasoning, the access environment risk fuzzy calculation value from the access environment risk degree calculation and the multi-asset value fuzzy calculation value from the access asset value calculation. The access subject trust element vector comes from the information related to the access subject in the access process, including the access subject identity, access time, access device and access certificate.

3. The information system access risk assessment system of claim 2, wherein, The main body trust value calculation and logical reasoning module includes an open relationship coefficient vector calculation unit, a revised trust value calculation unit, an instant session trust value calculation unit and an access ticket granting unit, wherein: the open relationship coefficient vector calculation unit judges and classifies the access context security element information to obtain the open relationship coefficient vector assignment result, the revised trust value calculation unit performs corresponding vector multiplication calculation and addition processing according to the access subject long-term trust degree, the access context security element weight vector and the output information of the open relationship coefficient vector calculation unit to obtain the access subject revised trust value, the instant session trust value calculation unit performs normalization processing according to the output information of the revised trust value calculation unit to obtain the access subject instant session trust value, and the access ticket granting unit judges and classifies the output information of the instant session trust value calculation unit to obtain the access subject grantable ticket.

4. The information system access risk assessment system of claim 2, wherein, The access environment risk degree calculation module includes an access environment threat fuzzification processing unit, an access environment protection measure fuzzification processing unit and an access environment risk fuzzy reasoning unit, wherein: the access environment threat fuzzification processing unit performs fuzzification processing on the access environment threat degree information to obtain the access environment threat fuzzy category, the access environment protection measure fuzzification processing unit performs fuzzification processing on the access environment protection measure level information to obtain the access environment protection measure fuzzy category, and the access environment risk fuzzy reasoning unit performs fuzzy reasoning on the output information of the access environment threat fuzzification processing unit and the output information of the access environment protection measure fuzzification processing unit to obtain the access environment risk fuzzy category result.

5. The information system access risk assessment system of claim 2, wherein, The access asset value calculation module comprises an access asset importance fuzzy processing unit and an access asset value fuzzy reasoning unit, wherein the access asset importance fuzzy processing unit performs fuzzy processing according to the importance value information of the access asset to obtain an access asset importance fuzzy classification result, and the access asset value fuzzy reasoning unit performs fuzzy reasoning according to the information of the access asset importance fuzzy processing unit and the security level of the access asset to obtain an access asset value fuzzy classification result.

6. The information system access risk assessment system of claim 2, wherein, The main body risk fuzzy calculation and access risk integration module comprises an access main body trust degree fuzzy processing unit, a single main body access risk fuzzy reasoning unit, a centroid method defuzzification unit and an entire system access risk calculation unit, wherein the access main body trust degree fuzzy processing unit performs fuzzy processing according to the instant session trust value information output by the instant session trust value calculation unit to obtain a fuzzy trust class to which the access main body belongs, the single main body access risk fuzzy reasoning unit performs fuzzy reasoning processing according to the fuzzy trust class to which the access main body belongs output by the access main body trust degree fuzzy processing unit, the access environment risk fuzzy class output by the access environment risk fuzzy reasoning unit and the access asset value classification information output by the access asset value fuzzy reasoning unit according to the provided fuzzy rule set to obtain a single main body access risk fuzzy class, the centroid method defuzzification unit performs centroid method defuzzification processing according to the single main body access risk fuzzy class information output by the single main body access risk fuzzy reasoning unit to obtain a single main body access security risk value, and the entire system access risk calculation unit constructs an access risk matrix and performs summary calculation processing according to the single main body access security risk value output by the centroid method defuzzification unit information to obtain an overall system access security risk value.

7. The information system access risk assessment system of any of claims 2-6, wherein, The access main body long-term trust value is stored in an access main body database and is obtained by the following method: 1) Calculate the open relation number vector: according to whether the identity of the input access subject is legal, whether the access time is compliant, whether the device used for access is compliant, and whether the certificate carried during access is trusted, the open relation number vector is assigned a value, and when a certain value meets the provisions, the value of the corresponding open relation number is 1, otherwise it is -1; ; 2) Calculate the adjusted trust value of the access subject: wherein: TrustAdjust is the adjusted trust value of the access subject, LongtermTrust is the long-term trust value of the access subject, is the weight vector of the input trust elements of the access subject, wherein each value is between 0-1, and satisfies , is the open relationship number corresponding to each trust element dimension, the value range is {-1, 1}, and k is the discount coefficient, the value range is (0, 1); 3) Calculate the instant session trust value of the access subject: after the modified trust value is calculated, the trust value is normalized by using the following Sigmoid formula: Wherein: CurrentTrust is the normalized result, which is mapped to 0-100 as the instant session trust value of the access subject ; 4) determining the ticket condition obtained by the access main body in the current session, i.e., whether the access main body can obtain a ticket in the current session and what level of ticket can be obtained: The level of the ticket obtained by the access main body is determined by classifying the instant session trust value output in the above step and combining a trust score threshold value, wherein the trust score threshold value comprises ordinary_score and special_score, which respectively represent the ordinary access trust score and the privileged trust score required by the system, when the trust value is less than ordinary_score, it is considered that the access main body does not obtain any ticket; when the trust value is greater than or equal to ordinary_score and less than special_score, it is considered that the access main body obtains a ticket of an ordinary level; and when the trust value is greater than or equal to special_score, it is considered that the access main body obtains a ticket of a privileged level. 5) determining the set of assets accessible to the access subject, i.e. determining the range of the set of assets accessible to the access subject in the current session through the set of logical reasoning rules: inputting the identity of the access subject and the ticket obtained by the access subject into the logical reasoning of the set of logical reasoning rules, obtaining the security level of the assets accessible to the access subject, and further obtaining the set of assets accessible to the access subject.

8. The information system access risk assessment system of any of claims 2-6, wherein, The access risk degree fuzzy calculation value is obtained through the following steps: 1) Fuzzy the threat degree of the environment: Calculate and obtain the membership of the threat degree of each class of the access environment, and divide the threat degree of the access environment into three classes: Low_Threat, Medium_ Threat and High_Threat classes, which represent the threat of the access environment: low, medium and high, specifically: , , , wherein: x represents the threat degree of the access environment, ranging from 0 to 100 in a closed interval; 2) Fuzzy processing of the level of environmental protection measures: Calculate and obtain the membership of the level of protection measures of the access environment belonging to each fuzzy class, and divide the level of protection measures of the access environment into three categories: Low_Protection, Medium_Protection and High_Protection classes, which represent the level of protection measures of the access environment: low, medium and high, specifically: , , , wherein: x represents the level of protection measures of the access environment, ranging from 0 to 100 in a closed interval; 3) implementing fuzzy reasoning according to fuzzy rules, calculating and obtaining the output membership degree corresponding to each rule, specifically including: 3.1) According to the membership degree of the corresponding fuzzy class of the threat degree of the access environment and the level of environmental protection measures, the risk degree of the access environment is further divided into three categories: Low_Env_Risk, Medium_Env_Risk and High_Env_Risk, which represent the risk degree of the access environment: low, medium and high, respectively, and the specific values are: , , wherein: x represents the risk degree of the access environment, ranging from 0 to 100 in the closed interval. 3.2) according to the membership degrees of the environment threat degree belonging to the three fuzzy classes of low environment threat degree, medium environment threat degree and high environment threat degree, and the membership degrees of the environment protection measure level belonging to the three fuzzy classes of low environment protection measure level, medium environment protection measure level and high environment protection measure level, implementing fuzzy reasoning based on the set of access environment risk degree rules, the access environment threat degree class and the access environment protection measure level class, traversing the 9 rules in the table, calculating the membership degrees of the access environment risk fuzzy class for the ignited rules, specifically: wherein: μLThreatrepresents the membership degree of the environmental threat level belonging to a fuzzy class (Low_Threat, Medium_Threat or High_Threat), μLProtectionrepresents the membership degree of the environmental protection level belonging to a fuzzy class (Low_Protection, Medium_Protection or High_Protection), μLEnv_Riskrepresents the membership degree of the environmental risk level belonging to a fuzzy class (Low_Env_Risk, Medium_Env_Risk or High_Env_Risk); 4) Calculate the membership degree of the risk degree of the access environment belonging to each fuzzy class: combine the output membership degrees of each rule, and take the maximum value of the membership degrees of the same fuzzy class output by each rule as the membership degree of the risk degree of the access environment belonging to each fuzzy class, which is specifically: , , wherein Low_Env_Risk_Deg represents the membership degree of the risk degree of the access environment belonging to the low risk degree of the access environment, Medium_Env_Risk_Deg represents the membership degree of the risk degree of the access environment belonging to the general risk degree of the access environment, and High_Env_Risk_Deg represents the membership degree of the risk degree of the access environment belonging to the high risk degree of the access environment.

9. The information system access risk assessment system of any of claims 2-6, wherein, The accessed asset value fuzzy calculation value is obtained through the following way: 1) fuzzification of the importance of accessing assets: the importance degree of accessing assets is calculated and obtained to belong to the membership degree of each fuzzy class, and the importance degree of accessing assets is divided into three classes: Low_Importance, Medium_Importance and High_Importance classes, which represent the importance of accessing assets: low, medium and high, specifically: , , , wherein: x represents the importance degree of accessing assets, ranging from 0 to 100 in a closed interval; The security level of the accessed asset is divided into three categories: Low_SecurityLevel, Medium_SecurityLevel and High_SecurityLevel, which represent the asset security level: low, medium and high, respectively; 2) implementing fuzzy reasoning according to fuzzy rules, obtaining the output membership degree corresponding to each rule, specifically including: 2.1) The asset value is divided into three categories: Low_Value, Medium_Value and High_Value classes, representing the value of the accessed asset: low, medium and high, respectively, as follows: , , where: x represents the value of the accessed asset, ranging in the closed interval from 0 to 100; 2.2) according to the membership degrees of the security level of the accessed asset belonging to the three classes of low security level, medium security level and high security level, and the membership degrees of the importance of the accessed asset belonging to the three fuzzy classes of low importance, medium importance and high importance; then according to the set of access asset value level rules, combining the access asset security level and the access asset importance class, implementing fuzzy reasoning, traversing the 9 rules in the table, and calculating the membership degrees of the access asset value, specifically: wherein: represents the degree of membership of the asset security level to a certain class (Low_SecurityLevel, Medium_SecurityLevel or High_SecurityLevel), represents the degree of membership of the asset importance to a certain fuzzy class (Low_Importance, Medium_Importance or High_Importance), represents the degree of membership of the value of accessing the asset to a certain fuzzy class (Low_Value, Medium_Value or High_Value); 3) calculating the membership degrees of the access asset value belonging to each fuzzy class: according to the inference results of the fired fuzzy rules, taking the maximum value of the membership degrees of the same fuzzy class output by each rule as the membership degrees of the access asset value belonging to each fuzzy class, specifically , , wherein: Low_Value_Deg represents the membership degree of the access asset value belonging to the low access asset value, Medium_Value_Deg represents the membership degree of the access asset value belonging to the medium access asset value, and High_Value_Deg represents the membership degree of the access asset value belonging to the high access asset value.

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