A password policy self-adaption arrangement method, system, device and medium

Abstract

This invention provides a method, system, device, and medium for adaptive cryptographic policy orchestration. The method includes: assessing the business status based on the collected system architecture, business interaction channels, business interaction information, and interaction node information; matching the corresponding baseline cryptographic policy according to the business type; and solving for the optimal cryptographic primitive combination sequence as the final cryptographic policy based on the business status and the baseline cryptographic policy under a planning budget constraint. This invention achieves dynamic perception of complex security objectives through comprehensive analysis of the business status, and solves for the optimal cryptographic primitive combination sequence as the final cryptographic policy based on the business status and the baseline cryptographic policy under a planning budget constraint. This realizes adaptive cryptographic policy orchestration, which improves the encryption protection strength of the business data interaction process while minimizing the irrelevant resource overhead and related economic investment caused by protection operations, and has good application prospects.

Description

Technical Field

[0001] This invention relates to the field of network security protection technology, specifically to a method, system, device, and medium for adaptive cryptographic policy orchestration. Background Technology

[0002] Data interaction within business systems is fundamental for information exchange and state synchronization, carrying critical business information and a large amount of sensitive data. Therefore, ensuring the security of data interaction is a basic prerequisite for maintaining business continuity and meeting stability requirements. With the increasing complexity of business logic and the diversification of interaction environments, data security objectives exhibit multi-dimensional and complex characteristics. For example, sensitive data transmission scenarios require simultaneous confidentiality and non-repudiation of the data's origin, while state data acquisition scenarios prioritize ensuring data integrity and authenticity. However, a single cryptographic primitive (such as an encryption algorithm, hash function, or encryption strategy) can only provide relatively fixed security attributes and cannot meet complex security objectives. Therefore, cryptographic strategies are typically used to address such challenges. A cryptographic strategy structurally defines the required cryptographic services and their implementation methods. By explicitly specifying the selection, combination, and operation modes (such as encryption modes, key management schemes, and encryption protocols) of cryptographic primitives (such as encryption algorithms, encryption algorithm parameters, digital signatures, and message authentication codes), it transforms abstract, complex security objectives into concrete technical implementation solutions. Organizations can build adaptive data protection schemes for specific business interaction scenarios to meet their complex security needs. Therefore, how to design effective cryptographic strategies and apply them to appropriate scenarios has become a key research issue in ensuring data interaction security in complex business scenarios.

[0003] Existing methods for configuring and applying cryptographic policies include: cryptographic policy construction methods based on predefined templates, cryptographic policy construction methods based on dynamic encryption algorithms, cryptographic policy construction methods based on adaptive encryption algorithms, and cryptographic policy construction methods based on cryptographic agility. The problem with these methods is that the static or partially dynamic nature of the cryptographic policy leads to a mismatch between the encryption strength and the dynamic business interaction context. This can easily result in resource waste due to "over-protection" or security risks due to "under-protection." Summary of the Invention

[0004] To address the mismatch between encryption strength and dynamic business interaction context caused by the static or partially dynamic nature of cryptographic policies in existing technologies, and to avoid resource waste due to "over-protection" and security risks caused by "under-protection," this invention proposes an adaptive cryptographic policy orchestration method, comprising:

[0005] Based on the collected system architecture, business interaction paths, business interaction information, and interaction node information, assess the business status;

[0006] Match the corresponding baseline password policy based on the business type;

[0007] Based on the business status and baseline cryptographic strategy, the optimal cryptographic primitive combination sequence is solved under the planning budget constraint as the final cryptographic strategy.

[0008] Preferably, the step of evaluating the business status based on the collected system architecture, business interaction path, business interaction information, and interaction node information includes:

[0009] Based on the static information collected, including system architecture, business interaction paths, business interaction information, and interaction node information, the status of basic business is assessed offline.

[0010] From the start to the end of the business process, the business status is obtained by updating the basic business status obtained in the offline process online based on the dynamic information of node interaction generated by the dynamic changes in the collected system architecture, business interaction path, business interaction information and interaction node information.

[0011] Preferably, the offline assessment of the basic business status based on the collected static information of system architecture, business interaction paths, business interaction information, and interaction node information includes:

[0012] Trust levels are calculated by combining a pre-set trust assessment model with information from interactive nodes.

[0013] Based on data mining technology, a preliminary set of triggering rules for associated business and subsequent business most likely to occur after the business ends is extracted from the business interaction information.

[0014] By combining expert knowledge or domain-specific knowledge graphs, the candidate set of triggering rules for associated and subsequent services is filtered to obtain the set of triggering rules for associated and subsequent services as the offline service information evaluation result;

[0015] Based on the system architecture and business interaction channels, the trust level and / or offline business information evaluation results are adjusted according to the domain-specific language model or expert knowledge to obtain the basic business status of offline evaluation.

[0016] The offline assessment is performed when the system architecture, business interaction path, or interaction node information changes, or periodically at time intervals set according to the business type; the trust assessment model includes one or more of the following: weighted summation or machine learning classifier; the data mining technology includes one or more of the following: sequence pattern mining, time series rule learning, or process mining.

[0017] Preferably, the step of updating the basic business status obtained in the offline process online to obtain the business status during the process from the start to the end of the business, based on the dynamically changing node interaction information generated by the collected system architecture, business interaction path, business interaction information, and interaction node information, includes:

[0018] During the process from the start to the end of the business, collect dynamic information of node interactions related to the current data interaction node, and update the trust level by combining the dynamic information of node interactions, the interaction node information used in the offline evaluation, and the trust level of the offline evaluation through a preset trust evaluation model.

[0019] Collect dynamic information about business interactions related to the current business interaction. Based on the evaluation results of offline business information obtained from offline evaluation, use the dynamic information about business interactions as triggering conditions to evaluate the set of triggering rules for associated business and subsequent business in the evaluation results of offline business information. Finally, obtain the set of associated business and the set of subsequent business associated with the current business to form the business interaction context.

[0020] The updated trust level and business interaction context are used as the business state.

[0021] Among them, online updates are performed continuously or periodically; the node interaction dynamic information includes one or more of the following: the deviation between the current behavior pattern and the historical behavior pattern in the interaction node information collected during the offline evaluation process, or the real-time network environment in which the node is located; the business interaction dynamic information includes one or more of the following: the interaction time of the current business, the frequency of interaction, or the sensitivity of interaction data.

[0022] Preferably, the step of solving for the optimal cryptographic primitive combination sequence as the final cryptographic strategy based on business status and baseline cryptographic strategy under planning budget constraints includes:

[0023] Based on the business status and baseline cryptographic strategy, and under the constraint of a planning budget, a planning approach is used to solve for the optimal cryptographic primitive combination sequence as the final cryptographic strategy; or

[0024] Based on the business status and baseline cryptographic strategy, and under the constraint of the planning budget, a multi-objective optimization approach is adopted to solve for the optimal cryptographic primitive combination sequence as the final cryptographic strategy.

[0025] Preferably, the step of solving for the optimal cryptographic primitive combination sequence as the final cryptographic strategy based on business status and baseline cryptographic strategy, under planning budget constraints, using a planning approach, includes:

[0026] Based on the aforementioned business interaction channels, system architecture, business status, and baseline cryptographic policies, formal reasoning is performed using business domain knowledge rules to obtain a composite security objective.

[0027] Starting with the composite security objective corresponding to the baseline cryptographic strategy, and ending with the composite security objective derived through reasoning, the available cryptographic primitives are used as planning actions, the resource consumption cost of the cryptographic primitives is used as the optimization objective, and the overhead generated by the execution of the cryptographic strategy is used as the planning budget. The optimal sequence of cryptographic primitive combinations is then obtained through planning and solving, which is taken as the final cryptographic strategy.

[0028] Preferably, the composite security objective, obtained by using formal reasoning based on the business interaction path, system architecture, business status, and baseline cryptographic policy, and employing business domain knowledge rules, includes:

[0029] Based on the business interaction path, system architecture, business state, and baseline cryptographic policy, formal reasoning is performed using business domain knowledge rules to determine whether the baseline cryptographic policy is sufficiently secure under the current business interaction path and system architecture configuration, given the trust level of the interaction node and the current business interaction context.

[0030] If so, the security objective corresponding to the baseline cryptographic strategy shall be used as the composite security objective;

[0031] Otherwise, a composite security objective is obtained based on the security objective corresponding to the baseline cryptographic policy, which is enhanced or adjusted.

[0032] The formal reasoning methods include: domain-specific language models or domain-specific knowledge graphs, wherein the business domain knowledge rules include rules that are insufficient in security and / or require security enhancement.

[0033] Preferably, the composite security objective is declarative.

[0034] Preferably, the step of using the composite security objective corresponding to the baseline cryptographic policy as the planning starting point, the composite security objective obtained through reasoning as the planning endpoint, the available cryptographic primitives as the planning actions, the resource consumption cost of the cryptographic primitives as the optimization objective, and the overhead generated by the execution of the cryptographic policy as the planning budget, to solve for the optimal sequence of cryptographic primitive combinations as the final cryptographic policy, includes:

[0035] Taking the composite security objective corresponding to the baseline cryptographic policy as the planning starting point, the composite security objective obtained through reasoning as the planning endpoint, the available cryptographic primitives as the planning actions, the resource consumption cost of the cryptographic primitives as the optimization objective, and the overhead generated by the execution of the cryptographic policy as the planning budget, an adaptive optimization model for the cryptographic policy is constructed.

[0036] The optimal sequence of cryptographic primitive combinations is obtained by solving the adaptive optimization model of the cryptographic strategy, which is then used as the final cryptographic strategy.

[0037] Preferably, the step of constructing an adaptive optimization model for a cryptographic policy, using the composite security objective corresponding to the baseline cryptographic policy as the planning starting point, the inferred composite security objective as the planning endpoint, the available cryptographic primitives as the planning actions, the resource consumption cost of the cryptographic primitives as the optimization objective, and the overhead generated by the execution of the cryptographic policy as the planning budget, includes:

[0038] All possible states of the system are set as a state space set. Each state contains a set of security objectives that the current cryptographic policy can satisfy. The baseline cryptographic policy is analyzed to obtain the corresponding composite security objectives as the initial state for planning.

[0039] The cryptographic primitives are treated as state transition operations and a set of operations is constructed. The correspondence between each state transition operation and the resource overhead vector is set as a cost function.

[0040] State transition operations on the state space set are defined as state transition functions, and hard constraints that must be satisfied for successful execution of the operation are set according to the logical relationships in the pre-built cryptographic primitive knowledge base.

[0041] The state space set, the composite security objective corresponding to the baseline cryptographic policy, the operation set, the state transition function and the cost function are formally encoded into a state transition model, and the composite security objective obtained through reasoning is set as the endpoint state of the state transition model.

[0042] In the state transition model, the objective function is constructed with the goal of minimizing the total resource consumption cost of cryptographic primitives in the state transition operation sequence from the initial state to the final state. The constraints are that the overhead generated by the execution of the cryptographic policy is less than the planning budget, and that each cryptographic primitive in the state transition operation sequence must satisfy the hard constraints. Thus, an adaptive optimization model for the cryptographic policy is constructed.

[0043] Preferably, the step of solving for the optimal cryptographic primitive combination sequence as the final cryptographic strategy based on business status and baseline cryptographic strategy, under planning budget constraints, using a multi-objective optimization approach, includes:

[0044] Based on the trust level in the business state, the business interaction context in the business state is integrated, and formal reasoning is performed using business domain knowledge rules to dynamically generate the security goals to be achieved in this interaction. The level that each security goal needs to be achieved is quantified based on expert knowledge.

[0045] By leveraging expert knowledge to score the security assets involved in business interactions, the security value to be protected by cryptographic strategies is quantified. The resource overhead and security value of each cryptographic primitive in the cryptographic primitive knowledge base are normalized, and the message increment is used as the budget.

[0046] To maximize security value while minimizing the resource overhead caused by applying cryptographic primitive combination sequences, a multi-objective optimization model for cryptographic policies is constructed, with the constraint that the sum of message increments of all operations in the cryptographic primitive combination sequence cannot exceed the budget, and the baseline cryptographic policy as the initial boundary condition.

[0047] The multi-objective optimization model of the cryptographic strategy is solved using a two-level optimization method to obtain the optimal sequence of cryptographic primitive combinations as the final cryptographic strategy.

[0048] The formal reasoning methods include: domain-specific language models or domain-specific knowledge graphs; the business domain knowledge rules include rules that are insufficient in security and / or require security enhancement.

[0049] Preferably, the process of constructing the cryptographic primitive knowledge base includes:

[0050] Extract the complete set of cryptographic primitives required by the current system business, and construct a cryptographic primitive library usable by the system based on the complete set of cryptographic primitives. Each cryptographic primitive in the cryptographic primitive library includes: a unique identifier, an operation type signature, and resource overhead.

[0051] Based on business domain knowledge, fine-tune the domain-specific language model, or based on business domain knowledge graphs and cryptographic ontology, combine logical reasoning or graph neural network technology to analyze and extract the logical relationships between members in the cryptographic primitive library. The logical relationships include: security strength partial order relationship, dependency relationship and security goal relationship.

[0052] The logical relationships are described as logical rules, and a cryptographic primitive knowledge base is constructed in a structured manner by combining them with a preset set of security objectives.

[0053] Preferably, the step of matching the corresponding baseline cryptographic policy according to the business type includes:

[0054] When no baseline cryptographic policy pool exists, return an empty set as the baseline cryptographic policy.

[0055] When a baseline cryptographic policy pool exists, the most basic baseline cryptographic policy applicable to that business type is found in the baseline cryptographic policy pool based on the business type.

[0056] Preferably, the process of constructing the baseline cryptographic policy pool includes:

[0057] Defined based on expert knowledge, a baseline cryptographic policy pool is obtained; or

[0058] By employing multiple analysis methods and their arbitrary combinations, candidate baseline cryptographic strategy pools are obtained, and the candidate baseline cryptographic strategy pools are adjusted based on expert knowledge or domain-specific knowledge graphs to generate a baseline cryptographic strategy pool.

[0059] The analysis methods include, but are not limited to, the following: analysis based on historical operation logs, analysis based on existing code, or analysis based on expert knowledge and / or design documents.

[0060] Based on the same inventive concept, the present invention also provides a cryptographic policy adaptive orchestration system, wherein the system is deployed in the dominant node in a master-slave node device group, or in any node in an equal node device group, and the system includes: a state module, a baseline cryptographic policy module, and a cryptographic policy module.

[0061] The status module is used to evaluate the business status based on the collected system architecture, business interaction path, business interaction information and interaction node information;

[0062] The baseline cryptographic policy module is used to match the corresponding baseline cryptographic policy according to the business type;

[0063] The cryptographic strategy module is used to solve for the optimal cryptographic primitive combination sequence as the final cryptographic strategy based on the business status and baseline cryptographic strategy, under the planning budget constraint.

[0064] Preferably, the status module evaluates the business status based on the collected system architecture, business interaction path, business interaction information, and interaction node information, including:

[0065] Based on the static information collected, including system architecture, business interaction paths, business interaction information, and interaction node information, the status of basic business is assessed offline.

[0066] From the start to the end of the business process, the business status is obtained by updating the basic business status obtained in the offline process online based on the dynamic information of node interaction generated by the dynamic changes in the collected system architecture, business interaction path, business interaction information and interaction node information.

[0067] Preferably, the status module evaluates the basic business status offline based on static information collected from the system architecture, business interaction paths, business interaction information, and interaction node information, including:

[0068] Trust levels are calculated by combining a pre-set trust assessment model with information from interactive nodes.

[0069] Based on data mining technology, a preliminary set of triggering rules for associated business and subsequent business most likely to occur after the business ends is extracted from the business interaction information.

[0070] By combining expert knowledge or domain-specific knowledge graphs, the candidate set of triggering rules for associated and subsequent services is filtered to obtain the set of triggering rules for associated and subsequent services as the offline service information evaluation result;

[0071] Based on the system architecture and business interaction channels, the trust level and / or offline business information evaluation results are adjusted according to the domain-specific language model or expert knowledge to obtain the basic business status of offline evaluation.

[0072] The offline assessment is performed when the system architecture, business interaction path, or interaction node information changes, or periodically at time intervals set according to the business type; the trust assessment model includes one or more of the following: weighted summation or machine learning classifier; the data mining technology includes one or more of the following: sequence pattern mining, time series rule learning, or process mining.

[0073] Preferably, during the process from the start to the end of the service, the status module updates the basic service status obtained offline to obtain the service status online based on the dynamic information of node interaction generated by the dynamic changes in the collected system architecture, service interaction path, service interaction information, and interaction node information, including:

[0074] During the process from the start to the end of the business, collect dynamic information of node interactions related to the current data interaction node, and update the trust level by combining the dynamic information of node interactions, the interaction node information used in the offline evaluation, and the trust level of the offline evaluation through a preset trust evaluation model.

[0075] Collect dynamic information about business interactions related to the current business interaction. Based on the evaluation results of offline business information obtained from offline evaluation, use the dynamic information about business interactions as triggering conditions to evaluate the set of triggering rules for associated business and subsequent business in the evaluation results of offline business information. Finally, obtain the set of associated business and the set of subsequent business associated with the current business to form the business interaction context.

[0076] The updated trust level and business interaction context are used as the business state.

[0077] Among them, online updates are performed continuously or periodically; the node interaction dynamic information includes one or more of the following: the deviation between the current behavior pattern and the historical behavior pattern in the interaction node information collected during the offline evaluation process, or the real-time network environment in which the node is located; the business interaction dynamic information includes one or more of the following: the interaction time of the current business, the frequency of interaction, or the sensitivity of interaction data.

[0078] Preferably, the cryptographic policy module is specifically used for:

[0079] Based on the business status and baseline cryptographic strategy, and under the constraint of a planning budget, a planning approach is used to solve for the optimal cryptographic primitive combination sequence as the final cryptographic strategy; or

[0080] Based on the business status and baseline cryptographic strategy, and under the constraint of the planning budget, a multi-objective optimization approach is adopted to solve for the optimal cryptographic primitive combination sequence as the final cryptographic strategy.

[0081] Preferably, the cryptographic strategy module, based on the business state and baseline cryptographic strategy, and under budget constraints, uses a planning approach to solve for the optimal cryptographic primitive combination sequence as the final cryptographic strategy, including:

[0082] Based on the aforementioned business interaction channels, system architecture, business status, and baseline cryptographic policies, formal reasoning is performed using business domain knowledge rules to obtain a composite security objective.

[0083] Starting with the composite security objective corresponding to the baseline cryptographic strategy, and ending with the composite security objective derived through reasoning, the available cryptographic primitives are used as planning actions, the resource consumption cost of the cryptographic primitives is used as the optimization objective, and the overhead generated by the execution of the cryptographic strategy is used as the planning budget. The optimal sequence of cryptographic primitive combinations is then obtained through planning and solving, which is taken as the final cryptographic strategy.

[0084] Preferably, the cryptographic policy module, based on the business interaction path, system architecture, business status, and baseline cryptographic policy, uses business domain knowledge rules for formal reasoning to obtain a composite security objective, including:

[0085] Based on the business interaction path, system architecture, business state, and baseline cryptographic policy, formal reasoning is performed using business domain knowledge rules to determine whether the baseline cryptographic policy is sufficiently secure under the current business interaction path and system architecture configuration, given the trust level of the interaction node and the current business interaction context.

[0086] If so, the security objective corresponding to the baseline cryptographic strategy shall be used as the composite security objective;

[0087] Otherwise, a composite security objective is obtained based on the security objective corresponding to the baseline cryptographic policy, which is enhanced or adjusted.

[0088] The formal reasoning methods include: domain-specific language models or domain-specific knowledge graphs, wherein the business domain knowledge rules include rules that are insufficient in security and / or require security enhancement.

[0089] Preferably, the composite security objective in the cryptographic policy module is declarative.

[0090] Preferably, the cryptographic strategy module takes the composite security objective corresponding to the baseline cryptographic strategy as the planning starting point, the composite security objective obtained through reasoning as the planning endpoint, the available cryptographic primitives as the planning actions, the resource consumption cost of the cryptographic primitives as the optimization objective, and the overhead generated by the execution of the cryptographic strategy as the planning budget, and performs planning and solving to obtain the optimal sequence of cryptographic primitive combinations as the final cryptographic strategy, including:

[0091] Taking the composite security objective corresponding to the baseline cryptographic policy as the planning starting point, the composite security objective obtained through reasoning as the planning endpoint, the available cryptographic primitives as the planning actions, the resource consumption cost of the cryptographic primitives as the optimization objective, and the overhead generated by the execution of the cryptographic policy as the planning budget, an adaptive optimization model for the cryptographic policy is constructed.

[0092] The optimal sequence of cryptographic primitive combinations is obtained by solving the adaptive optimization model of the cryptographic strategy, which is then used as the final cryptographic strategy.

[0093] Preferably, the cryptographic policy module takes the composite security objective corresponding to the baseline cryptographic policy as the planning starting point, the inferred composite security objective as the planning endpoint, the available cryptographic primitives as the planning actions, the resource consumption cost of the cryptographic primitives as the optimization objective, and the overhead generated by the execution of the cryptographic policy as the planning budget, and constructs an adaptive optimization model for the cryptographic policy, including:

[0094] All possible states of the system are set as a state space set. Each state contains a set of security objectives that the current cryptographic policy can satisfy. The baseline cryptographic policy is analyzed to obtain the corresponding composite security objectives as the initial state for planning.

[0095] The cryptographic primitives are treated as state transition operations and a set of operations is constructed. The correspondence between each state transition operation and the resource overhead vector is set as a cost function.

[0096] State transition operations on the state space set are defined as state transition functions, and hard constraints that must be satisfied for successful execution of the operation are set according to the logical relationships in the pre-built cryptographic primitive knowledge base.

[0097] The state space set, the composite security objective corresponding to the baseline cryptographic policy, the operation set, the state transition function and the cost function are formally encoded into a state transition model, and the composite security objective obtained through reasoning is set as the endpoint state of the state transition model.

[0098] In the state transition model, the objective function is constructed with the goal of minimizing the total resource consumption cost of cryptographic primitives in the state transition operation sequence from the initial state to the final state. The constraints are that the overhead generated by the execution of the cryptographic policy is less than the planning budget, and that each cryptographic primitive in the state transition operation sequence must satisfy the hard constraints. Thus, an adaptive optimization model for the cryptographic policy is constructed.

[0099] Preferably, the cryptographic strategy module, based on the business state and baseline cryptographic strategy, and under budget constraints, employs a multi-objective optimization approach to solve for the optimal cryptographic primitive combination sequence as the final cryptographic strategy, including:

[0100] Based on the trust level in the business state, the business interaction context in the business state is integrated, and formal reasoning is performed using business domain knowledge rules to dynamically generate the security goals to be achieved in this interaction. The level that each security goal needs to be achieved is quantified based on expert knowledge.

[0101] By leveraging expert knowledge to score the security assets involved in business interactions, the security value to be protected by cryptographic strategies is quantified. The resource overhead and security value of each cryptographic primitive in the cryptographic primitive knowledge base are normalized, and the message increment is used as the budget.

[0102] To maximize security value while minimizing the resource overhead caused by applying cryptographic primitive combination sequences, a multi-objective optimization model for cryptographic policies is constructed, with the constraint that the sum of message increments of all operations in the cryptographic primitive combination sequence cannot exceed the budget, and the baseline cryptographic policy as the initial boundary condition.

[0103] The multi-objective optimization model of the cryptographic strategy is solved using a two-level optimization method to obtain the optimal sequence of cryptographic primitive combinations as the final cryptographic strategy.

[0104] The formal reasoning methods include: domain-specific language models or domain-specific knowledge graphs; the business domain knowledge rules include rules that are insufficient in security and / or require security enhancement.

[0105] Preferably, the process of constructing the cryptographic primitive knowledge base in the cryptographic strategy module includes:

[0106] Extract the complete set of cryptographic primitives required by the current system business, and construct a cryptographic primitive library usable by the system based on the complete set of cryptographic primitives. Each cryptographic primitive in the cryptographic primitive library includes: a unique identifier, an operation type signature, and resource overhead.

[0107] Based on business domain knowledge, fine-tune the domain-specific language model, or based on business domain knowledge graphs and cryptographic ontology, combine logical reasoning or graph neural network technology to analyze and extract the logical relationships between members in the cryptographic primitive library. The logical relationships include: security strength partial order relationship, dependency relationship and security goal relationship.

[0108] The logical relationships are described as logical rules, and a cryptographic primitive knowledge base is constructed in a structured manner by combining them with a preset set of security objectives.

[0109] Preferably, the baseline cryptographic policy module matches the corresponding baseline cryptographic policy according to the service type, including:

[0110] When no baseline cryptographic policy pool exists, return an empty set as the baseline cryptographic policy.

[0111] When a baseline cryptographic policy pool exists, the most basic baseline cryptographic policy applicable to that business type is found in the baseline cryptographic policy pool based on the business type.

[0112] Preferably, the process of constructing the baseline cryptographic policy pool in the baseline cryptographic policy module includes:

[0113] Defined based on expert knowledge, a baseline cryptographic policy pool is obtained; or

[0114] By employing multiple analysis methods and their arbitrary combinations, candidate baseline cryptographic strategy pools are obtained, and the candidate baseline cryptographic strategy pools are adjusted based on expert knowledge or domain-specific knowledge graphs to generate a baseline cryptographic strategy pool.

[0115] The analysis methods include, but are not limited to, the following: analysis based on historical operation logs, analysis based on existing code, or analysis based on expert knowledge and / or design documents.

[0116] In another aspect, the present invention also provides an electronic device, comprising: at least one processor and a memory;

[0117] The memory is used to store one or more programs;

[0118] When the one or more programs are executed by the one or more processors, an adaptive orchestration method for cryptographic policies as described above is implemented.

[0119] In another aspect, the present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed, it implements a cryptographic strategy adaptive arrangement method as described above.

[0120] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0121] This invention provides a method, system, device, and medium for adaptive cryptographic policy orchestration. The method includes: assessing the business status based on the collected system architecture, business interaction channels, business interaction information, and interaction node information; matching the corresponding baseline cryptographic policy according to the business type; and solving for the optimal cryptographic primitive combination sequence as the final cryptographic policy based on the business status and the baseline cryptographic policy under a planning budget constraint. This invention achieves dynamic perception of complex security objectives through comprehensive analysis of the business status, and solves for the optimal cryptographic primitive combination sequence as the final cryptographic policy based on the business status and the baseline cryptographic policy under a planning budget constraint. This realizes adaptive cryptographic policy orchestration, which improves the encryption protection strength of the business data interaction process while minimizing the irrelevant resource overhead and related economic investment caused by protection operations, and has good application prospects. Attached Figure Description

[0122] Figure 1 A flowchart of an adaptive orchestration method for cryptographic strategies provided by the present invention;

[0123] Figure 2 A flowchart illustrating an example of an adaptive orchestration method for cryptographic policies provided by this invention;

[0124] Figure 3 This invention provides a schematic diagram of the structure of an adaptive orchestration system for cryptographic policies.

[0125] Figure 4 This is a schematic diagram of an electronic device structure provided by the present invention. Detailed Implementation

[0126] This invention proposes a method, system, device, and medium for adaptive cryptographic policy orchestration, continuously building cryptographic policies that adapt to the business data interaction environment. This method aims to address the mismatch between encryption strength and dynamic business interaction context caused by the static or partially dynamic nature of cryptographic policies in existing technologies, avoiding resource waste caused by "over-protection" and security risks arising from "insufficient protection."

[0127] To address the limitations of existing technologies, the present invention makes the following improvements:

[0128] (1) Regarding the issues of "overprotection" and "underprotection": Existing methods cannot adjust the encryption protection strength according to the dynamically changing interaction environment, resulting in wasted resources (overprotection) or security risks (underprotection). This invention effectively avoids the above problems by continuously building a cryptographic strategy that dynamically adapts to the business data interaction environment.

[0129] (2) Addressing the issues of insufficient local dynamism and systematic protection: Existing methods based on dynamic encryption / adaptive encryption and methods based on cryptographic agility are limited to the dynamism of a single cryptographic primitive or a local replacement of the algorithm. They lack an overall perception of business states such as trust relationships and business interaction contexts, making it difficult to achieve systematic adaptive encryption protection. This invention achieves dynamic perception of complex security objectives by comprehensively analyzing business states. By describing the perception results with declarative logical statements rather than based on subjective quantitative scoring, the subjectivity of the orchestration results is reduced, providing better support for continuous, proactive, and fine-grained adjustment of cryptographic primitive combinations.

[0130] Specifically, this invention employs a constraint programming method for modeling and solving. This method takes the security target state corresponding to the baseline cryptographic policy as the starting point, the composite security target to be satisfied under the current business state as the planning endpoint, and the available cryptographic primitives as planning actions. Simultaneously, it uses the resource consumption cost of the cryptographic primitives as the optimization objective and the communication overhead generated by policy execution as the planning budget, automatically generating the optimal sequence of cryptographic primitive combinations. Through this approach, the invention can dynamically update the original baseline cryptographic policy, thereby providing effective protection for subsequent business data interaction processes adapted to the current business state.

[0131] Constraint programming refers to the process of searching for a feasible or optimal solution (such as a sequence of actions or a resource allocation scheme) for a problem under a set of given constraints.

[0132] In this invention, the cryptographic primitives refer to the basic components used in cryptography to build more complex encryption protection systems, such as symmetric encryption algorithms, public-key encryption algorithms, encryption algorithm parameters, hash functions, digital signature algorithms, timestamps, random numbers, etc.

[0133] The difference between cryptographic strategies and encryption strategies lies in their focus: Encryption strategies primarily address a single data encryption task, defining the encryption parameters required to execute that task. Their structure is relatively fixed, and the order of elements within the strategy is irrelevant to the encryption result. Cryptographic strategies, on the other hand, are sequences of multiple cryptographic primitives, with the core being the arrangement and combination of these primitives. For example, "hashing first, then encrypting" versus "encrypting first, then hashing" will produce drastically different security outcomes. Therefore, cryptographic strategies encompass not only encryption strategies but also other cryptographic operations (such as message authentication codes and digital signatures) and their combinations and operational modes, strictly limiting their combination order to achieve a more systematic approach to encryption protection.

[0134] The distinction between encryption protection and data encryption: Data encryption is a single technical means that aims to transform plaintext data into ciphertext through cryptographic algorithms to achieve data confidentiality. Encryption protection, on the other hand, is a broader and more comprehensive concept. It is not limited to encryption but refers to providing multi-dimensional security guarantees through the combination and synergy of a series of cryptographic primitives. For example, a complete encryption protection scheme might include encrypting data (ensuring confidentiality), using hash functions (ensuring integrity), and employing digital signatures (ensuring authenticity and non-repudiation).

[0135] The security objective refers to the expected state that a system or information must meet in order to resist threats and reduce risks during processing, storage and transmission. It typically includes objectives such as confidentiality, integrity, availability, authenticity, non-repudiation and combinations thereof.

[0136] The composite security objective requires a combination of two or more security objectives to be met simultaneously. For example, sensitive data transmission scenarios require both data confidentiality and non-repudiation of the source, while status data acquisition scenarios prioritize ensuring data integrity and authenticity.

[0137] Example 1:

[0138] This invention provides an adaptive orchestration method for cryptographic policies, suitable for business data interaction scenarios requiring dynamic, multi-granular, and systematic security protection, specifically as follows: Figure 1 As shown, it includes:

[0139] Step S1: Based on the collected system architecture, business interaction paths, business interaction information, and interaction node information, assess the business status.

[0140] Step S2: Match the corresponding baseline cryptographic policy according to the business type;

[0141] Step S3: Based on the business status and baseline cryptographic strategy, solve for the optimal cryptographic primitive combination sequence as the final cryptographic strategy under the planning budget constraint.

[0142] The adaptive cryptographic policy orchestration method provided by this invention is applicable to distributed data interaction architectures with a central data controller and one or more data interaction nodes. In this architecture, the central data controller (such as a trusted server, cloud platform, power distribution master station, or edge computing terminal) is responsible for storing a baseline security policy pool defined by a security administrator and interacting with various data interaction nodes. Data interaction nodes (such as edge devices, IoT devices, etc.) are responsible for collecting data and interacting with the central data controller.

[0143] In this architecture, the central data controller is typically located within a secure domain and is fully trusted; however, data interaction nodes are widely distributed and may be deployed in complex and diverse physical and network environments. In particular, some data interaction nodes may be under the jurisdiction of third-party organizations or connected to untrusted public networks, and the inherent trustworthiness of their behavior and state cannot be guaranteed in advance. This uncertainty in trust relationships poses a significant challenge to static, passive cryptographic strategies. Therefore, dynamically assessing the real-time trust relationships of data interaction nodes before each data interaction is a crucial prerequisite for achieving refined and adaptive security protection.

[0144] Step S1 specifically includes:

[0145] (1) Step 1: Offline construction of a cryptographic primitive knowledge base

[0146] This step, performed offline, aims to build a knowledge base to support subsequent real-time decision-making. First, a complete set of cryptographic primitives required for the business is extracted from existing system resources (such as logs, code implementations, or design documents). Using domain-specific knowledge models or ontology, the logical relationships between cryptographic primitives are analyzed and reasoned, and structured modeling is performed to form a cryptographic primitive knowledge base. Simultaneously, based on experimental testing, historical interaction records, or expert knowledge, the security overhead of each cryptographic primitive in the knowledge base is quantitatively labeled.

[0147] This step is executed offline once in the background management system of the central data controller or business system during the initial deployment of this invention, aiming to provide basic knowledge support for subsequent online, real-time strategy decisions. Step 1 can be executed at the beginning of S1, or it can be executed in advance before S1. Step 1 is as follows:

[0148] 1) Extracting the cryptographic primitive library. First, extract the complete set of cryptographic primitives required for the current system's business. The construction methods include, but are not limited to, the following methods and any combination thereof: ① Based on historical operation logs: extract the operation / protocol fields from the log entries, and then abstract the set of cryptographic primitives corresponding to the operations or decompose the combination of cryptographic primitives required for the protocol; ② Based on existing code analysis: use methods such as static analysis to reconstruct the data interaction process from the existing code implementation, obtain the specific protection operations, and record them as cryptographic primitives; ③ Based on expert knowledge or design documents: directly use expert knowledge to define the set of cryptographic primitives, or use natural language processing technology to identify and extract relevant natural language descriptions from the design documents.

[0149] Based on the complete set of cryptographic primitives obtained by the above method, a system-usable cryptographic primitive library is defined. Each cryptographic primitive For a triple .in, A unique identifier representing the current cryptographic primitive, such as: encryption algorithm (e.g., SM1, SM2, SM3, SM4, Z encryption algorithm, etc.), Message Authentication Code (MAC), timestamp, temporary key, etc.; This is the operation type signature of the primitive, representing the safe state transitions of its input and output, denoted as... ,Right now This indicates the input of the primitive. This indicates the output of the primitive. This indicates the resource consumption incurred in executing cryptographic primitives.

[0150] 2) Annotate the overhead of the cryptographic primitive library. The resource overhead of cryptographic primitives can be obtained based on the test results of the experimental environment provided by the manufacturer, historical business interaction records, expert knowledge, etc. This represents a mapping function from a library of cryptographic primitives to resource overhead, indicating the resource consumption incurred in executing a cryptographic primitive. Given a primitive... , The return value is a multi-dimensional resource cost vector. , respectively, represent the computational overhead of executing the primitive. (This represents the CPU, memory, and other resources required to perform cryptographic primitive operations), communication overhead. (Indicates the message increment resulting from executing cryptographic primitives), storage overhead (Storage resources consumed by the keys, certificates, public key infrastructure, and other related data required to execute cryptographic primitives), latency overhead. (Indicates the time cost and energy cost incurred in executing cryptographic primitives) (When interaction node resources are limited, computational overhead can also be directly described as the consumption of energy resources such as electricity).

[0151] 3) Extracting primitive relations. The central data controller, based on domain-specific knowledge, fine-tunes the domain-specific language model, or, based on domain-specific knowledge graphs, cryptographic ontology, and other technologies such as logical reasoning or graph neural networks, automatically analyzes and extracts the cryptographic primitive database. Logical relationships between members For example, the model will understand statements such as "the encryption strength of the Z algorithm is higher than that of SM1" and "the prerequisite for adding a temporary key mechanism is that the channel itself has been encrypted." It includes three types of relations: ① Security strength partial order relation This indicates a comparison of the security strength achievable by two cryptographic primitives for a specific security objective. ① Indicates the goal to be achieved; ② Dependency relationship : indicates that a certain cryptographic primitive depends on another primitive to be implemented; ③ security objective relationship : Indicates the use of a certain cryptographic primitive Able to achieve composite safety objectives , , Representing cryptographic primitives The data being processed. Among these, business domain knowledge includes rules defining what constitutes "insufficient security" or "security needs enhancement."

[0152] The composite security target It concerns the set of security objectives. The first-order logical conjunction formula for a subset, where It can be represented as: These represent the confidentiality, integrity, authenticity, and freshness of the currently interacting data, respectively; It will also contain first-order predicates describing the expected strength of a single security objective. , respectively representing composite security objectives In the meantime, there is a preference for (not mandatory) normal or higher levels of security.

[0153] 4) Formal description of the knowledge base. The relationships extracted in the previous step are described as a set of first-order logical rules, combined with the set of security objectives. Build graphs, trees, and other structures to construct a knowledge base in a structured way. The data is stored in the central data control unit for real-time retrieval in subsequent steps.

[0154] (2) Step 2: Assess the business status

[0155] This step aims to integrate static and dynamic information such as business system architecture, business pathways, and interaction nodes to continuously maintain the business status of data interaction nodes. It includes two sub-processes for business status assessment: the offline process aims to maintain the basic business status of each business based on the aforementioned static information; the online process aims to continuously / periodically update the basic business status obtained in the offline process based on the aforementioned dynamic information from the start to the end of the business process. The final business status assessment result includes the trust level of the data interaction node and the business interaction context. The steps are as follows:

[0156] 1) Offline Status Assessment. This process handles each service within the business interaction system separately. For a given service, the sub-steps are as follows:

[0157] ① First, the central data controller / back-end management system collects the static information involved in this business. Static information includes "data interaction node information" "-"Business interaction information "-"Business interaction channel information "-"System Architecture Status "Including data interaction node information" It describes static information related to nodes, such as the node's manufacturer, static identity identifier, node permissions, and interaction history; business interaction information. Describes static information such as business type and interaction history; business interaction path status. It describes the interaction topology and protocols under the current configuration; system architecture status (hereinafter referred to as system architecture). It describes the security boundaries and compliance constraints under the system architecture involving business operations.

[0158] ② After information collection is completed, a pre-set trust assessment model is used. (Such as weighted summation, machine learning classifiers, etc.), combined with data interaction node information. A comprehensive trust score is calculated. And map the result to a discrete trust level. The trust level The set of values ​​is The current trust level of data interaction nodes is described in segments from high to low. This indicates the highest level of credibility. This indicates a moderate level of credibility. This indicates low credibility. This indicates the lowest level of credibility.

[0159] ③ Subsequently, based on techniques such as sequence pattern mining, temporal rule learning, and process mining, data from business interaction information... The initial extraction process yields a candidate set of triggering rules for associated services related to this business. The set of candidate trigger rules for the most likely subsequent business after the completion of this business. Then, by combining expert knowledge or domain-specific knowledge graphs, these candidate rules are filtered to find the business that is truly temporally related to the current business and its corresponding candidate rules, that is, the set of triggering rules for the accompanying business. and the set of triggering rules for subsequent business. Use this information to construct business information evaluation results The triggering conditions of the aforementioned business triggering rules include, but are not limited to: the interaction time falling within a pre-set time period, the frequency of interaction exceeding a pre-set threshold, and the sensitivity level of the interaction data.

[0160] ④ Finally, based on and From a macro perspective, information is used to assess trust levels based on domain-specific language models, expert knowledge, and other technologies. And business information assessment results These two offline assessment results were adjusted accordingly, including the trust level. The adjustments are based on system architecture information such as whether the current interaction node is located within the security boundary and whether it is a temporary topology, as well as whether there are random fluctuations in the business interaction path involved in the node and the type of protocol used; the results of the business information assessment are also considered. The adjustments are based on the current system architecture and interaction paths. and Does the business in question exist or can it be activated?

[0161] The offline state assessment is performed when: , or When changes occur, a business status assessment is initiated. In addition, an offline status assessment process is performed periodically, with the time interval set automatically based on the business type.

[0162] 2) Online Status Assessment. This process assesses and maintains the business status involved in the current data interaction process and can update offline assessment results. After each business interaction session is established with a data interaction node, the central data control party will continuously / periodically perform this step to update the node's current trust status and business interaction context. The sub-steps are as follows:

[0163] ① Trust Status Assessment. The central data controller collects dynamic information related to the current data interaction nodes. (That is, the interaction state information of nodes, which is generated by the dynamic changes of the information of interacting nodes), including but not limited to: the current behavior pattern and the information collected during the offline evaluation process. Deviations in historical behavior patterns and the real-time network environment of the node (e.g., whether it is within a trusted VLAN). A pre-defined trust assessment model is used. (Such as weighted summation, machine learning classifiers, etc.), combined with the node static information obtained from offline evaluation. Offline evaluation of node trust level Node status information A comprehensive trust score is calculated. And map the result to a discrete trust level. The trust level The set of values ​​is The current trust level of data interaction nodes is described in segments from high to low.

[0164] ② Business context assessment. The central data controller collects dynamic information related to the current business interaction. (i.e., dynamic information on business interactions and dynamic information on node interactions) (Different factors), including but not limited to: the interaction time of the current business, the frequency of interaction, and the sensitivity level of the interaction data. Then, based on the results obtained from offline evaluation... ,use As triggering conditions, each is evaluated separately. and Ultimately, a set of associated services related to the current business is obtained. The set of most likely follow-up services after the completion of this service. And construct the evaluation results for the business context (i.e., the business interaction context). .

[0165] Online status assessments can be performed continuously, or periodically in resource-constrained scenarios. The assessment yields the business status (trust level). and business interaction context This serves as the basis for subsequent adaptive orchestration.

[0166] Step S2 specifically includes:

[0167] (3) Step 3: Match the baseline cryptographic policy corresponding to the business

[0168] This step, based on the business type of the current business data interaction, matches and retrieves the baseline cryptographic policy corresponding to that business type, which can provide basic protection, from a predefined / offline constructed baseline cryptographic policy pool. This baseline cryptographic policy contains combinations of cryptographic primitives that meet the basic security requirements of that business.

[0169] This step aims to match a baseline cryptographic policy corresponding to the business type of the current operation. The central data controller, based on the business interaction information collected in step 2, determines the policy based on the business interaction information gathered in step 2 for this interaction. Based on the business type, determine a baseline cryptographic policy that meets the basic security objectives. The central data controller parses data interaction information to identify its business type (e.g., control command issuance, device status reporting, remote parameter configuration, etc.), and then uses a baseline cryptographic policy pool predefined based on expert knowledge. The search is performed to match the most basic cryptographic strategies applicable to this business type. This strategy This represents the preset trust level, such as The following are the minimum security requirements that this business must meet.

[0170] The baseline cryptographic policy pool can be generated in several ways. First, it can be predefined based on expert knowledge. Second, it can be constructed offline during step 1, "Constructing the complete set of cryptographic primitives required for the current system's business," using methods including but not limited to: ① based on historical operation logs; ② based on existing code analysis; ③ based on expert knowledge / design documents. Combinations of cryptographic primitives obtained from these combinations can be considered as candidate baseline cryptographic policy pools. Then, the candidate baseline cryptographic policy pools are adjusted using expert knowledge or domain-specific knowledge graphs to generate the final baseline cryptographic policy pool. .

[0171] The matching process relies on a mapping function from business type to baseline strategy. This mapping is predefined by the organization administrator and pre-placed in the central data control center, where It is a collection of all business types. It is the complete set of baseline cryptographic policies.

[0172] The baseline cryptographic policy This can be described as a nesting of cryptographic primitives: Among them, data objects For business data that currently needs protection; Representing cryptographic primitives The corresponding encryption predicate, It indicates the nesting relationship between predicates.

[0173] Step S3 specifically includes:

[0174] (4) Step 4: Analyze and generate dynamic composite security targets.

[0175] This step takes the business interaction path and system architecture information collected in step 2, the business status evaluated in step 2, and the baseline cryptographic policy matched in step 3 as inputs. It uses technologies such as calling domain-specific language models or domain-specific knowledge graphs based on business domain knowledge rules to perform formal reasoning, analyze whether the baseline cryptographic policy is secure enough, and output the composite security target most suitable for the current business status, described in declarative logical statements.

[0176] This step aims to analyze and generate dynamic, composite security objectives. In this step, the central data controller dynamically generates the security objectives to be achieved in this interaction based on the node's business status and the matched baseline cryptographic policy. The steps are as follows:

[0177] 1) Integrate contextual information. Integrate the business interaction path information collected in step 2. and system architecture status Step 2 outputs the business status (trust level) and business context The baseline cryptographic policy output from step 3) As the primary input.

[0178] 2) Formal reasoning is performed using business domain knowledge rules and technologies such as domain-specific language models or domain-specific knowledge graphs. The central data control direction first proposes a reasoning request, which aims to obtain: a typical execution strategy under the current system architecture and interaction path configuration. For services where the trust level of the interaction node is [value missing], The business interaction context is At that time, strategy Is it secure enough? If not, what additional or higher-level security objectives need to be met?

[0179] 3) Output a declarative, dynamic, composite security objective. After inference, a result can be output that is consistent with... The security objectives are the same, or Composite security objectives enhanced or adjusted based on the basic security objectives The goal is declarative, not subjective, rating. For example, if Only the confidentiality of the current data exchange process is required, and However, the current interaction process The model may then infer the composite security objective after this interaction update. It will be set as This indicates that the current composite security objectives are higher levels of confidentiality, baseline integrity, and baseline freshness.

[0180] (5) Step 5: Arrange problem definition and coding

[0181] This step aims to model the cryptographic policy update problem. The central data controller will determine "how to combine sequences of cryptographic primitives to achieve composite security objectives." This problem is defined as an orchestration problem, aiming to replace and update the sequence of cryptographic primitive combinations in a baseline cryptographic strategy. It is modeled as an automatically solvable constrained programming problem: the composite security objective satisfied by the baseline cryptographic strategy is taken as the initial state; the composite security objective output in step 4 is taken as the endpoint of the programming problem; the set of cryptographic primitives is taken as the state transition operation; minimizing the resource overhead of the cryptographic primitives is taken as the optimization objective; and the communication overhead caused by implementing the cryptographic primitives is taken as the programming budget constraint. The specific details are as follows:

[0182] 1) The central data controller sets a state space set that includes all possible states of the system. A state It contains the set of security objectives that the current strategy can satisfy, and is analyzed through the baseline cryptographic strategy output in step 3. To obtain the composite security objectives that it can satisfy. This is taken as the initial state from which the planning begins. ;

[0183] 2) The central data controller defines the operation set. The cryptographic primitives defined in step 1 are treated as state transition operations A. Simultaneously, a cost function is defined. , each operation Mapped to a quantized resource cost vector Here, the resource overhead vector originates from the annotation of the cryptographic primitive resource overhead in step 1. .

[0184] 3) The central data controller sets the state transition function. According to the knowledge base defined in step 1 Relationship in Defines a state Execute the following operation After that, the system will transition to a new state. At the same time, according to Set up to execute operation successfully Hard constraints that must be met: when operating The prerequisite is in the current state The transfer will only occur when the conditions are met.

[0185] 4) Formalize the above definitions into a state transition model. .

[0186] 5) The central data controller will output the composite security target from step 4. Set as a state transition model End point state Target state It can be a single state or a set of states, resulting in a composite security objective output from step 4. Decide.

[0187] 6) Define the planning problem. This involves determining "how to combine sequences of cryptographic primitives to achieve a composite security objective." This problem can be transformed into: in the state transition model In the middle, find a path from the initial state To the target state Optimal state transition operation sequence Make it satisfy the following conditions:

[0188]

[0189]

[0190] in, State transition model All possible endpoint states The effective state transition operation sequence, that is, for , ; It is a scalar function used to transform a multidimensional cost vector A single optimized value is aggregated based on priority weights, with weights determined by the importance of overhead; the communication overhead resulting from applying the final cryptographic policy is defined according to the organization's security requirements. (Taking message increment as an example) Budget , representing the state transition operation sequence The total message increment of all operations in the sequence cannot exceed the budget. Furthermore, each operation in the state transition sequence must satisfy its own hard constraints.

[0191] (6) Step 6: Solve the arrangement problem and update the cryptographic strategy

[0192] This step encodes and solves the orchestration problem defined in step 5. Using a constrained programming algorithm, it automatically finds the optimal path from the starting point to the ending point while meeting the planning budget. This path is the optimal sequence of cryptographic primitive combinations that best fits the current data interaction context. Finally, this optimal sequence is applied to the existing baseline cryptographic strategy to generate a final cryptographic strategy that is fully adapted to the current interaction environment, and then applied to this business data interaction. Afterward, this process can be used for a new round of dynamic orchestration for subsequent interactions.

[0193] The specific process is as follows:

[0194] 1) Planning Encoding and Solving. The central data controller encodes the planning problem defined in step 5 and uses it as input. Using a constrained programming algorithm, it automatically finds a sequence of state transitions that, while meeting the budget, reaches the planning endpoint with optimal resource consumption. .

[0195] 2) Construct an executable cryptographic strategy. The central data controller performs the optimal state transition operation sequence output in step 5. Adjust and construct the final cryptographic strategy. .

[0196] 3) Application strategy. The central data controller immediately adopts... This is used to handle the current data transmission and reception with the target data interaction node. If the strategy requires collaboration between the two parties, the central data controller will securely notify the data interaction node of the necessary strategy parameters during the secure session establishment phase.

[0197] 4) Complete the interaction and prepare for the next iteration. This data interaction is in the strategy... The process is completed under the protection of [the relevant authority / system]. For the next business interaction with the same or different data interaction nodes, the process will jump to step 2 to re-evaluate the real-time trust relationship of the nodes, thereby starting a new round of dynamic policy orchestration to ensure that the security policy always matches the dynamically changing trust relationship.

[0198] The general flow of the above example of adaptive cryptographic strategy orchestration method is as follows: Figure 2 As shown.

[0199] Alternative solutions:

[0200] (1) The present invention uses a distributed data interaction architecture with a central data controller and one or more data interaction nodes as an example to illustrate the technical solution. In addition to the power distribution network architecture mentioned in the embodiment, it can also be used in cloud service providers and tenants in cloud computing, master stations and remote terminal units in industrial control systems, IoT hubs and various sensors and actuators, edge nodes and user clients in content delivery networks, and unified management servers and employee equipment in enterprise IT architecture. These examples have obvious master-slave relationships and can be replaced with a business data interaction architecture with equal data parties, such as vehicle-to-vehicle (V2V) communication in vehicle-to-everything (V2X), business data collaboration between enterprises, and Internet of Things (IoT) Mesh networks without a central gateway.

[0201] (2) The “set of cryptographic primitives” mentioned in “step 1-1)” of this invention may also include other atomic encryption methods with equivalent effects, such as AES and 3DES in the field of symmetric encryption, RSA and ECC in the field of asymmetric encryption, SHA-2 and SHA-3 series in the field of secure hashing, post-quantum cryptography (PQC) algorithms, or higher-level security operations such as configuring specific TLS cipher suites; the “resource overhead required to execute cryptographic primitives” is not limited to historical data, but may also come from benchmark tests, theoretical complexity analysis or vendor performance data under specific hardware platforms (such as embedded ARM or high-performance x86 architecture) or specific network conditions (such as high-latency wireless networks). Furthermore, the overhead index itself is not limited to computational overhead, communication overhead, storage overhead, latency overhead, energy overhead, and can also be extended to any dimension that can quantify the impact of secure operations on the system, such as the end-to-end latency required to complete the operation, the number of network round trips for key negotiation, or even the economic cost such as the licensing fee for a specific algorithm.

[0202] (3) The present invention mentions in "Step 3" that a "predefined baseline cryptographic policy pool" is needed. However, it is not mandatory to provide this baseline cryptographic policy pool. If the baseline cryptographic policy pool is not provided, step 3 will directly initialize an empty policy. This is the output of this step.

[0203] (4) In step 5 of this invention, the "message increment resulting from applying the final cryptographic strategy" is not mandatory as a budget-participating problem. Moreover, other overheads can also be solved as budget-participating problems. There can also be multiple budgets.

[0204] (5) In steps 4, 5, and 6 of this invention, the composite security objectives, problem model, and problem solution of the dynamic orchestration problem of cryptographic strategies are defined and described respectively, and this problem is modeled as a planning problem. An alternative is to model it as a multi-objective optimization problem. It includes the following steps: ① New step 4: Dynamic security objective quantification. The central data controller integrates contextual information based on the real-time trust status of nodes, uses business domain knowledge rules to perform formal reasoning to dynamically generate the security objectives to be achieved in this interaction, and quantifies the level that each security objective needs to reach based on expert knowledge; ② New step 5: Modeling a multi-objective optimization problem. First, quantify the security value: by using expert knowledge to score the security assets involved in the business interaction, the value of the security assets to be protected by the cryptographic strategy is quantified. Then, quantify the overhead of cryptographic primitives: quantify the resource overhead involved in each cryptographic primitive by normalizing it with the security value, and use the message increment as the budget (the budget constraint is optional). Finally, define a multi-objective optimization problem, which is dedicated to solving a set of cryptographic primitive sequences. For the current business data interaction process, it can maximize security value while minimizing the overhead of applying this sequence of cryptographic primitives, and the sequence... The total message increment of all operations must not exceed the budget. ③ New step 6: Replace "Section 1") and use optimization methods such as bi-level optimization to solve the multi-objective optimization problem modeled in the new step 5. The subsequent steps are the same as the old step 6.

[0205] This invention addresses the challenge of meeting complex security objectives arising from changes in business status, which leads to insufficient or excessive protection in existing cryptographic strategies. It introduces an adaptive cryptographic strategy orchestration technique, which adjusts the sequence of multiple cryptographic primitives in a coordinated manner. This enhances the encryption strength of business data interaction while minimizing irrelevant resource overhead and related economic investment, demonstrating promising application prospects.

[0206] (1) From micro-nodes to macro-architecture, this invention achieves objective and comprehensive perception of business status, enhancing the multi-granularity analysis capability that meets security objectives under an open and variable business system architecture. Compared to existing work that only assesses interaction risks, relies mainly on subjective scoring and lacks a macro perspective, it can only perceive isolated security objectives. This invention collects static / dynamic information of the business system from a micro to a macro perspective, including system architecture status, business interaction path information, business interactions, and data interaction nodes. Through a two-stage continuous perception mechanism combining offline and online methods, it comprehensively maintains the business status of current business data interactions. At the same time, the declarative logical statements used in this invention to describe composite security objectives are more objective than subjective scoring, comprehensively improving the ability to comprehensively and objectively explore multi-dimensional security requirements.

[0207] (2) Matching appropriate cryptographic strategies to business status achieves a dynamic balance between protection strength and resource overhead. Compared to existing solutions that employ static or partially dynamic high-strength cryptographic strategies, leading to "overprotection" and unnecessary security costs and resource waste, this invention can adaptively select lightweight cryptographic strategies for high-trust-level business interactions. Simultaneously, while ensuring adequate business encryption protection, it reduces resource overhead such as computation, communication, storage, latency, and energy costs associated with implementing encryption protection in unnecessary scenarios, thereby improving overall system efficiency. Furthermore, it avoids security risks caused by insufficient protection from existing cryptographic strategies.

[0208] (3) In a complex environment where security threats are constantly evolving and trust relationships are dynamically changing, this invention enhances the comprehensive defense capabilities against data security risks in open and variable business systems. Existing solutions only perform localized adjustments at the encryption strategy level, lacking the ability to dynamically adjust cryptographic strategies globally, making it difficult to comprehensively cover every dynamic security objective. This invention, however, can dynamically arrange the combination sequence of all cryptographic primitives in the cryptographic strategy, automatically configuring the most suitable combination sequence of cryptographic primitives for each security objective. It achieves multi-granularity arrangement accurate to a single session and multi-cryptographic primitive linkage, supporting the linkage protection of complex security objectives and providing global dynamic protection capabilities adapted to open and variable business systems.

[0209] Example 2:

[0210] In this embodiment, the system architecture consists of a distribution network edge computing terminal (e.g., a smart distribution substation terminal) acting as the central data controller and a novel entity (e.g., a user-side energy storage station, a third-party charging pile, etc.) belonging to an external investor of the power grid company, acting as a data interaction node. When this novel entity needs to perform a sensitive business data interaction with the edge computing terminal (e.g., reporting key operating parameters or receiving scheduling instructions), the workflow of the method described in this invention is as follows:

[0211] First, the edge computing terminal has pre-stored a cryptographic primitives knowledge base, including the national standard SM series algorithms, offline, and configured baseline cryptographic policies for different business types. When the edge computing terminal establishes a business interaction session with a new entity, step 2 is performed to assess its business status. Since the business system architecture and business pathways have not changed, this data interaction mainly assesses the trust relationship and business interaction context. Specifically, regarding the trust relationship assessment: since this entity is not a directly affiliated device within the power grid company, if there are risks in its network environment or if this interaction behavior deviates from historical behavior, its trust relationship may be assessed as low. This indicates a low trust level. Business interaction context assessment: Determine the accompanying business processes involved in this interaction. and subsequent business If the service is reported after a fault, then based on historical interactions, it can be known that the accompanying services... Empty, subsequent business after the end of this business interaction We need to receive the reset request as soon as possible.

[0212] Subsequently, the edge computing terminal, based on the current service type of the interaction, executes step 3 to match a default baseline password policy. This baseline strategy It is designed for high-trust subjects and may only contain the basic SM1 encryption algorithm to ensure data confidentiality, which poses a potential risk of "insufficient protection" for low-trust subjects.

[0213] After obtaining the baseline cryptographic policy, the edge computing terminal executes step 4, assigning the assessed lower trust level. Follow-up business Baseline Strategy The corresponding basic security objectives are used as context input to analyze and generate a policy that is superior to the baseline. Stronger dynamic and complex security objectives For example, the goal It may have added mandatory requirements for data integrity and freshness on top of the existing confidentiality, in order to prevent data from being tampered with or replayed during transmission.

[0214] Then, the edge computing terminal executes steps 5 and 6, modeling the policy orchestration process as a constrained programming problem, in order to The planning should start with the safety objectives that can be met. To plan the endpoint, and considering the message increment resulting from achieving the planned endpoint as the budget, the system automatically calculates a path that optimally combines cryptographic primitives while satisfying the new goal and budget and minimizing resource consumption. For example, the solution might require adding message verification codes and timestamp primitives to the existing SM4 encryption.

[0215] Finally, the edge computing terminal constructs the final cryptographic strategy based on the optimal primitive combination obtained from the solution. (For example, This approach is immediately applied to sensitive data interactions with the new entity, replacing the original baseline strategy and ensuring data security when interacting with potentially untrusted entities. When the entity initiates another business interaction, or when a new entity joins, the system repeats the above evaluation and dynamic orchestration process to achieve continuous adaptation of the security strategy. In this way, the present invention can adaptively enhance the security protection level, effectively avoiding the security risk of "insufficient protection" caused by using default strategies when interacting with low-trust entities, and ensuring the secure and reliable transmission of critical power grid data.

[0216] Example 3:

[0217] This embodiment provides an example of adaptive cryptographic policy orchestration for access to entities within a distribution network.

[0218] In this embodiment, the system consists of a distribution automation system master station / distribution control system master station (hereinafter referred to as the distribution master station) acting as the central data controller, and an internal power grid distribution network edge computing terminal deployed in a physically protected substation, acting as a data interaction node. When the distribution master station needs to perform non-urgent file transfers to the edge computing terminal, the workflow of the method described in this invention is as follows:

[0219] First, the distribution master station has pre-stored a cryptographic primitives knowledge base, including the national cryptographic SM series algorithms, offline, and configured baseline cryptographic policies for different business types. Since the business system architecture and business pathways have not changed, the distribution master station performs step 2 at the start of the interaction to conduct trust assessment and interaction context assessment of the edge computing terminal. Specifically, regarding the trust relationship assessment: because the terminal is deployed within a physically secure substation and communicates via a dedicated network, its overall trust level is rated as the highest. This represents a high level of trust. Business interaction context assessment: Based on historical data, the accompanying business processes involved in this interaction... and subsequent business All are empty.

[0220] Subsequently, based on the type of file transfer service, the system executes step 3 to match a default, high-security baseline password policy. This strategy Designed for general scenarios, it may include multiple cryptographic primitives such as asymmetric encryption, symmetric encryption, message verification codes, and timestamps to deal with complex network attack threats. However, in this scenario, this protection method is too strict and wastes computing resources.

[0221] Next, the system executes step 4, which assesses the high level of trust. With complex baseline strategies This is analyzed together as context. The analysis results suggest that, in the current highly trusted interaction environment, the original security target level is too high, which would cause unnecessary resource consumption. Therefore, a new security target level is generated to align with the baseline policy. Compared to the more simplified dynamic security target For example, the goal It may be sufficient to meet basic confidentiality and integrity requirements, without mandating high levels of encryption and replay protection.

[0222] Then, the distribution master station executes steps 5 and 6, modeling the strategy orchestration process as a constrained programming problem, in order to... The planning should start with the safety objectives that can be met. To plan the endpoint, and considering the message increment resulting from achieving the planned endpoint as the budget, the system automatically solves for a path that satisfies the new goal and budget while minimizing resource overhead. The solution may remove expensive primitives from the original strategy, such as asymmetric encryption used for key negotiation and timestamps used for replay prevention.

[0223] Finally, the distribution master station generates a lightweight final cryptographic strategy based on the solution results. (For example, while maintaining the original authentication algorithm, the original Z encryption is downgraded to SM1 encryption, and encryption primitives such as timestamps are removed), and this is applied to this file transfer. In this way, the present invention significantly reduces the resource overhead of cryptographic operations while ensuring basic security, avoids resource waste caused by "over-protection" during routine operations in a highly trusted environment, reduces the computational burden of the distribution network edge computing terminal and the end-to-end latency of command interaction, saves system computing resources, and achieves the economy and efficiency of adaptive security protection.

[0224] Example 4:

[0225] Based on the same inventive concept, this invention also provides a cryptographic policy adaptive orchestration system, wherein the system is deployed in the dominant node of a master-slave node device group, or in any node of an equal node device group, the system as follows: Figure 3 As shown, it includes: a state module, a baseline cryptographic policy module, and a cryptographic policy module;

[0226] The status module is used to evaluate the business status based on the collected system architecture, business interaction path, business interaction information and interaction node information;

[0227] The baseline cryptographic policy module is used to match the corresponding baseline cryptographic policy according to the business type;

[0228] The cryptographic strategy module is used to solve for the optimal cryptographic primitive combination sequence as the final cryptographic strategy based on the business status and baseline cryptographic strategy, under the planning budget constraint.

[0229] The master-slave relationship among node devices includes the master station of the power distribution control system and the edge computing terminal of the power distribution network architecture, as well as cloud service providers and tenants in cloud computing, master stations and remote terminal units in industrial control systems, IoT hubs and various sensors and actuators, edge nodes and user clients in content delivery networks, and unified management servers and employee devices in enterprise IT architectures. The equal relationship among node devices includes vehicle-to-vehicle (V2V) communication in vehicle-to-everything (V2X) networks, business data collaboration between enterprises, and IoT mesh networks without a central gateway.

[0230] Preferably, the status module evaluates the business status based on the collected system architecture, business interaction path, business interaction information, and interaction node information, including:

[0231] Based on the static information collected, including system architecture, business interaction paths, business interaction information, and interaction node information, the status of basic business is assessed offline.

[0232] From the start to the end of the business process, the business status is obtained by updating the basic business status obtained in the offline process online based on the dynamic information of node interaction generated by the dynamic changes in the collected system architecture, business interaction path, business interaction information and interaction node information.

[0233] Preferably, the status module evaluates the basic business status offline based on static information collected from the system architecture, business interaction paths, business interaction information, and interaction node information, including:

[0234] Trust levels are calculated by combining a pre-set trust assessment model with information from interactive nodes.

[0235] Based on data mining technology, a preliminary set of triggering rules for associated business and subsequent business most likely to occur after the business ends is extracted from the business interaction information.

[0236] By combining expert knowledge or domain-specific knowledge graphs, the candidate set of triggering rules for associated and subsequent services is filtered to obtain the set of triggering rules for associated and subsequent services as the offline service information evaluation result;

[0237] Based on the system architecture and business interaction channels, the trust level and / or offline business information evaluation results are adjusted according to the domain-specific language model or expert knowledge to obtain the basic business status of offline evaluation.

[0238] The offline assessment is performed when the system architecture, business interaction path, or interaction node information changes, or periodically at time intervals set according to the business type; the trust assessment model includes one or more of the following: weighted summation or machine learning classifier; the data mining technology includes one or more of the following: sequence pattern mining, time series rule learning, or process mining.

[0239] Preferably, during the process from the start to the end of the service, the status module updates the basic service status obtained offline to obtain the service status online based on the dynamic information of node interaction generated by the dynamic changes in the collected system architecture, service interaction path, service interaction information, and interaction node information, including:

[0240] During the process from the start to the end of the business, collect dynamic information of node interactions related to the current data interaction node, and update the trust level by combining the dynamic information of node interactions, the interaction node information used in the offline evaluation, and the trust level of the offline evaluation through a preset trust evaluation model.

[0241] Collect dynamic information about business interactions related to the current business interaction. Based on the evaluation results of offline business information obtained from offline evaluation, use the dynamic information about business interactions as triggering conditions to evaluate the set of triggering rules for associated business and subsequent business in the evaluation results of offline business information. Finally, obtain the set of associated business and the set of subsequent business associated with the current business to form the business interaction context.

[0242] The updated trust level and business interaction context are used as the business state.

[0243] Among them, online updates are performed continuously or periodically; the node interaction dynamic information includes one or more of the following: the deviation between the current behavior pattern and the historical behavior pattern in the interaction node information collected during the offline evaluation process, or the real-time network environment in which the node is located; the business interaction dynamic information includes one or more of the following: the interaction time of the current business, the frequency of interaction, or the sensitivity of interaction data.

[0244] Preferably, the cryptographic policy module is specifically used for:

[0245] Based on the business status and baseline cryptographic strategy, and under the constraint of a planning budget, a planning approach is used to solve for the optimal cryptographic primitive combination sequence as the final cryptographic strategy; or

[0246] Based on the business status and baseline cryptographic strategy, and under the constraint of the planning budget, a multi-objective optimization approach is adopted to solve for the optimal cryptographic primitive combination sequence as the final cryptographic strategy.

[0247] Preferably, the cryptographic strategy module, based on the business state and baseline cryptographic strategy, and under budget constraints, uses a planning approach to solve for the optimal cryptographic primitive combination sequence as the final cryptographic strategy, including:

[0248] Based on the aforementioned business interaction channels, system architecture, business status, and baseline cryptographic policies, formal reasoning is performed using business domain knowledge rules to obtain a composite security objective.

[0249] Starting with the composite security objective corresponding to the baseline cryptographic strategy, and ending with the composite security objective derived through reasoning, the available cryptographic primitives are used as planning actions, the resource consumption cost of the cryptographic primitives is used as the optimization objective, and the overhead generated by the execution of the cryptographic strategy is used as the planning budget. The optimal sequence of cryptographic primitive combinations is then obtained through planning and solving, which is taken as the final cryptographic strategy.

[0250] Preferably, the cryptographic policy module, based on the business interaction path, system architecture, business status, and baseline cryptographic policy, uses business domain knowledge rules for formal reasoning to obtain a composite security objective, including:

[0251] Based on the business interaction path, system architecture, business state, and baseline cryptographic policy, formal reasoning is performed using business domain knowledge rules to determine whether the baseline cryptographic policy is sufficiently secure under the current business interaction path and system architecture configuration, given the trust level of the interaction node and the current business interaction context.

[0252] If so, the security objective corresponding to the baseline cryptographic strategy shall be used as the composite security objective;

[0253] Otherwise, a composite security objective is obtained based on the security objective corresponding to the baseline cryptographic policy, which is enhanced or adjusted.

[0254] The formal reasoning methods include: domain-specific language models or domain-specific knowledge graphs, wherein the business domain knowledge rules include rules that are insufficient in security and / or require security enhancement.

[0255] Preferably, the composite security objective in the cryptographic policy module is declarative.

[0256] Preferably, the cryptographic strategy module takes the composite security objective corresponding to the baseline cryptographic strategy as the planning starting point, the composite security objective obtained through reasoning as the planning endpoint, the available cryptographic primitives as the planning actions, the resource consumption cost of the cryptographic primitives as the optimization objective, and the overhead generated by the execution of the cryptographic strategy as the planning budget, and performs planning and solving to obtain the optimal sequence of cryptographic primitive combinations as the final cryptographic strategy, including:

[0257] Taking the composite security objective corresponding to the baseline cryptographic policy as the planning starting point, the composite security objective obtained through reasoning as the planning endpoint, the available cryptographic primitives as the planning actions, the resource consumption cost of the cryptographic primitives as the optimization objective, and the overhead generated by the execution of the cryptographic policy as the planning budget, an adaptive optimization model for the cryptographic policy is constructed.

[0258] The optimal sequence of cryptographic primitive combinations is obtained by solving the adaptive optimization model of the cryptographic strategy, which is then used as the final cryptographic strategy.

[0259] Preferably, the cryptographic policy module takes the composite security objective corresponding to the baseline cryptographic policy as the planning starting point, the inferred composite security objective as the planning endpoint, the available cryptographic primitives as the planning actions, the resource consumption cost of the cryptographic primitives as the optimization objective, and the overhead generated by the execution of the cryptographic policy as the planning budget, and constructs an adaptive optimization model for the cryptographic policy, including:

[0260] All possible states of the system are set as a state space set. Each state contains a set of security objectives that the current cryptographic policy can satisfy. The baseline cryptographic policy is analyzed to obtain the corresponding composite security objectives as the initial state for planning.

[0261] The cryptographic primitives are treated as state transition operations and a set of operations is constructed. The correspondence between each state transition operation and the resource overhead vector is set as a cost function.

[0262] State transition operations on the state space set are defined as state transition functions, and hard constraints that must be satisfied for successful execution of the operation are set according to the logical relationships in the pre-built cryptographic primitive knowledge base.

[0263] The state space set, the composite security objective corresponding to the baseline cryptographic policy, the operation set, the state transition function and the cost function are formally encoded into a state transition model, and the composite security objective obtained through reasoning is set as the endpoint state of the state transition model.

[0264] In the state transition model, the objective function is constructed with the goal of minimizing the total resource consumption cost of cryptographic primitives in the state transition operation sequence from the initial state to the final state. The constraints are that the overhead generated by the execution of the cryptographic policy is less than the planning budget, and that each cryptographic primitive in the state transition operation sequence must satisfy the hard constraints. Thus, an adaptive optimization model for the cryptographic policy is constructed.

[0265] Preferably, the cryptographic strategy module, based on the business state and baseline cryptographic strategy, and under budget constraints, employs a multi-objective optimization approach to solve for the optimal cryptographic primitive combination sequence as the final cryptographic strategy, including:

[0266] Based on the trust level in the business state, the business interaction context in the business state is integrated, and formal reasoning is performed using business domain knowledge rules to dynamically generate the security goals to be achieved in this interaction. The level that each security goal needs to be achieved is quantified based on expert knowledge.

[0267] By leveraging expert knowledge to score the security assets involved in business interactions, the security value to be protected by cryptographic strategies is quantified. The resource overhead and security value of each cryptographic primitive in the cryptographic primitive knowledge base are normalized, and the message increment is used as the budget.

[0268] To maximize security value while minimizing the resource overhead caused by applying cryptographic primitive combination sequences, a multi-objective optimization model for cryptographic policies is constructed, with the constraint that the sum of message increments of all operations in the cryptographic primitive combination sequence cannot exceed the budget, and the baseline cryptographic policy as the initial boundary condition.

[0269] The multi-objective optimization model of the cryptographic strategy is solved using a two-level optimization method to obtain the optimal sequence of cryptographic primitive combinations as the final cryptographic strategy.

[0270] The formal reasoning methods include: domain-specific language models or domain-specific knowledge graphs; the business domain knowledge rules include rules that are insufficient in security and / or require security enhancement.

[0271] Preferably, the process of constructing the cryptographic primitive knowledge base in the cryptographic strategy module includes:

[0272] Extract the complete set of cryptographic primitives required by the current system business, and construct a cryptographic primitive library usable by the system based on the complete set of cryptographic primitives. Each cryptographic primitive in the cryptographic primitive library includes: a unique identifier, an operation type signature, and resource overhead.

[0273] Based on business domain knowledge, fine-tune the domain-specific language model, or based on business domain knowledge graphs and cryptographic ontology, combine logical reasoning or graph neural network technology to analyze and extract the logical relationships between members in the cryptographic primitive library. The logical relationships include: security strength partial order relationship, dependency relationship and security goal relationship.

[0274] The logical relationships are described as logical rules, and a cryptographic primitive knowledge base is constructed in a structured manner by combining them with a preset set of security objectives.

[0275] Preferably, the baseline cryptographic policy module matches the corresponding baseline cryptographic policy according to the service type, including:

[0276] When no baseline cryptographic policy pool exists, return an empty set as the baseline cryptographic policy.

[0277] When a baseline cryptographic policy pool exists, the most basic baseline cryptographic policy applicable to that business type is found in the baseline cryptographic policy pool based on the business type.

[0278] Preferably, the process of constructing the baseline cryptographic policy pool in the baseline cryptographic policy module includes:

[0279] Defined based on expert knowledge, a baseline cryptographic policy pool is obtained; or

[0280] By employing multiple analysis methods and their arbitrary combinations, candidate baseline cryptographic strategy pools are obtained, and the candidate baseline cryptographic strategy pools are adjusted based on expert knowledge or domain-specific knowledge graphs to generate a baseline cryptographic strategy pool.

[0281] The analysis methods include, but are not limited to, the following: analysis based on historical operation logs, analysis based on existing code, or analysis based on expert knowledge and / or design documents.

[0282] Example 5

[0283] like Figure 4 As shown, the present invention also provides an electronic device, which may be a computer device, a microcontroller device, a smart mobile device, etc. The electronic device in this embodiment may include a processor, a memory, a transceiver component, etc. The memory, processor, and transceiver component are connected via a bus; the memory can be used to store executable programs, and an exemplary executable program may include instructions; the processor is used to execute the instructions stored in the memory. The memory can also be used to store data, which can be accessed and / or modified when instructions are executed.

[0284] The processor may be a Central Processing Unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, and it is suitable for implementing one or more instructions. Specifically, it is suitable for loading and executing one or more instructions in the storage medium to implement the corresponding method flow or corresponding function, so as to implement the steps of the cryptographic strategy adaptive arrangement method in the above embodiments.

[0285] Example 6

[0286] Based on the same inventive concept, this invention also provides a readable storage medium, specifically an electronic device readable storage medium (Memory). This readable storage medium is a memory device within an electronic device used to store programs and data. It is understood that the storage medium here can include both built-in storage media within the electronic device and extended storage media supported by the electronic device. The storage medium provides storage space, which stores the terminal's operating system. Furthermore, this storage space also stores one or more instructions suitable for loading and execution by a processor. These instructions can be one or more executable programs (including program code). It should be noted that the storage medium here can be high-speed RAM or non-volatile memory, such as at least one disk storage device. Loading and executing one or more instructions stored in the storage medium by the processor can implement the steps of the cryptographic policy adaptive arrangement method in the above embodiments.

[0287] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0288] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0289] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0290] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0291] The above are merely embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention are included within the scope of the claims of the present invention pending approval.

Claims

1. A method for adaptive arrangement of cryptographic strategies, characterized in that, include: Based on the collected system architecture, business interaction paths, business interaction information, and interaction node information, assess the business status; Match the corresponding baseline password policy based on the business type; Based on the business status and baseline cryptographic strategy, the optimal cryptographic primitive combination sequence is solved under the planning budget constraint as the final cryptographic strategy; The assessment of business status based on the collected system architecture, business interaction paths, business interaction information, and interaction node information includes: Based on the static information collected, including system architecture, business interaction paths, business interaction information, and interaction node information, the status of basic business is assessed offline. From the start to the end of the business process, the business status is obtained by updating the basic business status obtained in the offline process online based on the dynamic information of node interaction generated by the dynamic changes in the collected system architecture, business interaction path, business interaction information and interaction node information.

2. The method as described in claim 1, characterized in that, The offline assessment of basic business status based on static information collected, including system architecture, business interaction paths, business interaction information, and interaction node information, includes: Trust levels are calculated by combining a pre-set trust assessment model with information from interactive nodes. Based on data mining technology, we can initially extract the trigger rule candidate set of the associated business of the business corresponding to the business interaction information, as well as the trigger rule candidate set of the most likely subsequent business after the corresponding business ends. By combining expert knowledge or domain-specific knowledge graphs, the candidate set of triggering rules for associated and subsequent services is filtered to obtain the set of triggering rules for associated and subsequent services as the offline service information evaluation result; Based on the system architecture and business interaction channels, the trust level and / or offline business information evaluation results are adjusted according to the domain-specific language model or expert knowledge to obtain the basic business status of offline evaluation. The offline assessment is performed when the system architecture, business interaction path, or interaction node information changes, or periodically at time intervals set according to the business type; the trust assessment model includes one or more of the following: weighted summation or machine learning classifier; the data mining technology includes one or more of the following: sequence pattern mining, time series rule learning, or process mining.

3. The method as described in claim 2, characterized in that, The process of updating the basic business status obtained from the offline process online to obtain the business status during the business process from start to finish includes: (This is based on the dynamic information of node interactions generated by the dynamic changes in the collected system architecture, business interaction paths, business interaction information, and interaction node information.) During the process from the start to the end of the business, collect dynamic information of node interactions related to the current data interaction node, and update the trust level by combining the dynamic information of node interactions, the interaction node information used in the offline evaluation, and the trust level of the offline evaluation through a preset trust evaluation model. Collect dynamic information about business interactions related to the current business interaction. Based on the evaluation results of offline business information obtained from offline evaluation, use the dynamic information about business interactions as triggering conditions to evaluate the set of triggering rules for associated business and subsequent business in the evaluation results of offline business information. Finally, obtain the set of associated business and the set of subsequent business associated with the current business to form the business interaction context. The updated trust level and business interaction context are used as the business state. Among them, online updates are performed continuously or periodically; the node interaction dynamic information includes one or more of the following: the deviation between the current behavior pattern and the historical behavior pattern in the interaction node information collected during the offline evaluation process, or the real-time network environment in which the node is located; the business interaction dynamic information includes one or more of the following: the interaction time of the current business, the frequency of interaction, or the sensitivity of interaction data.

4. The method of claim 3, wherein, The process of finding the optimal cryptographic primitive combination sequence as the final cryptographic strategy based on business status and baseline cryptographic policy, under planning budget constraints, includes: Based on the business status and baseline cryptographic strategy, and under the constraint of a planning budget, a planning approach is used to solve for the optimal cryptographic primitive combination sequence as the final cryptographic strategy; or Based on the business status and baseline cryptographic strategy, and under the constraint of the planning budget, a multi-objective optimization approach is adopted to solve for the optimal cryptographic primitive combination sequence as the final cryptographic strategy.

5. The method of claim 4, wherein, The method of finding the optimal cryptographic primitive combination sequence as the final cryptographic strategy based on business status and baseline cryptographic policy, under planning budget constraints, and using a planning approach, includes: Based on the aforementioned business interaction channels, system architecture, business status, and baseline cryptographic policies, formal reasoning is performed using business domain knowledge rules to obtain a composite security objective. Starting with the composite security objective corresponding to the baseline cryptographic strategy, and ending with the composite security objective derived through reasoning, the available cryptographic primitives are used as planning actions, the resource consumption cost of the cryptographic primitives is used as the optimization objective, and the overhead generated by the execution of the cryptographic strategy is used as the planning budget. The optimal sequence of cryptographic primitive combinations is then obtained through planning and solving, which is taken as the final cryptographic strategy.

6. The method of claim 5, wherein, Based on the aforementioned business interaction channels, system architecture, business status, and baseline cryptographic policies, formal reasoning is performed using business domain knowledge rules to obtain a composite security objective, including: Based on the business interaction path, system architecture, business state, and baseline cryptographic policy, formal reasoning is performed using business domain knowledge rules to determine whether the baseline cryptographic policy is sufficiently secure under the current business interaction path and system architecture configuration, given the trust level of the interaction node and the current business interaction context. If so, the security objective corresponding to the baseline cryptographic strategy shall be used as the composite security objective; Otherwise, a composite security objective is obtained based on the security objective corresponding to the baseline cryptographic policy, which is enhanced or adjusted. The formal reasoning methods include: domain-specific language models or domain-specific knowledge graphs, wherein the business domain knowledge rules include rules that are insufficient in security and / or require security enhancement.

7. The method of claim 5, wherein, The composite security objective is declarative.

8. The method of claim 5, wherein, The process begins with a composite security objective corresponding to the baseline cryptographic policy, ends with a composite security objective derived through reasoning, uses available cryptographic primitives as planning actions, takes the resource consumption cost of cryptographic primitives as the optimization objective, and uses the overhead generated by executing the cryptographic policy as the planning budget. The optimal sequence of cryptographic primitive combinations is then solved to obtain the final cryptographic policy. This process includes: Taking the composite security objective corresponding to the baseline cryptographic policy as the planning starting point, the composite security objective obtained through reasoning as the planning endpoint, the available cryptographic primitives as the planning actions, the resource consumption cost of the cryptographic primitives as the optimization objective, and the overhead generated by the execution of the cryptographic policy as the planning budget, an adaptive optimization model for the cryptographic policy is constructed. The optimal sequence of cryptographic primitive combinations is obtained by solving the adaptive optimization model of the cryptographic strategy, which is then used as the final cryptographic strategy.

9. The method of claim 8, wherein, The adaptive optimization model for cryptographic policies is constructed by taking the composite security objective corresponding to the baseline cryptographic policy as the planning starting point, the composite security objective obtained through reasoning as the planning endpoint, the available cryptographic primitives as the planning actions, the resource consumption cost of the cryptographic primitives as the optimization objective, and the overhead generated by the execution of the cryptographic policy as the planning budget. The model includes: All possible states of the system are set as a state space set. Each state contains a set of security objectives that the current cryptographic policy can satisfy. The baseline cryptographic policy is analyzed to obtain the corresponding composite security objectives as the initial state for planning. The cryptographic primitives are treated as state transition operations and a set of operations is constructed. The correspondence between each state transition operation and the resource overhead vector is set as a cost function. State transition operations on the state space set are defined as state transition functions, and hard constraints that must be satisfied for successful execution of the operation are set according to the logical relationships in the pre-built cryptographic primitive knowledge base. The state space set, the composite security objective corresponding to the baseline cryptographic policy, the operation set, the state transition function and the cost function are formally encoded into a state transition model, and the composite security objective obtained through reasoning is set as the endpoint state of the state transition model. In the state transition model, the objective function is constructed with the goal of minimizing the total resource consumption cost of cryptographic primitives in the state transition operation sequence from the initial state to the final state. The constraints are that the overhead generated by the execution of the cryptographic policy is less than the planning budget, and that each cryptographic primitive in the state transition operation sequence must satisfy the hard constraints. Thus, an adaptive optimization model for the cryptographic policy is constructed.

10. The method of claim 4, wherein, The method, based on business status and baseline cryptographic strategy, and under budget constraints, employs a multi-objective optimization approach to find the optimal cryptographic primitive combination sequence as the final cryptographic strategy, including: Based on the trust level in the business state, the business interaction context in the business state is integrated, and formal reasoning is performed using business domain knowledge rules to dynamically generate the security goals to be achieved in this interaction. The level that each security goal needs to be achieved is quantified based on expert knowledge. By leveraging expert knowledge to score the security assets involved in business interactions, the security value to be protected by cryptographic strategies is quantified. The resource overhead and security value of each cryptographic primitive in the cryptographic primitive knowledge base are normalized, and the message increment is used as the budget. To maximize security value while minimizing the resource overhead caused by applying cryptographic primitive combination sequences, a multi-objective optimization model for cryptographic policies is constructed, with the constraint that the sum of message increments of all operations in the cryptographic primitive combination sequence cannot exceed the budget, and the baseline cryptographic policy as the initial boundary condition. The multi-objective optimization model of the cryptographic strategy is solved using a two-level optimization method to obtain the optimal cryptographic primitive combination sequence as the final cryptographic strategy; The formal reasoning methods include: domain-specific language models or domain-specific knowledge graphs; the business domain knowledge rules include rules that are insufficient in security and / or require security enhancement.

11. The method as described in claim 9 or 10, characterized in that, The construction process of the cryptographic primitive knowledge base includes: Extract the complete set of cryptographic primitives required by the current system business, and construct a cryptographic primitive library usable by the system based on the complete set of cryptographic primitives. Each cryptographic primitive in the cryptographic primitive library includes: a unique identifier, an operation type signature, and resource overhead. Based on business domain knowledge, fine-tune the domain-specific language model, or based on business domain knowledge graphs and cryptographic ontology, combine logical reasoning or graph neural network technology to analyze and extract the logical relationships between members in the cryptographic primitive library. The logical relationships include: security strength partial order relationship, dependency relationship and security goal relationship. The logical relationships are described as logical rules, and a cryptographic primitive knowledge base is constructed in a structured manner by combining them with a preset set of security objectives.

12. The method as described in claim 1, characterized in that, The step of matching the corresponding baseline cryptographic policy according to the business type includes: When no baseline cryptographic policy pool exists, return an empty set as the baseline cryptographic policy. When a baseline cryptographic policy pool exists, the most basic baseline cryptographic policy applicable to that business type is found in the baseline cryptographic policy pool based on the business type.

13. The method as described in claim 12, characterized in that, The process of constructing the baseline cryptographic policy pool includes: Defined based on expert knowledge, a baseline cryptographic policy pool is obtained; or By employing multiple analysis methods and their arbitrary combinations, candidate baseline cryptographic strategy pools are obtained, and the candidate baseline cryptographic strategy pools are adjusted based on expert knowledge or domain-specific knowledge graphs to generate a baseline cryptographic strategy pool. The analysis methods include, but are not limited to, the following: analysis based on historical operation logs, analysis based on existing code, or analysis based on expert knowledge and / or design documents.

14. A cryptographic policy adaptive orchestration system, characterized in that, The system is deployed in the dominant node of a master-slave node group or in any node of an equal node group. The system includes: a status module, a baseline cryptographic policy module, and a cryptographic policy module. The status module is used to evaluate the business status based on the collected system architecture, business interaction path, business interaction information and interaction node information; The baseline cryptographic policy module is used to match the corresponding baseline cryptographic policy according to the business type; The cryptographic strategy module is used to solve for the optimal cryptographic primitive combination sequence as the final cryptographic strategy under the planning budget constraint, based on the business status and baseline cryptographic strategy. The status module assesses the business status based on the collected system architecture, business interaction paths, business interaction information, and interaction node information, including: Based on the static information collected, including system architecture, business interaction paths, business interaction information, and interaction node information, the status of basic business is assessed offline. From the start to the end of the business process, the business status is obtained by updating the basic business status obtained in the offline process online based on the dynamic information of node interaction generated by the dynamic changes in the collected system architecture, business interaction path, business interaction information and interaction node information.

15. The password policy adaptive orchestration system of claim 14, wherein, The status module offline evaluates the basic business status based on static information collected from the system architecture, business interaction paths, business interaction information, and interaction node information, including: Trust levels are calculated by combining a pre-set trust assessment model with information from interactive nodes. Based on data mining technology, we can initially extract the trigger rule candidate set of the associated business of the business corresponding to the business interaction information, as well as the trigger rule candidate set of the most likely subsequent business after the corresponding business ends. By combining expert knowledge or domain-specific knowledge graphs, the candidate set of triggering rules for associated and subsequent services is filtered to obtain the set of triggering rules for associated and subsequent services as the offline service information evaluation result; Based on the system architecture and business interaction channels, the trust level and / or offline business information evaluation results are adjusted according to the domain-specific language model or expert knowledge to obtain the basic business status of offline evaluation. The offline assessment is performed when the system architecture, business interaction path, or interaction node information changes, or periodically at time intervals set according to the business type; the trust assessment model includes one or more of the following: weighted summation or machine learning classifier; the data mining technology includes one or more of the following: sequence pattern mining, time series rule learning, or process mining.

16. The password policy self-adaptive orchestration system of claim 15, wherein, During the service process from start to finish, the status module updates the basic service status obtained offline to obtain the service status online based on the dynamic information of node interactions generated by the dynamic changes in the collected system architecture, service interaction path, service interaction information, and interaction node information. This includes: During the process from the start to the end of the business, collect dynamic information of node interactions related to the current data interaction node, and update the trust level by combining the dynamic information of node interactions, the interaction node information used in the offline evaluation, and the trust level of the offline evaluation through a preset trust evaluation model. Collect dynamic information about business interactions related to the current business interaction. Based on the evaluation results of offline business information obtained from offline evaluation, use the dynamic information about business interactions as triggering conditions to evaluate the set of triggering rules for associated business and subsequent business in the evaluation results of offline business information. Finally, obtain the set of associated business and the set of subsequent business associated with the current business to form the business interaction context. The updated trust level and business interaction context are used as the business state. Among them, online updates are performed continuously or periodically; the node interaction dynamic information includes one or more of the following: the deviation between the current behavior pattern and the historical behavior pattern in the interaction node information collected during the offline evaluation process, or the real-time network environment in which the node is located; the business interaction dynamic information includes one or more of the following: the interaction time of the current business, the frequency of interaction, or the sensitivity of interaction data.

17. The password policy adaptive orchestration system of claim 16, wherein, The cryptographic policy module is specifically used for: Based on the business status and baseline cryptographic strategy, and under the constraint of a planning budget, a planning approach is used to solve for the optimal cryptographic primitive combination sequence as the final cryptographic strategy; or Based on the business status and baseline cryptographic strategy, and under the constraint of the planning budget, a multi-objective optimization approach is adopted to solve for the optimal cryptographic primitive combination sequence as the final cryptographic strategy.

18. The password policy self-adaptive orchestration system of claim 17, wherein, The cryptographic strategy module, based on the business state and baseline cryptographic strategy, and under budget constraints, uses a planning approach to solve for the optimal cryptographic primitive combination sequence as the final cryptographic strategy, including: Based on the aforementioned business interaction channels, system architecture, business status, and baseline cryptographic policies, formal reasoning is performed using business domain knowledge rules to obtain a composite security objective. Starting with the composite security objective corresponding to the baseline cryptographic strategy, and ending with the composite security objective derived through reasoning, the available cryptographic primitives are used as planning actions, the resource consumption cost of the cryptographic primitives is used as the optimization objective, and the overhead generated by the execution of the cryptographic strategy is used as the planning budget. The optimal sequence of cryptographic primitive combinations is then obtained through planning and solving, which is taken as the final cryptographic strategy.

19. The password policy self-adaptive orchestration system of claim 18, wherein, The cryptographic policy module, based on the business interaction path, system architecture, business status, and baseline cryptographic policy, uses business domain knowledge rules for formal reasoning to obtain a composite security objective, including: Based on the business interaction path, system architecture, business state, and baseline cryptographic policy, formal reasoning is performed using business domain knowledge rules to determine whether the baseline cryptographic policy is sufficiently secure under the current business interaction path and system architecture configuration, given the trust level of the interaction node and the current business interaction context. If so, the security objective corresponding to the baseline cryptographic strategy shall be used as the composite security objective; Otherwise, a composite security objective is obtained based on the security objective corresponding to the baseline cryptographic policy, which is enhanced or adjusted. The formal reasoning methods include: domain-specific language models or domain-specific knowledge graphs, wherein the business domain knowledge rules include rules that are insufficient in security and / or require security enhancement.

20. The password policy self-adaptive orchestration system of claim 18, wherein, The composite security objectives in the cryptographic policy module are declarative.

21. The password policy adaptive orchestration system of claim 18, wherein, The cryptographic strategy module takes the composite security objective corresponding to the baseline cryptographic strategy as the planning starting point, the composite security objective obtained through reasoning as the planning endpoint, the available cryptographic primitives as the planning actions, the resource consumption cost of the cryptographic primitives as the optimization objective, and the overhead generated by the execution of the cryptographic strategy as the planning budget. The optimal sequence of cryptographic primitive combinations is then obtained through the planning solution, which serves as the final cryptographic strategy. This includes: Taking the composite security objective corresponding to the baseline cryptographic policy as the planning starting point, the composite security objective obtained through reasoning as the planning endpoint, the available cryptographic primitives as the planning actions, the resource consumption cost of the cryptographic primitives as the optimization objective, and the overhead generated by the execution of the cryptographic policy as the planning budget, an adaptive optimization model for the cryptographic policy is constructed. The optimal sequence of cryptographic primitive combinations is obtained by solving the adaptive optimization model of the cryptographic strategy, which is then used as the final cryptographic strategy.

22. The password policy adaptive orchestration system of claim 21, wherein, The cryptographic policy module takes the composite security objective corresponding to the baseline cryptographic policy as the planning starting point, the inferred composite security objective as the planning endpoint, the available cryptographic primitives as the planning actions, the resource consumption cost of the cryptographic primitives as the optimization objective, and the overhead generated by the execution of the cryptographic policy as the planning budget, constructing an adaptive optimization model for the cryptographic policy, including: All possible states of the system are set as a state space set. Each state contains a set of security objectives that the current cryptographic policy can satisfy. The baseline cryptographic policy is analyzed to obtain the corresponding composite security objectives as the initial state for planning. The cryptographic primitives are treated as state transition operations and a set of operations is constructed. The correspondence between each state transition operation and the resource overhead vector is set as a cost function. State transition operations on the state space set are defined as state transition functions, and hard constraints that must be satisfied for successful execution of the operation are set according to the logical relationships in the pre-built cryptographic primitive knowledge base. The state space set, the composite security objective corresponding to the baseline cryptographic policy, the operation set, the state transition function and the cost function are formally encoded into a state transition model, and the composite security objective obtained through reasoning is set as the endpoint state of the state transition model. In the state transition model, the objective function is constructed with the goal of minimizing the total resource consumption cost of cryptographic primitives in the state transition operation sequence from the initial state to the final state. The constraints are that the overhead generated by the execution of the cryptographic policy is less than the planning budget, and that each cryptographic primitive in the state transition operation sequence must satisfy the hard constraints. Thus, an adaptive optimization model for the cryptographic policy is constructed.

23. The system of claim 17, wherein, The cryptographic strategy module, based on business status and baseline cryptographic strategy, and under budget constraints, employs a multi-objective optimization approach to find the optimal cryptographic primitive combination sequence as the final cryptographic strategy, including: Based on the trust level in the business state, the business interaction context in the business state is integrated, and formal reasoning is performed using business domain knowledge rules to dynamically generate the security goals to be achieved in this interaction. The level that each security goal needs to be achieved is quantified based on expert knowledge. By leveraging expert knowledge to score the security assets involved in business interactions, the security value to be protected by cryptographic strategies is quantified. The resource overhead and security value of each cryptographic primitive in the cryptographic primitive knowledge base are normalized, and the message increment is used as the budget. To maximize security value while minimizing the resource overhead caused by applying cryptographic primitive combination sequences, a multi-objective optimization model for cryptographic policies is constructed, with the constraint that the sum of message increments of all operations in the cryptographic primitive combination sequence cannot exceed the budget, and the baseline cryptographic policy as the initial boundary condition. The multi-objective optimization model of the cryptographic strategy is solved using a two-level optimization method to obtain the optimal cryptographic primitive combination sequence as the final cryptographic strategy; The formal reasoning methods include: domain-specific language models or domain-specific knowledge graphs; the business domain knowledge rules include rules that are insufficient in security and / or require security enhancement.

24. The system of claim 22 or 23, wherein, The construction process of the cryptographic primitive knowledge base in the cryptographic policy module includes: Extract the complete set of cryptographic primitives required by the current system business, and construct a cryptographic primitive library usable by the system based on the complete set of cryptographic primitives. Each cryptographic primitive in the cryptographic primitive library includes: a unique identifier, an operation type signature, and resource overhead. Based on business domain knowledge, fine-tune the domain-specific language model, or based on business domain knowledge graphs and cryptographic ontology, combine logical reasoning or graph neural network technology to analyze and extract the logical relationships between members in the cryptographic primitive library. The logical relationships include: security strength partial order relationship, dependency relationship and security goal relationship. The logical relationships are described as logical rules, and a cryptographic primitive knowledge base is constructed in a structured manner by combining them with a preset set of security objectives.

25. The system of claim 14, wherein, The baseline cryptographic policy module matches the corresponding baseline cryptographic policy according to the service type, including: When no baseline cryptographic policy pool exists, return an empty set as the baseline cryptographic policy. When a baseline cryptographic policy pool exists, the most basic baseline cryptographic policy applicable to that business type is found in the baseline cryptographic policy pool based on the business type.

26. The system of claim 25, wherein, The process of constructing the baseline cryptographic policy pool in the baseline cryptographic policy module includes: Defined based on expert knowledge, a baseline cryptographic policy pool is obtained; or By employing multiple analysis methods and their arbitrary combinations, candidate baseline cryptographic strategy pools are obtained, and the candidate baseline cryptographic strategy pools are adjusted based on expert knowledge or domain-specific knowledge graphs to generate a baseline cryptographic strategy pool. The analysis methods include, but are not limited to, the following: analysis based on historical operation logs, analysis based on existing code, or analysis based on expert knowledge and / or design documents.

27. An electronic device, characterized in that, include: At least one processor and memory; The memory and processor are connected via a bus; The memory is used to store one or more programs; When the one or more programs are executed by the at least one processor, a cryptographic policy adaptive orchestration method as described in any one of claims 1 to 13 is implemented.

28. A computer-readable storage medium, characterized in that, It contains an executable program, which, when executed, implements a cryptographic strategy adaptive arrangement method as described in any one of claims 1 to 13.

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