Zero-trust framework design method based on smart contract

Abstract

The invention relates to a zero-trust framework design method based on an intelligent contract, and belongs to the field of artificial intelligence models and block chains. The intelligence of the method is mainly reflected in two aspects of contract intelligence and authentication intelligence. The intelligence of the contract is embodied in that the contract can be elastically changed, instead of being based on an established fixed contract, the authentication node and the authentication mode of the node can be changed based on a block chain form; the authentication intelligence is embodied in that specific authentication modes, authentication strength and different authentication modes of authentication behaviors in authentication based on an artificial intelligence model can be switched according to specific authentication requirements, matching and adjustment can be carried out according to actual requirements, and safe and flexible authentication under large-scale complex authentication requirements is met.

Description

Technical Field

[0001] The present invention belongs to the field of artificial intelligence models and blockchains, and specifically relates to a zero-trust framework design method based on smart contracts. Background Art

[0002] A smart contract is an agreement to automatically execute corresponding actions based on predetermined conditions. In concrete form, it is a series of computer codes that are executed according to specific conditions. The nature of smart contracts is mainly to authorize the compliance of specific actions and the operability of action execution to the blockchain based on the distributed verification and decentralized trust system of the blockchain, and to ensure the immutability and non-repudiation of action conditions and action execution through the joint verification of users on the chain and the pre-agreed action execution mechanism.

[0003] The zero-trust system is a computer or information system trust architecture or system built on a trust method based on the idea of ​​never trusting and continuous authentication. The main feature of zero trust is that it can maximize the controllability of relevant information permissions and the operational space of corresponding permissions, and achieve fine-grained permission control for different people in different states and different devices in the same information system at different stages, meeting the current and future complex information system architecture, complex business, huge data volume, serious personnel overlap and other scenarios of personnel and equipment security status control.

[0004] However, with the increasingly complex computing and information system usage environment, the number of zero-trust trust chains has increased sharply, the number of authentication tasks at various stages of the same task is huge, the efficiency of traditional authentication is extremely low, and the contradiction between authentication application scheduling and business scheduling is obviously intensifying. It is urgent to develop a dual technical framework that can meet both the security enhancement needs of authentication and the efficient development of corresponding businesses.

[0005] The intelligence in traditional smart contracts is based on the automated execution of pre-negotiated authentication mechanisms and authentication execution conditions. When a certain authentication execution condition is met, the authentication action under the corresponding condition is automatically executed, so once the authentication condition is negotiated, it cannot be changed. Although traditional smart contracts have greatly alleviated the complexity of authentication in solving the problem of automated execution of authentication actions, with the development of the network and the surge in the number of multi-scenario authentication needs, the immutability of the authentication protocol and the intelligent development of authentication urgently need to be truly based on intelligent models, fully combining intelligent authentication condition negotiation with the immutability of blockchain and distributed trust mechanisms, forming an authentication protocol negotiation authentication framework based on intelligent models, replacing the pre-set authentication mechanism in traditional smart contracts, and forming an intelligent authentication system with variable and adjustable authentication conditions and a record of the authentication process on the blockchain. Summary of the invention

[0006] 1. Technical issues to be resolved

[0007] The technical problem to be solved by the present invention is how to provide a zero-trust framework design method based on smart contracts to solve the problems of the invariance of the authentication protocol and the intelligent development needs of authentication.

[0008] (II) Technical solution

[0009] In order to solve the above technical problems, the present invention proposes a zero-trust framework design method based on smart contracts, which includes the following steps:

[0010] Step 1: Build a trust verification node for the zero trust framework

[0011] By setting trust verification demand nodes based on the needs of actual scenarios, that is, setting authentication nodes with different security strengths at different time and space nodes according to the specific needs of actual scenarios, the authentication nodes in the authentication process are adjustable;

[0012] Step 2: Build a smart contract model

[0013] The smart contract model is the contract module in the blockchain module, which generates different trigger conditions for the generation and change of contracts in each block. When designing the smart contract model, the historical authentication trigger conditions and corresponding authentication behaviors are used as the original data for training the model. Through different authentication trigger conditions and corresponding authentication behaviors, the basic smart contract model is learned and generated. Then, the correctness of the matching relationship between the current authentication trigger conditions and authentication behaviors is tested as the detection standard for the quality of model learning. The difference between the authentication behavior learned by the model and the actual authentication behavior is used as the input value of learning feedback, and the adjustment and optimization of the model is completed through repeated iterations.

[0014] Step 3: Set up a smart authentication model based on the smart contract model

[0015] Based on the smart contract model trained in the second step, a specific smart authentication model is set. The smart authentication model makes a comprehensive decision to match the real authentication mode for different authentication factors in the same authentication condition.

[0016] Step 4: Set up a zero-trust smart contract framework based on the smart contract model in the second step and the smart authentication model in the third step.

[0017] (III) Beneficial effects

[0018] The present invention proposes a zero-trust framework design method based on smart contracts. The present invention proposes an intelligent authentication framework based on artificial intelligence models and blockchains. The intelligence is mainly reflected in the two aspects of contract intelligence and authentication intelligence. The intelligence of the contract is reflected in the fact that the contract can be changed flexibly. It is not based on an established fixed contract, but can change the authentication node and the authentication method of the node in the form of blockchain; the intelligence of authentication is reflected in the fact that the specific authentication method, authentication strength and different authentication modes of the authentication behavior in the authentication based on the artificial intelligence model can be switched according to specific authentication requirements, and can be matched and adjusted according to actual needs to meet the security and flexible authentication under large-scale complex authentication requirements. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 This is a zero-trust framework diagram based on smart contracts in the present invention. DETAILED DESCRIPTION

[0020] In order to make the purpose, content and advantages of the present invention more clear, the specific implementation methods of the present invention are further described in detail below in conjunction with the drawings and examples.

[0021] The zero-trust authentication mechanism based on blockchain smart contracts in the present invention can provide an authentication framework that meets actual needs to solve the above problems, and fully utilizes the contract formulation sharing mechanism, contract self-triggered execution mechanism, and contract non-tamperability characteristics of smart contracts in blockchain technology. It not only solves the continuous authentication needs of continuously verifying user behavior in the zero-trust mechanism, but also can self-trigger authentication actions through the automatic execution mechanism in the blockchain smart contract mechanism, thereby improving the efficiency of authentication without affecting the normal progress of the business.

[0022] The specific execution action in the blockchain-based smart contract mechanism in the present invention is the authentication behavior. The specific conditions for executing the authentication behavior are the same as the specific authentication conditions in the zero-trust mechanism. They are all specific authentication conditions determined based on the defined authentication requirements. The intelligence in the smart contract zero-trust authentication system in the present invention is reflected in the two aspects of the intelligence of the contract and the intelligence of the authentication. The specific framework of the smart contract zero-trust authentication system is as follows: Figure 1 shown.

[0023] The main principles of the present invention are as follows:

[0024] Build a trust verification node for the zero-trust framework. By setting trust verification demand nodes based on the needs of actual scenarios, that is, setting authentication nodes of different strengths at different time and space nodes according to the needs of actual scenarios, the authentication and nodes in the process are adjustable, that is, the number of authentication nodes and authentication nodes can be adjusted according to actual needs. However, all actions at the beginning and in the process, including the addition and withdrawal of nodes and changes in authentication behavior for the same node, are initiated through the sharing of the blockchain and confirmed on the chain. Each action can be recorded, checked, traced and cannot be tampered with on the chain.

[0025] Build an intelligent model for setting trust conditions. This model is the smart contract model in the block, which generates different trigger conditions for the generation and change of contracts, rather than generating them by pre-agreed lifelong unchanged. The design of this model comprehensively utilizes historical authentication conditions as the original data for model generation and training. Through different authentication trigger conditions and corresponding authentication behaviors, the model learns the matching relationship between trigger conditions and authentication behaviors to generate the basic model of the smart contract, and then learns the specific authentication behavior through the current authentication trigger conditions, and judges the matching relationship between the learned authentication behavior and the real authentication behavior, and uses the result of this learning as the detection standard for the quality of model learning. The difference between the matching result learned by the model and the real result is used as the input value of learning feedback. Specifically, it can be carried out by quantifying the authentication mode and the matching mode value learned by the model and the different norm modes of the difference between the two, and the size of this difference or other quantitative indicators to learn a well-performing intelligent model. The sign of the end of model learning can be comprehensively determined by the number of feedback iterations and the accuracy of learning. Specifically, it can be determined according to different authentication conditions and the security strength of the authentication mode. For example, authentication with different security strengths in specific authentication scenarios can correspond to the accuracy of the model under different corresponding conditions.

[0026] Construct an intelligent authentication model for authentication. This model can make a comprehensive decision to match the real authentication mode for different authentication factors in the same authentication condition, such as multi-mode matching based on different static and dynamic biometrics, identity identification, and public key certificates in multi-factor authentication. Specifically, the authentication results can be divided into different levels, and the levels can be determined based on actual conditions.

[0027] A zero-trust smart contract framework is set up based on the trained and verified smart contract model and smart authentication model. A specific contract framework is set up based on the two artificial intelligence models trained in the previous step. The contract framework can be divided into multiple levels of contract constraints. For example, different contract modes can be set up on different blockchains, that is, contract modes on public chains, contract modes on private chains, contract modes on alliance chains, etc. Contract verification is performed on the chain according to different contracts in different modes, and comprehensive verification is performed between different blocks in combination with the chain form and authentication mode.

[0028] The present invention proposes a zero-trust framework design method based on smart contracts, which specifically includes the following steps:

[0029] Step 1: Build a trust verification node for the zero trust framework.

[0030] By setting trust verification demand nodes based on the needs of actual scenarios. That is, according to the specific needs of the actual scenarios, authentication nodes with different security strengths are set at different time and space nodes, so that the authentication nodes in the authentication process are adjustable, that is, the setting of authentication nodes and the number of authentication nodes can be adjusted according to actual needs. However, all actions from the beginning to the end, including the addition and withdrawal of nodes and changes in the authentication mode for the same node, are initiated through the sharing of the blockchain and confirmed on the blockchain, ensuring that each action can be recorded, checked, traced and cannot be tampered with on the chain.

[0031] Step 2: Build a smart contract model.

[0032] The smart contract model is the contract module in the blockchain module. Different trigger conditions are generated for the generation and change of contracts in each block, rather than being agreed upon in advance. When designing this smart contract model, the historical authentication trigger conditions and corresponding authentication behaviors are used as the original data for training this model. Through different authentication trigger conditions and corresponding authentication behaviors, the basic smart contract model is learned and generated. Then, the correctness of the matching relationship between the current authentication trigger conditions and authentication behaviors is tested as the detection standard for the quality of model learning. The difference between the authentication behavior learned by the model and the actual authentication behavior is used as the input value of learning feedback, and the adjustment and optimization of the model is completed through repeated iterations.

[0033] Specifically, this can be done by quantifying the authentication pattern and the matching pattern values ​​obtained by model learning and different norm patterns of the difference between the two, and a model with good performance can be obtained through the size of the difference or other quantitative indicators.

[0034] The sign of the end of model learning can be determined by the number of feedback iterations and the accuracy of learning, and can be determined based on the matching degree of different authentication trigger conditions and authentication behaviors.

[0035] Step 3: Set up a smart authentication model based on the smart contract model.

[0036] Based on the smart contract model trained in the second step, a specific smart authentication model is set up. The smart authentication model can make a comprehensive decision to match the real authentication mode for different authentication factors in the same authentication condition. For example, in multi-factor authentication, multiple modes are matched based on different static and dynamic biometrics, identity identification, and public key certificates. Specifically, the authentication factor combination modes can be divided into different security levels according to the number of authentication factors and the security strength of the factors. The level needs to be determined based on actual needs.

[0037] Step 4: Set up a zero-trust smart contract framework based on the smart contract model in step 2 and the smart authentication model in step 3

[0038] The smart contract framework can be divided into multiple levels of contract constraints. For example, different contract modes can be set on different blockchains, that is, contract modes on public chains, contract modes on private chains, contract modes on alliance chains, etc., and contract verification on the chain is performed according to different contracts in different modes, and comprehensive verification is performed between different blocks of the blockchain in combination with the chain form and authentication mode.

[0039] Step 5: Update and upgrade the model on different blocks and different chain modes.

[0040] The artificial intelligence model in the smart contract has the ability to learn and upgrade and evolve. It can be updated and upgraded on different blocks and different chain modes, and thus can enhance or weaken the trust strength of zero trust according to actual specific needs. The strength and authentication method of zero trust can both serve as the basic components of zero trust, and can be adjusted and changed on the trust verification chain.

[0041] The present invention proposes an intelligent authentication framework based on artificial intelligence model and blockchain, and the intelligence is mainly reflected in the two aspects of contract intelligence and authentication intelligence. The intelligence of the contract is reflected in the fact that the contract can be changed flexibly. It is not based on an established fixed contract, but can change the authentication node and the authentication method of the node in the form of blockchain; the intelligence of authentication is reflected in the specific authentication method, authentication strength and different authentication modes of the authentication behavior in the authentication based on the artificial intelligence model. It can be switched according to specific authentication requirements, and can be matched and adjusted according to actual needs to meet the security and flexible authentication under large-scale complex authentication requirements.

[0042] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A zero-trust framework design method based on smart contracts, characterized in that: The method comprises the following steps: Step 1: Build a trust verification node for the zero trust framework By setting trust verification demand nodes based on the needs of actual scenarios, that is, setting authentication nodes with different security strengths at different time and space nodes according to the specific needs of actual scenarios, the authentication nodes in the authentication process are adjustable; Step 2: Build a smart contract model The smart contract model is the contract module in the blockchain module, which generates different trigger conditions for the generation and change of contracts in each block; When designing the smart contract model, the historical authentication trigger conditions and corresponding authentication behaviors are used as the original data for training the model. Through different authentication trigger conditions and corresponding authentication behaviors, the basic smart contract model is learned and generated. Then, the correctness of the matching relationship between the current authentication trigger conditions and authentication behaviors is tested as the detection standard for the quality of model learning. The difference between the authentication behavior learned by the model and the actual authentication behavior is used as the input value of learning feedback, and the adjustment and optimization of the model is completed through repeated iterations. Step 3: Set up a smart authentication model based on the smart contract model Based on the smart contract model trained in the second step, a specific smart authentication model is set. The smart authentication model makes a comprehensive decision to match the real authentication mode for different authentication factors in the same authentication condition. Step 4: Set up a zero-trust smart contract framework based on the smart contract model in the second step and the smart authentication model in the third step.

2. The method for designing a zero-trust framework based on smart contracts as claimed in claim 1, characterized in that: The setting of authentication nodes and the number of authentication nodes are adjusted according to actual needs.

3. The method for designing a zero-trust framework based on smart contracts as claimed in claim 2, characterized in that: All actions from the beginning to the end, including the addition and withdrawal of nodes and changes in the authentication mode of the same node, are initiated through the sharing of the blockchain and confirmed on the blockchain, ensuring that each action can be recorded, traceable and cannot be tampered with on the chain.

4. The method for designing a zero-trust framework based on smart contracts as claimed in claim 1, characterized in that: In the second step, the values ​​of the matching pattern obtained by quantifying the authentication pattern and the model learning and different norm patterns of the difference between the two are performed, and a model with good performance is learned through the size of the difference or other quantitative indicators.

5. The method for designing a zero-trust framework based on smart contracts as claimed in claim 4, characterized in that: In the second step, the sign of the end of model learning is determined by the number of feedback iterations and the accuracy of learning, and can be determined according to the matching degree of different authentication trigger conditions and authentication behaviors.

6. The method for designing a zero-trust framework based on smart contracts as claimed in claim 1, characterized in that: The intelligent authentication model in the third step performs multiple pattern matching based on different static and dynamic biometric features, identity identification and public key certificates in multi-factor authentication.

7. The method for designing a zero-trust framework based on smart contracts as claimed in claim 6, characterized in that: In the third step, authentication factor combination modes are divided into different security levels according to the number of authentication factors and the security strength of the factors.

8. The method for designing a zero-trust framework based on smart contracts as claimed in claim 1, characterized in that: In the fourth step, the smart contract framework can be divided into multiple levels of contract constraints.

9. The method for designing a zero-trust framework based on smart contracts as claimed in claim 8, characterized in that: The fourth step includes: different contract models can be set on different blockchains, namely, contract models on public chains, contract models on private chains, and contract models on alliance chains, and contract verification is performed on the chain according to different contracts in different models, and comprehensive verification is performed between different blocks of the blockchain in combination with the chain form and authentication model.

10. The method for designing a zero-trust framework based on smart contracts according to claim 1, wherein: The fourth step also includes: Step 5: Updating the model on different blocks and different chain modes The artificial intelligence model in the smart contract has the ability to learn and upgrade and evolve. It can be updated and upgraded on different blocks and different chain modes, and can thus enhance or weaken the trust strength of zero trust according to actual specific needs. The strength and authentication method of zero trust can both serve as the basic components of zero trust, and can be adjusted and changed on the trust verification chain.

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