Trust management method, system and device based on multi-core-node dynamic community

Abstract

The invention discloses a trust management method, system and device based on a multi-core-node dynamic community, and the method comprises the steps: recognizing community structure changes through diffusion type and cohesion type core nodes, and achieving the updating of node community attributes through maximization of incremental modularity; a continuous trust evaluation system is constructed through a zero trust model, and a dynamic trust value is calculated in combination with user, equipment and behavior characteristics; and finally, when the node accesses the block chain network, a dynamic permission threshold adjustment technology is adopted, permission distribution is automatically optimized according to the node trust value and the network security condition, and the efficiency of node trust management and permission management and control is improved. According to the node trust management method, an accurate, safe and extensible node trust management scheme is realized in block chain application, and the method is suitable for a large-scale distributed network scene needing dynamic node management and control.

Description

Technical Field

[0001] The present invention belongs to the field of blockchain node trust management, and specifically relates to a trust management method, system and device based on a multi-core node dynamic community. Background Art

[0002] With the continuous development of blockchain networks, especially in areas such as large-scale distributed transactions, cross-chain collaborative operations, and complex business scenarios, node trust management faces the challenges of diverse node behaviors and complex network environments. Traditional node trust management mechanisms, relying on static evaluation criteria, often struggle to reflect dynamic node changes in real time. However, with the surge in the number of nodes, accurately assessing node trust and ensuring network security has become a critical issue. Existing node trust management solutions often suffer from lagging evaluation of dynamically joining nodes, insufficient response to sudden changes in community structure, and delayed identification of malicious behavior. Summary of the Invention

[0003] The present invention addresses the problems of lack of trust assessment in existing dynamic community discovery and difficulty of zero trust model in adapting to dynamic network evolution, and provides a trust management method, system and device based on multi-core node dynamic community.

[0004] In order to solve the above technical problems, the present invention is solved by the following technical solutions: A trust management method based on a multi-core node dynamic community includes the following steps: Obtain diffuse core nodes and cohesive core nodes, and update the node community affiliation through the incremental modularity maximization algorithm; Collect the user identity, device status and behavior data of the node, and obtain the corresponding independent trust value, and then obtain the node's comprehensive trust value; Generate dynamic permission thresholds based on node community affiliation. Conduct zero-trust continuous assessments based on node comprehensive trust values ​​and dynamic permission thresholds to implement node access control. The target permission threshold is obtained based on the dynamic permission threshold, and the node community affiliation and trust status are periodically verified and adjusted.

[0005] As an implementable method, the method of obtaining diffusion core nodes and cohesive core nodes and updating node community affiliation by using an incremental modularity maximization algorithm includes the following steps: The degree discount heuristic method is used to calculate the node degree discount value, and the nodes with the top 10% of the node degree discount value are selected as diffusion core nodes; Through the 2-core subgraph decomposition method, the node with the largest degree in the subgraph is iteratively deleted and the subgraph topology is dynamically updated until the subgraph is empty. The deleted nodes form a cohesive core node. When an incremental topology change occurs, determine the affected nodes, if there is a diffusion type core node in the affected nodes, then all nodes belonging to the community of the diffusion type core node are included in the unlabeled node set, if there is a cohesive type core node in the affected nodes, then the neighborhood nodes of the cohesive type core node are included in the set, wherein the incremental topology change includes node addition, node removal, edge addition and edge removal; Obtain the change amount of the modularity of the nodes in the unlabeled set, and perform community merging optimization through a modularity maximization algorithm to realize dynamic update of the node community attribution, wherein the change amount of the modularity is represented as follows:

[0006] Wherein, is the change amount of the modularity, is the number of edges in the network, is the node edge ratio, , is the node degree, is the increase or decrease state of the edge.

[0007] As an implementable manner, the degree discount heuristic method is used to calculate the node degree discount value, and the top 10% of the nodes with the node degree discount value are selected as the diffusion type core nodes, including the following steps: Obtain the node degree , the number of activated seeds in the neighbor nodes and the activation probability parameter ; Through the node degree, the number of activated seeds in the neighbor nodes and the activation probability parameter, the node degree discount value is calculated, and is represented as follows:

[0008] Sort the degree discount values of all nodes in descending order, and select the top 10% of the nodes as the diffusion type core nodes; Wherein, denotes the node degree of the th node, denotes the number of activated seeds in the neighbor nodes of the th node, denotes the activation probability parameter, denotes the node degree discount value of the th node.

[0009] As an implementable manner, the user identity, device state and behavior data of the nodes are collected, and the corresponding independent trust value is obtained, and then the node comprehensive trust value is obtained, including the following steps: Collect node user identity, device status, and behavior data. User identity includes account information and authentication method; device status includes MAC address and vulnerability detection results; and behavior data includes access frequency and operation type. Based on the sliding window mechanism, the historical data of the node is processed and different weights are assigned to different data, as shown below:

[0010] The independent trust values ​​corresponding to user identity, device status, and behavior data are calculated using a logistic regression model, as shown below:

[0011] By modifying the threat behavior factor, the node comprehensive trust value is obtained, which is expressed as follows:

[0012]

[0013] in, is the window size, is an adjustment coefficient used to highlight the impact of recent data on trust assessment. is the indicator impact factor, is the trust value of the measurement indicator, is the data weight, is a set of threat behaviors, is the influencing factor of each threat behavior, , is the weight of user identity, device status and behavior data, the sum of which is 1. is the total trust value of the access subject, is the comprehensive trust value of the node.

[0014] As an implementable method, the method generates a dynamic permission threshold based on the node community affiliation, performs zero-trust continuous evaluation based on the node comprehensive trust value and the dynamic permission threshold, and implements node access control, including the following steps: Initialize the permission threshold interval based on the sensitivity of the node community ; According to the cumulative number of accesses and the permission threshold range, the permission threshold is dynamically adjusted to obtain the dynamic permission threshold, which is expressed as follows:

[0015] If the node's comprehensive trust value is greater than the adjusted permission threshold, the corresponding access permission is granted, otherwise it is denied; in, To access the behavior associated parameters, is a threshold adjustment reference quantity, is a threshold adjustment reference quantity, is an adjustment step parameter, is a dynamic permission threshold, is a non-dangerous access frequency.

[0016] As an implementable manner, the periodic verification and adjustment of the node community attribution and trust state include the following steps: Periodically checking whether the node community attribution matches the network structure and verifying whether the node comprehensive trust value meets the behavior characteristics; Based on the verification result, the node community attribution and trust state are adjusted.

[0017] As an implementable manner, it further includes: Based on the verification result, a penalty mechanism is used to correct the target permission threshold of the malicious node to obtain a corrected target permission threshold, which is represented as follows:

[0018] Based on the corrected target permission threshold and the community structure, the trust evaluation model and the permission control strategy are iteratively optimized to limit the permission acquisition of the malicious node through the mechanism; wherein, is a penalty coefficient, is an operation level, is a total number of levels, is a target permission threshold, is a corrected target permission threshold.

[0019] A trust management system based on a multi-core node dynamic community includes a node acquisition module, a trust value calculation module, an access control module, and a trust management module. The node acquisition module acquires the diffusion-type core node and the cohesive-type core node, and updates the node community attribution through an incremental module degree maximization algorithm; The trust value calculation module collects user identity, device state, and behavior data of the node, and obtains corresponding independent trust values, and then obtains a node comprehensive trust value; The access control module generates a dynamic permission threshold based on the node community attribution, and performs zero-trust continuous evaluation through the node comprehensive trust value and the dynamic permission threshold to realize node access control; The trust management module obtains a target permission threshold based on the dynamic permission threshold, and periodically verifies and adjusts the node community attribution and trust state.

[0020] A computer readable storage medium stores a computer program, and the computer program is executed by a processor to implement the method as follows: obtain the diffusion type core node and the cohesive type core node, and update the node community attribution through an incremental modularity maximization algorithm; collect the user identity, device state and behavior data of the node, and obtain the corresponding independent trust value, and then obtain the node comprehensive trust value; based on the node community attribution, generate a dynamic permission threshold, perform zero-trust continuous evaluation through the node comprehensive trust value and the dynamic permission threshold, and realize node access control; based on the dynamic permission threshold, obtain a target permission threshold, and periodically verify and adjust the node community attribution and the trust state.

[0021] A trust management device based on a dynamic community of multiple core nodes, comprising a memory, a processor and a computer program stored in the memory and running on the processor, and the processor implements the method as follows when executing the computer program: obtain the diffusion type core node and the cohesive type core node, and update the node community attribution through an incremental modularity maximization algorithm; collect the user identity, device state and behavior data of the node, and obtain the corresponding independent trust value, and then obtain the node comprehensive trust value; based on the node community attribution, generate a dynamic permission threshold, perform zero-trust continuous evaluation through the node comprehensive trust value and the dynamic permission threshold, and realize node access control; based on the dynamic permission threshold, obtain a target permission threshold, and periodically verify and adjust the node community attribution and the trust state.

[0022] The present application has the following technical effects: (1) The core node accurately captures the community dynamics, solves the problem that the traditional trust management cannot adapt to network evolution, and improves the average performance index of modularity by 28%; (2) Combined with the incremental update strategy and dynamic trust measurement, the accuracy of community division is ensured, the trust value is corrected in real time, and the security and stability of permission control in a dynamic network environment are improved.

[0023] Other advantages, objects and features of the present application will be embodied in part by the following description, and will be understood by those skilled in the art through research and practice of the present application. BRIEF DESCRIPTION OF DRAWINGS

[0024] Figure 1 is a flowchart of the method of the present application; Figure 2 is a module schematic diagram of the system of the present application; Figure 3 is a specific flowchart of the method of the present application. DETAILED DESCRIPTION

[0025] In order to make the purpose, technical scheme and advantages of the embodiments of the present application more clear, the technical scheme in the embodiments of the present application is described clearly and completely below with reference to the drawings in the embodiments of the present application, so that those skilled in the art can implement the present application according to the description.

[0026] In the context of the present application, from the perspective of network topology evolution, the dynamic changes of community structure are captured by identifying diffusion-type and cohesive-type core nodes, and the group correlation basis is provided for node trust management. The zero-trust model realizes fine-grained access control by continuously evaluating the trust status of nodes from the perspective of identity authentication and permission control. The combination of the two can cope with the challenges of diversification of node behavior and complexity of community structure in dynamic networks. The basic principles of related technologies are known to those skilled in the art, but after reading this application, those skilled in the art will know how to integrate and use these technologies in this context, and will clearly know that the integration technology has novelty when combined with other features in specific contexts.

[0027] The technical scheme of the present application will be described in detail below with reference to the drawings and specific embodiments.

[0028] Embodiment 1: A trust management method based on dynamic community of multi-core nodes, as shown in Figure 1 includes the following steps: S100, diffusion-type core nodes and cohesive-type core nodes are obtained, and the node community attribution is updated through an incremental modularity maximization algorithm; S200, user identity, device state and behavior data of the node are collected, and corresponding independent trust values are obtained, and then the node comprehensive trust value is obtained; S300, based on the node community attribution, a dynamic permission threshold is generated, and the node access control is realized by the zero-trust continuous evaluation through the node comprehensive trust value and the dynamic permission threshold; S400, based on the dynamic permission threshold, a target permission threshold is obtained, and the node community attribution and trust status are periodically verified and adjusted.

[0029] In the process of core node identification and community dynamic update, the diffusion-type core nodes are screened based on the degree discount heuristic method, and the cohesive-type core nodes are extracted by 2-core subgraph disassembly; for the increase and decrease of nodes / edges, the affected nodes are included in the set , the node community attribution is output, wherein the node is all related nodes affected by the increment change of the network topology; the identity attribute, device state and behavior data of the node are collected, the independent trust value is calculated by using a sliding window mechanism and a logistic regression model, and the comprehensive trust value is obtained by combining the threat factor correction; the permission threshold is dynamically adjusted based on the node community attribution and the comprehensive trust value, the access permission is determined by comparing the trust value with the threshold value, and the dynamic permission control is realized; for the malicious node, a punishment mechanism is set to reduce the trust upper limit to limit the permission acquisition, and a specific flowchart of the method is shown in Figure 3 .

[0030] (1) Core node identification and community dynamic update, including the following steps: obtaining the node degree , the number of activated seeds in the neighbor and the activation probability , calculating the node degree discount value based on the degree discount heuristic formula , and selecting the top 10% nodes as diffusion-type core nodes. Among them, is the degree centrality of the node itself, is the number of activated seeds in the neighbor node, is the activation probability of the edge, and through the formula, the influence of the node itself and the activation of the neighbor node can be considered comprehensively, and the diffusion-type core node with significant propagation effect can be accurately selected.

[0031] Identify the 2-core subgraph of the community, iteratively delete the node with the largest degree in the subgraph and update the subgraph structure until the subgraph is empty, and the deleted node forms a set of cohesive core nodes. The 2-core subgraph refers to the largest subgraph with a degree of at least 2, and by continuously deleting the node with the largest degree, the nodes that are crucial to maintaining the connectivity of the community, i.e. the cohesive core nodes, can be found. These nodes highlight the cohesive properties between nodes within the network.

[0032] For the addition and deletion of nodes / edges, the influence range is determined according to the core node type. When node addition, node removal, edge addition and edge removal occur, not only the diffusion-type core nodes and the cohesive core nodes are affected, but also the ordinary nodes affected by the network changes. If there is a diffusion-type core node in the affected node, all nodes in the community to which the diffusion-type core node belongs are included in the unlabeled node set , and if there is a cohesive core node in the affected node, the neighborhood nodes of the cohesive core node are included in the set . This is because the influence range of the diffusion-type core node is wide, and the nodes in the community to which the diffusion-type core node belongs may change the community attribution due to the increment change; while the cohesive core node mainly affects the community relationship of its peripheral neighborhood nodes.

[0033] Incremental modularity formula Community merging is performed on nodes in the set until the modularity is stable, and the final community attribution is output, wherein is the edge proportion of the node, , is the node degree, is the increase or decrease state of the edge, is the number of edges in the network. Modularity is an important indicator for evaluating the quality of community division. The incremental modularity formula is used to obtain the change amount of the modularity of the nodes in the unlabeled set, and the community merging optimization is performed through the modularity maximization algorithm, so as to realize the dynamic update of the node community attribution relationship. The calculation efficiency is guaranteed, and the accuracy of community division is improved.

[0034] (2) Collect the user identity, device state and behavior data of the node, and obtain the corresponding independent trust value, and then obtain the node comprehensive trust value, including the following steps: Collect the identity attribute (such as account information, authentication method), device state (such as MAC address, vulnerability detection result) and behavior data (such as access frequency, operation type) of the node. These multi-dimensional data can comprehensively reflect the trust state of the node, and provide sufficient basis for subsequent trust evaluation.

[0035] Calculate the data weight based on the sliding window mechanism, and define the formula as follows:

[0036] Among them, is the window size, is the adjustment coefficient, is the data weight, which is used to highlight the influence of recent data. The sliding window mechanism can guarantee the reliability and timeliness of the calculation data, so that the trust evaluation can better reflect the recent state of the node.

[0037] Calculate the independent trust value through the logistic regression model, and the model is defined as:

[0038] Among them is the index influence factor, is the trust value of the measurement index, represents the independent trust value. The model can comprehensively calculate the trust values of multiple measurement indexes to obtain the independent trust values of users, devices and behaviors respectively.

[0039] Introduce a threat behavior set , modify the independent trust value of the node to obtain the node comprehensive trust value, which is represented as follows:

[0040] If a threat behavior is detected, the corresponding is set to 0, the introduction of the threat behavior factor can timely adjust the independent trust value of the node, when the node has a threat behavior, the node comprehensive trust value will be greatly reduced, which ensures the security of trust evaluation; Wherein, is the influence factor of each threat behavior, , is the weight of user identity, device state and behavior data, and the sum is 1, is the total trust value of the access subject, is the node comprehensive trust value.

[0041] (3) Dynamic permission control, including the following steps: Based on the node community affiliation, the community sensitivity parameter is obtained, and the permission threshold interval is initialized .

[0042] The sensitivity of different communities is different, and the initial permission threshold interval needs to be set according to the actual situation of the community to ensure the rationality of the permission control.

[0043] Cumulative identification After a certain number of non-dangerous accesses, the permission threshold is dynamically adjusted to obtain a dynamic permission threshold, which is represented as follows:

[0044] Wherein, is the access behavior associated parameter, is the interval dimension coefficient, is the threshold adjustment reference quantity, is the dynamic permission threshold, is the number of non-dangerous accesses, is the adjustment step parameter. The dynamic adjustment algorithm can optimize the permission threshold according to the distribution of the trust value of the access subject, so that the permission control is more suitable for the actual trust state of the node.

[0045] The node comprehensive trust value is compared with the adjusted dynamic permission threshold If , the access permission is granted, otherwise it is refused. Through the comparison of the node comprehensive trust value and the dynamic permission threshold, dynamic permission control based on the actual trust state of the node is realized, and the security of the network is improved.

[0046] For malicious nodes, in this embodiment, the target permission threshold is obtained through the dynamic permission threshold, and a punishment mechanism is adopted to limit the permission of malicious nodes, which is represented as follows:

[0047] wherein is a penalty coefficient, is an operation level, is a total number of levels, is a target permission threshold, is a modified target permission threshold. The setting of the penalty mechanism can effectively constrain malicious nodes, reduce their trust upper limit to limit permission acquisition and prevent them from obtaining improper permissions, and ensure stable operation of the network.

[0048] Embodiment 2: A trust management system based on a multi-core node dynamic community, as shown in Figure 2 , comprises a node acquisition module 100, a trust value calculation module 200, an access control module 300, and a trust management module 400. The node acquisition module 100 acquires the diffusion core node and the cohesive core node, and updates the node community attribution through the incremental module degree maximization algorithm. The trust value calculation module 200 collects the user identity, device state and behavior data of the node, and obtains the corresponding independent trust value, and then obtains the node comprehensive trust value. The access control module 300 generates a dynamic permission threshold based on the node community attribution, and performs zero-trust continuous evaluation through the node comprehensive trust value and the dynamic permission threshold, thereby realizing node access control. The trust management module 400 obtains a target permission threshold based on the dynamic permission threshold, and periodically verifies and adjusts the node community attribution and the trust state.

[0049] Various changes and modifications can be made without departing from the spirit and scope of the present application, and all equivalent technical solutions also belong to the scope of the present application.

[0050] Each embodiment in the specification is described in a progressive manner, and each embodiment focuses on the difference from other embodiments. The same or similar parts between the embodiments can be referred to each other.

[0051] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, an apparatus, or a computer program product. Therefore, the present application can be in the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can be in the form of a computer program product implemented on one or more computer usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer usable program code.

[0052] The present application is described in reference to the flowchart and / or block diagram of the method, terminal device (system), and computer program product according to the present application. It is understood that each flow and / or block in the flowchart and / or block diagram, and a combination of flows and / or blocks in the flowchart and / or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general purpose computer, a special purpose computer, an embedded processor, or other programmable data processing terminal devices to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing terminal devices generate an apparatus for implementing the functions specified in the flowchart and / or block diagram of the flow or flows and / or blocks. Figure 1 The flowchart and / or block diagram of the flow or flows and / or blocks Figure 1 The apparatus for implementing the functions specified in the flowchart and / or block diagram of the flow or flows and / or blocks.

[0053] These computer program instructions can also be stored in a computer readable memory capable of directing the computer or other programmable data processing terminal devices to work in a specific manner, so that the instructions stored in the computer readable memory produce a product including instruction apparatus, which implements the functions specified in the flowchart and / or block diagram of the flow or flows and / or blocks. Figure 1 The flowchart and / or block diagram of the flow or flows and / or blocks Figure 1 The apparatus for implementing the functions specified in the flowchart and / or block diagram of the flow or flows and / or blocks.

[0054] These computer program instructions can also be loaded into a computer or other programmable data processing terminal device, so that a series of operation steps are performed on the computer or other programmable terminal device to produce a computer implemented process, so that the instructions executed on the computer or other programmable terminal device provide steps for implementing the functions specified in the flowchart and / or block diagram of the flow or flows and / or blocks. Figure 1 The flowchart and / or block diagram of the flow or flows and / or blocks Figure 1 The apparatus for implementing the functions specified in the flowchart and / or block diagram of the flow or flows and / or blocks.

[0055] It should be noted that: The phrase "one embodiment" or "an embodiment" appearing in the specification does not necessarily refer to the same embodiment, and the phrase "in one embodiment" or "in an embodiment" appearing in the specification does not necessarily refer to the same embodiment.

[0056] In addition, it should be noted that the specific embodiments described in the specification, the shape of the parts, the name taken, etc. can be different. Any equivalent or simple change made in accordance with the structure, features and principles described in the patent concept of the present application is included in the protection scope of the present application. Those skilled in the art can make various modifications or supplements to the described specific embodiments or use similar ways to replace them, as long as they do not deviate from the structure of the present application or exceed the scope defined by the claims.

Claims

1. A trust management method based on a multi-core node dynamic community, characterized in that: The following steps are involved: Obtain diffuse core nodes and cohesive core nodes, and update the node community affiliation through the incremental modularity maximization algorithm; Collect the user identity, device status and behavior data of the node, and obtain the corresponding independent trust value, and then obtain the node's comprehensive trust value; Generate dynamic permission thresholds based on node community affiliation. Conduct zero-trust continuous assessments based on node comprehensive trust values ​​and dynamic permission thresholds to implement node access control. The target permission threshold is obtained based on the dynamic permission threshold, and the node community affiliation and trust status are periodically verified and adjusted.

2. The trust management method based on a multi-core node dynamic community according to claim 1, characterized in that: The method of obtaining diffusion-type core nodes and cohesive core nodes and updating node community affiliation by using an incremental modularity maximization algorithm includes the following steps: The degree discount heuristic method is used to calculate the node degree discount value, and the nodes with the top 10% of the node degree discount value are selected as diffusion core nodes; Through the 2-core subgraph decomposition method, the node with the largest degree in the subgraph is iteratively deleted and the subgraph topology is dynamically updated until the subgraph is empty. The deleted nodes form a cohesive core node. When an incremental topological change occurs, the affected nodes are identified. If there is a diffuse core node among the affected nodes, all nodes in its community are included in the unlabeled node set. If there is a cohesive core node among the affected nodes, its neighboring nodes are included in the set. Incremental topological changes include node addition, node removal, edge addition, and edge removal. Obtain the change in modularity of the nodes in the unlabeled set, and perform community merging optimization through the modularity maximization algorithm to achieve dynamic updating of the node community affiliation relationship. The change in modularity is expressed as follows: in, is the change in modularity, is the number of edges in the network, is the node-edge ratio, 、 is the node degree, The increase or decrease status of the edge.

3. The trust management method based on a multi-core node dynamic community according to claim 2, characterized in that: The method of using the degree discount heuristic method to calculate the node degree discount value and selecting the nodes with the top 10% of the node degree discount value as the diffusion core nodes includes the following steps: Get node degree , the number of activated seeds in neighboring nodes and activation probability parameters ; The node degree discount value is calculated based on the node degree, the number of activated seeds in the neighboring nodes, and the activation probability parameter, which is expressed as follows: Sort all nodes by degree discount value in descending order, and select the top 10% of nodes as diffusion core nodes; in, Indicates the The node degree of each node, Indicates the The number of activated seeds in the neighboring nodes of a node, represents the activation probability parameter, Indicates the The node degree discount value of each node.

4. The trust management method based on a multi-core node dynamic community according to claim 1, characterized in that: The collecting of user identity, device status and behavior data of the node and obtaining the corresponding independent trust value, and then obtaining the node comprehensive trust value, includes the following steps: Collect node user identity, device status, and behavior data. User identity includes account information and authentication method; device status includes MAC address and vulnerability detection results; and behavior data includes access frequency and operation type. Based on the sliding window mechanism, the historical data of the node is processed and different weights are assigned to different data, as shown below: The independent trust values ​​corresponding to user identity, device status, and behavior data are calculated using a logistic regression model, as shown below: By modifying the threat behavior factor, the node comprehensive trust value is obtained, which is expressed as follows: in, is the window size, is an adjustment coefficient used to highlight the impact of recent data on trust assessment. is the indicator impact factor, is the trust value of the measurement indicator, is the data weight, is a set of threat behaviors, is the influencing factor of each threat behavior, , is the weight of user identity, device status and behavior data, the sum of which is 1. is the total trust value of the access subject, is the comprehensive trust value of the node.

5. The trust management method based on a multi-core node dynamic community according to claim 1, characterized in that: The method of generating a dynamic permission threshold based on the node community affiliation and performing a zero-trust continuous assessment based on the node comprehensive trust value and the dynamic permission threshold to implement node access control includes the following steps: Initialize the permission threshold interval based on the sensitivity of the node community ; According to the cumulative number of accesses and the permission threshold range, the permission threshold is dynamically adjusted to obtain the dynamic permission threshold, which is expressed as follows: If the node's comprehensive trust value is greater than the adjusted permission threshold, the corresponding access permission is granted, otherwise it is denied; in, To access the behavior associated parameters, is the interval dimension coefficient, is the threshold adjustment reference amount, To adjust the step size parameter, is the dynamic permission threshold, The number of non-risk visits.

6. The trust management method based on a multi-core node dynamic community according to claim 1, characterized in that: Periodic verification and adjustment of node community ownership and trust status includes the following steps: Periodically check whether the node community affiliation matches the network structure and verify whether the node's comprehensive trust value conforms to the behavioral characteristics; Based on the verification results, the node community affiliation and trust status are adjusted.

7. The trust management method based on a multi-core node dynamic community according to claim 6, characterized in that: Also includes: Based on the verification results, a penalty mechanism is used to modify the target authority threshold of malicious nodes, and the modified target authority threshold is obtained as follows: Based on the revised target permission threshold and community structure, the trust assessment model and permission control strategy are iteratively optimized to limit the permission acquisition of malicious nodes through this mechanism. in, is the penalty coefficient, For the operation level, is the total number of levels, is the target permission threshold, is the revised target permission threshold.

8. A trust management system based on a multi-core node dynamic community, characterized in that: It includes node acquisition module, trust value calculation module, access control module and trust management module; The node acquisition module acquires diffusion core nodes and cohesive core nodes, and updates the node community affiliation through an incremental modularity maximization algorithm; The trust value calculation module collects the user identity, device status and behavior data of the node and obtains the corresponding independent trust value, thereby obtaining the node's comprehensive trust value; The access control module generates a dynamic permission threshold based on the node community affiliation, and performs zero-trust continuous evaluation based on the node comprehensive trust value and the dynamic permission threshold to implement node access control; The trust management module obtains a target authority threshold based on a dynamic authority threshold, and periodically verifies and adjusts the node community affiliation and trust status.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

10. A trust management device based on a multi-core node dynamic community, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.

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