Multistage communication link adaptive encryption method

Abstract

The invention relates to the technical field of data encryption, in particular to a multistage communication link self-adaptive encryption method, which comprises multistage link grading, watermark embedding, data encryption, grading decryption and data recombination.According to the method, links are divided according to the sensitivity of the data, the security of the data is improved, and the security of the data is improved. Parameters of the AES encryption algorithm are optimized by using the PPE-P algorithm, so that the encryption performance is optimized; through a row-column confusion matrix and a closed-loop bidirectional diffusion strategy, the data complexity is increased, and the anti-attack capability is improved; chaotic mapping parameters are set according to the link security level to generate a multi-level chaotic key sequence, so that the encryption flexibility and security are enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of data encryption, and specifically refers to a multi-level communication link adaptive encryption method. Background Art

[0002] With the rapid development of information technology and the explosive growth of data volume, the problem of data security has become increasingly prominent. Existing encryption methods have problems of insufficient security and low efficiency. Sensitive data is more vulnerable to attacks and leakage risks. Due to the lack of flexible encryption strategies, existing methods may cause performance bottlenecks when dealing with large-capacity data, resulting in slower encryption and decryption processes, affecting the overall efficiency of the system and user experience; general decryption methods have problems of weak anti-attack ability and low flexibility, being vulnerable when facing advanced persistent threats and complex attacks, easy to be cracked, and the encryption process cannot efficiently utilize the characteristics of data, reducing the encryption performance. Summary of the Invention

[0003] In view of the above situation, to overcome the defects of the existing technology, the present invention provides a multi-level communication link adaptive encryption method. Aiming at the problems of insufficient security and low efficiency of existing encryption methods, the present invention divides the link into high security level, medium security level and low security level according to the sensitivity of data, and takes different protection measures for data at different levels, improving the security of data; by using the PPE-P (Phasmatodea Population Evolution-Pro) algorithm to optimize the parameters of the AES encryption algorithm, the encryption performance is optimized, the computational complexity is reduced, and at the same time the encryption strength is maintained, improving the overall efficiency of the system; aiming at the problem of weak anti-attack ability of general decryption methods, through the row-column confusion matrix and the closed-loop bidirectional diffusion strategy, the complexity of data is increased, the anti-attack ability is improved, and the risk of data being intercepted or cracked during transmission is reduced; aiming at the problem of low flexibility of general decryption methods, the present invention sets chaos mapping parameters according to the link security level to generate multi-level chaos key sequences, enhancing the flexibility and security of encryption, using different keys for data at different security levels, and reducing the risk of the key being cracked.

[0004] The technical solution adopted by the present invention is as follows: A multi-level communication link adaptive encryption method provided by the present invention includes the following steps: Step S1: Multi-level link classification; Step S2: Watermark embedding; Step S3: Data encryption; Step S4: Hierarchical decryption; Step S5: Data recombination.

[0005] Further, step S1 specifically includes the following contents: The link is divided into high-security-level links, medium-security-level links, and low-security-level links. High-security-level links contain sensitive data, medium-security-level links contain moderately sensitive data, and low-security-level links contain ordinary data and non-sensitive information.

[0006] Further, in step S2, watermark embedding specifically includes the following content: Step S21: Watermark preprocessing. Different watermark contents are set for the three security-level links and encrypted using a chaotic key. Step S22: Feature extraction. The Canny operator is used to extract the edge features of the transmitted data and perform noise reduction processing. Step S23: Hierarchical encryption embedding. The complexity of watermark embedding is adjusted according to the security level of the link. Step S24: Inverse frequency-domain transformation. The information embedded with the watermark is converted back into a spatial-domain image. Step S25: Set permissions. Permissions and key levels are set for users, and different decryption schemes are provided. Step S26: Watermark concealment. The transmitted data embedded with the watermark is obtained.

[0007] Further, step S3 specifically includes the following steps: Step S31: Row-column confusion. The transmitted data embedded with the watermark is subjected to row-column confusion. A row-column confusion matrix is generated according to the chaotic key, and the rows and columns of the transmitted data are rearranged according to the row-column confusion matrix to obtain the confused transmitted data. Step S32: Closed-loop bidirectional diffusion. The confused transmitted data is diffused using the chaotic key to obtain the diffused transmitted data. Step S33: Data encryption execution. The confused transmitted data and the diffused transmitted data are combined and encrypted using the AES encryption algorithm to obtain the encrypted data. Step S34: Verify security. An attack simulation is performed on the encrypted data to obtain the attack test results, and the data encryption process is optimized using the PPE-P algorithm according to the attack test results.

[0008] Further, in step S32, when using the chaotic key to diffuse the confused transmitted data, it specifically includes the following steps: Step S321: Dynamic key hierarchical generation. A multi-level key generation mechanism is introduced. The chaotic mapping parameters are set according to the link security level to generate a multi-level chaotic key sequence. The length of the key sequence corresponds to the transmitted data, and different levels of key sequences are used to control the diffusion stage. Step S322: Edge Adaptive Diffusion. Design an adaptive diffusion strategy according to the edge features of the transmitted data, divide the transmitted data into a high-frequency region and a low-frequency region, apply a high diffusion intensity to the high-frequency region, and a low diffusion intensity to the low-frequency region; Step S323: Multi-directional Bidirectional Diffusion. The multi-directional diffusion includes diagonals, anti-diagonals, and connections at arbitrary angles, forming a multi-dimensional closed-loop diffusion structure. Introduce a differentiated chaotic key in each diffusion direction to form a diffusion path; Step S324: Incorporate Random Perturbation. Introduce a random perturbation factor, use a pseudo-random number generator to randomly perturb the chaotic key sequence during the diffusion operation; Step S325: Interactive Embedded Diffusion. Introduce an interactive embedding mechanism between the chaotic key sequence and the scrambled transmitted data during the diffusion process to adjust the key sequence; Step S326: Multi-layer Iterative Closed-loop. Introduce a multi-layer closed-loop structure to perform closed-loop encryption on the diffused data again. Each layer of closed-loop processing is based on a different chaotic key sequence; Step S327: Feature Verification. After the diffusion is completed, the diffused transmitted data is obtained. Use a feature extraction algorithm to perform integrity verification on the diffused transmitted data, generate a feature hash value of the diffused data, and embed it into the diffused data.

[0009] Further, in step S34, optimize the data encryption process using the PPE-P algorithm according to the attack test results, which specifically includes the following steps: Step S341: Initialize the matrix. Initialize a matrix with a dimension of , where all elements are initialized to 0. The matrix is as follows: ; Randomly generate a vector and replace the first row of matrix ; Step S342: Generate subsequent vectors using chaotic mapping. Traverse the second row to the th row of matrix , generate vectors using chaotic mapping, and fill them into matrix ; Step S343: Map to the interval. For each element in the filled matrix , map it to the interval . The mapping formula is as follows: ; where, represents any element in the matrix, is the row, is a column; Step S344: Initialize attributes, and the evolution trend is set to 0; Step S345: Initialize the population. In steps S341 to S344, obtain the initial population, the size of the initial population is , and the dimension of each individual in the initial population is ; Step S346: Calculate the fitness value. Calculate the fitness value according to each individual in the initial population, store all the calculated fitness values of the individuals in a fitness value vector, take the maximum value in the fitness value vector as the current global optimal solution, and set a historical solution set , and store the current global optimal solution in the historical solution set ; Step S347: Iteration. Use the particle swarm optimization strategy to update the initial population, and set the maximum number of iterations , the current number of iterations and the convergence threshold , enter the iteration process, and obtain the final global optimal solution and its fitness value after the iteration is completed; Step S348: Optimize the data encryption process, and use the final global optimal solution as the parameter for optimizing the AES encryption algorithm.

[0010] Furthermore, step S347 specifically includes the following steps: Step S3471: Update the population. Use the particle swarm optimization strategy to update the initial population to obtain a new generation of population; Step S3472: Recalculate the fitness value. Calculate and record the fitness value of each individual in the new generation of population, perform the operations in step S346, and update the global optimal solution and the historical solution set ; Step S3473: Attribute update. Increase the evolution trend , and the formula used is as follows: ; where, is the increasing trend factor; Step S3474: Population competition. Set the distance threshold , randomly select two individuals and , when the straight-line distance between and is less than the distance threshold , it is determined that and belong to a competitive relationship, then reduce the evolution trend ​, the formulas used are as follows: ; Among them, is the downward trend factor; Step S3475: Update the current iteration count, and update the current iteration count to ; Step S3476: Termination judgment, iterate from step S3471 to step S3475, and judge whether the current iteration count reaches the maximum iteration count , and check whether the change in the current fitness value is less than the convergence threshold . If the above two conditions are met, output the final global optimal solution and its fitness value. If not, continue the iteration.

[0011] Furthermore, step S4 specifically includes the following content: Set the decryption condition and select the AES encryption algorithm as the decryption algorithm, and perform step-by-step decryption according to the embedded watermark to output the decrypted data;

[0012] Furthermore, step S5 specifically includes the following content: Set the restructured data structure and format according to the decrypted data, formulate a restructuring strategy according to the restructured data structure, perform data restructuring, and output the restructuring result.

[0013] The beneficial effects achieved by the present invention using the above solution are as follows:

[0014] (1) Aiming at the problems of insufficient security and low efficiency in existing encryption methods, the present invention divides the link into high security level, medium security level, and low security level according to the sensitivity of the data, and takes different protection measures for data at different levels to improve the security of the data; by using the PPE-P algorithm to optimize the parameters of the AES encryption algorithm, the encryption performance is optimized, the computational complexity is reduced, and the encryption strength is maintained while improving the overall efficiency of the system;

[0015] (2) Aiming at the problem of weak anti-attack ability in general decryption methods, through the row-column confusion matrix and the closed-loop bidirectional diffusion strategy, the complexity of the data is increased, the anti-attack ability is improved, and the risk of the data being intercepted or cracked during transmission is reduced;

[0016] (3) Aiming at the problem of low flexibility in general decryption methods, the present invention sets chaotic mapping parameters according to the link security level to generate a multi-level chaotic key sequence, enhancing the flexibility and security of encryption. Different security levels of data use different keys, reducing the risk of the key being cracked. Description of the Drawings

[0017] Figure 1 Schematic flow diagram of a multi - level communication link adaptive encryption method provided by the present invention;

[0018] Figure 2 Schematic flow diagram of the specific steps of step S32 provided by the present invention.

[0019] The accompanying drawings are used to provide a further understanding of the present invention, and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention, and do not constitute a limitation to the present invention. Detailed implementation manners

[0020] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0021] In the description of the present invention, it should be understood that the terms "upper", "lower", "front", "rear", "left", "right", "top", "bottom", "inner", "outer", etc. indicating the orientation or positional relationship are based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as a limitation to the present invention.

[0022] Embodiment 1, refer to Figure 1 , a multi - level communication link adaptive encryption method provided by the present invention, the method includes the following steps: Step S1: Multi - level link classification; Step S2: Watermark embedding; Step S3: Data encryption; Step S4: Hierarchical decryption; Step S5: Data recombination; Step S1 specifically includes the following content: The link is divided into a high - security - level link, a medium - security - level link, and a low - security - level link. The high - security - level link contains sensitive data, the medium - security - level link contains data with medium sensitivity, and the low - security - level link contains ordinary data and non - sensitive information.

[0023] In step S2, the watermark embedding specifically includes the following content: Step S21: Watermark pre - processing, setting different watermark contents for the three security - level links and encrypting them using a chaotic key; Step S22: Feature extraction. Use the Canny operator to extract the edge features of the transmitted data and perform noise reduction processing; Step S23: Hierarchical encryption embedding. Adjust the complexity of watermark embedding according to the security level of the link; Step S24: Inverse frequency domain transformation. Convert the information embedded with the watermark back into a spatial domain image; Step S25: Set permissions. Set permissions and key levels for users and provide different decryption schemes; Step S26: Watermark concealment. Obtain the transmitted data with the embedded watermark.

[0024] In this embodiment, for data with three security levels, the following watermark contents are set respectively: High security level link: The embedded watermark content is "Sensitive Medical Data", and the watermark is encrypted using a chaotic sequence generated by the Logistic map; Medium security level link: The embedded watermark content is "Personal Information", and it is encrypted using a simple chaotic key; Low security level link: The embedded watermark content is "Public Data", which is directly embedded without encryption.

[0025] In this embodiment, according to the security level of the data, the complexity of watermark embedding is adjusted: High security level link: The watermark embedding uses the DCT algorithm to ensure that the watermark is difficult to remove.

[0026] Medium security level link: Use the LSB embedding method to embed through the low-frequency region.

[0027] Low security level link: Directly embed the watermark in the data with relatively low difficulty.

[0028] Embodiment 2. This embodiment is based on the above embodiment. Step S3 specifically includes the following steps: Step S31: Row-column confusion. Perform row-column confusion on the transmitted data with the embedded watermark. Generate a row-column confusion matrix according to the chaotic key, and rearrange the rows and columns of the transmitted data according to the row-column confusion matrix to obtain the confused transmitted data; Step S32: Closed-loop bidirectional diffusion. Use the chaotic key to diffuse the confused transmitted data to obtain the diffused transmitted data; Step S33: Data encryption execution. Combine the confused transmitted data and the diffused transmitted data and encrypt them using the AES encryption algorithm to obtain the encrypted data; Step S34: Verify security, perform attack simulation on the encrypted data to obtain attack test results, and optimize the data encryption process using the PPE-P algorithm according to the attack test results.

[0029] In this embodiment, assume that we have a grayscale image data with pixel values as a two-dimensional matrix, and the operations are as follows: Original data = [100, 150, 200], [50, 75, 125], [10, 20, 30] This embodiment uses the Logistic map to generate a chaotic key and obtains the following random number sequence as the row and column confusion matrix: Confusion matrix = [2, 0, 1], / / representing the rearrangement of rows [1, 0, 2] / / representing the rearrangement of columns Rearrange the original data according to the confusion matrix: Rearrange the rows and select the 2nd, 0th, and 1st rows; Rearrange the columns and select the 1st, 0th, and 2nd columns; Confused data = [75, 50, 150], [20, 10, 100], [30, 0, 200] Continue to perform diffusion processing on the confused data using the same chaotic key, and use a simple bitwise XOR operation: Diffused data = [75 ⊕ k1, 50 ⊕ k2, 150 ⊕ k3], [20 ⊕ k4, 10 ⊕ k5, 100 ⊕ k6], [30 ⊕ k7, 0 ⊕ k8, 200 ⊕ k9] Where k1 to k9 are the values generated by the chaotic key; Combine the confused data and the diffused data to form a new data set, and use the AES encryption algorithm to encrypt the new data set to obtain the encrypted data; Test the encrypted data using differential attack and plaintext attack methods.

[0030] Example three, refer to Figure 2 , this example is based on the above example. In step S32, use the chaotic key to perform diffusion on the confused transmission data, which specifically includes the following steps: ​​​​Step S321: Dynamic key hierarchy generation. Introduce a multi-level key generation mechanism, set the chaos mapping parameters according to the link security level, generate a multi-level chaotic key sequence, where the length of the key sequence corresponds to the transmitted data, and use key sequences at different levels to control the diffusion stage; Step S322: Edge adaptive diffusion. Design an adaptive diffusion strategy based on the edge features of the transmitted data, divide the transmitted data into a high-frequency region and a low-frequency region, apply a high diffusion intensity to the high-frequency region, and a low diffusion intensity to the low-frequency region; Step S323: Multi-directional two-way diffusion. The multi-directional diffusion includes diagonals, anti-diagonals, and connections at arbitrary angles, forming a multi-dimensional closed-loop diffusion structure. Introduce differentiated chaotic keys in each diffusion direction to form a diffusion path; Step S324: Incorporate random perturbation. Introduce a random perturbation factor, use a pseudo-random number generator to randomly perturb the chaotic key sequence during the diffusion operation; Step S325: Interactive embedding diffusion. Introduce an interactive embedding mechanism between the chaotic key sequence and the scrambled transmitted data during the diffusion process to adjust the key sequence; Step S326: Multi-layer iterative closed-loop. Introduce a multi-level closed-loop structure to perform closed-loop encryption on the diffused data again. Each layer of closed-loop processing is based on a different chaotic key sequence; Step S327: Feature verification. After the diffusion is completed, the diffused transmitted data is obtained. Use a feature extraction algorithm to perform integrity verification on the diffused transmitted data, generate a feature hash value of the diffused data, and embed it into the diffused data.

[0031] In this embodiment, in step S322, use the Canny operator to extract the edge features of the scrambled image, determine the high-frequency region and the low-frequency region, where the high-frequency region = pixel value > 128; The low-frequency region = pixel value <= 128; Diffusion intensity setting: The diffusion coefficient for the high-frequency region is 2, and the diffusion coefficient for the low-frequency region is 0.5; High-frequency region: The diffusion intensity is set to high (for example, the coefficient is 2) Low-frequency region: The diffusion intensity is set to low (for example, the coefficient is 0.5); In step S326, perform multi-level closed-loop structure processing on the diffused data: The data of the first layer = perform AES encryption (diffused data, k1) The data of the second layer = perform AES encryption (the data of the first layer, k2) ... (Perform encryption layer by layer in this way) Each layer of closed-loop processing is based on a different chaotic key sequence.

[0032] Example 4. This example is based on the above example. In step S34, according to the attack test results, the PPE-P algorithm is used to optimize the data encryption process, which specifically includes the following steps: Step S341: Initialize the matrix. Initialize a matrix with a dimension of , where all elements are initialized to 0. The matrix is as follows: ; Randomly generate a vector and replace the first row of the matrix ; Step S342: Generate subsequent vectors using chaotic mapping. Traverse the second row to the th row of the matrix , generate vectors using chaotic mapping, and fill them into the matrix ; Step S343: Map to the interval. For each element in the filled matrix , map it to the interval . The mapping formula is as follows: ; Among them, represents any element in the matrix, is the row, is the column; Step S344: Initialize the attributes. The evolution trend is set to 0; Step S345: Initialize the population. In steps S341 to S344, an initial population is obtained. The size of the initial population is , and the dimension of each individual in the initial population is ; Step S346: Calculate the fitness value. Calculate the fitness value according to each individual in the initial population, store all the calculated fitness values of the individuals in a fitness value vector, take the maximum value in the fitness value vector as the current global optimal solution, and set a historical solution set , and store the current global optimal solution in the historical solution set ; Step S347: Iteration. Use the particle swarm optimization strategy to update the initial population. Set the maximum number of iterations , the current iteration number and the convergence threshold , enter the iteration process, and obtain the final global optimal solution and its fitness value after the iteration is completed; Step S348: Optimize the data encryption process, and use the final global optimal solution as the parameter for optimizing the AES encryption algorithm.

[0033] In this embodiment, is 5, is 3, and the randomly generated vector is [0.2, 0.5, 0.8], replacing the first row of matrix ; Traverse the second row to the fifth row of matrix and generate vectors of using the chaotic mapping: The second row generates: [0.4, 0.3, 0.6] The third row generates: [0.1, 0.7, 0.2] The fourth row generates: [0.5, 0.9, 0.3] The fifth row generates: [0.3, 0.4, 0.5] Fill matrix ; Set the mapping interval as (1, 2); Perform mapping on each element in the filled matrix : ; After completing the mapping, matrix remains unchanged, and all values are already in the interval [0, 1].

[0034] Embodiment 5. This embodiment is based on the above embodiment, step S347, and specifically includes the following steps: Step S3471: Update the population, and use the particle swarm optimization strategy to update the initial population to obtain a new generation of population; Step S3472: Recalculate the fitness value, calculate and record the fitness value of each individual in the new generation of population, execute the operation of step S346, and update the global optimal solution and the historical solution set ; Step S3473: Attribute update, add the evolution trend , and the formula used is as follows: ; Among them, is the increasing trend factor; Step S3474: Population competition, set the distance threshold , randomly select two individuals and , when and The straight-line distance between them is less than the distance threshold When it is determined that and belong to a competitive relationship, then the evolution trend is reduced , and the formula used is as follows: ; Among them, is the downward trend factor; Step S3475: Update the current iteration number, and update the current iteration number to ; Step S3476: Termination judgment, iterate steps S3471 to S3475, and judge whether the current iteration number reaches the maximum iteration number , and check whether the change in the current fitness value is less than the convergence threshold . If the above two conditions are met, output the final global optimal solution and its fitness value. If not, continue to iterate.

[0035] Example 6, this example is based on the above example, and step S4 specifically includes the following contents: Set the decryption condition and select the AES encryption algorithm as the decryption algorithm, and perform decryption level by level according to the embedded watermark, and output the decrypted data; Step S5 specifically includes the following contents: Set the data structure and format after recombination according to the decrypted data, formulate a recombination strategy according to the recombined data structure, perform data recombination, and output the recombination result.

[0036] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device.

[0037] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

[0038] The above description of the present invention and its embodiments is not restrictive. What is shown in the drawings is only one of the embodiments of the present invention, and the actual structure is not limited thereto. In general, if those of ordinary skill in the art are inspired by it and, without departing from the spirit of the present invention, design similar structural forms and embodiments to this technical solution without creative efforts, they shall fall within the protection scope of the present invention.

Claims

1. A multi-level communication link adaptive encryption method for ultra-large capacity nodes, characterized in that: The method includes the following steps: Step S1: Multi-level link classification; Step S2: Watermark embedding; Step S3: Data encryption; Step S4: Hierarchical decryption; Step S5: Data recombination.

2. The multi-level communication link adaptive encryption method for an ultra-large capacity node according to claim 1, characterized in that: Step S1 specifically includes the following: The link is divided into a high-security-level link, a medium-security-level link, and a low-security-level link. The high-security-level link contains sensitive data, the medium-security-level link contains data with medium sensitivity, and the low-security-level link contains ordinary data and non-sensitive information; Step S2, watermark embedding, specifically includes the following: Step S21: Watermark preprocessing. Different watermark contents are set for the three security-level links and encrypted using a chaotic key; Step S22: Feature extraction. The Canny operator is used to extract the edge features of the transmitted data and perform noise reduction processing; Step S23: Hierarchical encryption embedding. The complexity of watermark embedding is adjusted according to the security level of the link; Step S24: Inverse frequency-domain transformation. The information with the embedded watermark is converted back into a spatial-domain image; Step S25: Set permissions. Permissions and key levels are set for users, and different decryption schemes are provided; Step S26: Watermark concealment. The transmitted data with the embedded watermark is obtained.

3. A multi - level communication link adaptive encryption method for an ultra - large - capacity node according to claim 1, characterized in that: Step S3 specifically includes the following steps: Step S31: Row-column confusion. The transmitted data with the embedded watermark is subjected to row-column confusion. A row-column confusion matrix is generated according to the chaotic key, and the rows and columns of the transmitted data are rearranged according to the row-column confusion matrix to obtain the confused transmitted data; Step S32: Closed-loop bidirectional diffusion. The confused transmitted data is diffused using the chaotic key to obtain the diffused transmitted data; Step S33: Data encryption execution. The confused transmitted data and the diffused transmitted data are combined and encrypted using the AES encryption algorithm to obtain the encrypted data; Step S34: Verify security. An attack simulation is performed on the encrypted data to obtain an attack test result, and the PPE-P algorithm is used to optimize the data encryption process according to the attack test result.

4. A multi-level communication link adaptive encryption method for an ultra-large capacity node according to claim 3, characterized in that: In Step S32, when using the chaotic key to diffuse the confused transmitted data, it specifically includes the following steps: Step S321: Dynamic key hierarchy generation. A multi-level key generation mechanism is introduced. The chaotic mapping parameters are set according to the link security level to generate a multi-level chaotic key sequence. The length of the key sequence corresponds to the transmitted data, and different levels of key sequences are used to control the diffusion stage; Step S322: Edge-adaptive diffusion. An adaptive diffusion strategy is designed according to the edge features of the transmitted data. The transmitted data is divided into a high-frequency region and a low-frequency region. A high diffusion intensity is applied to the high-frequency region, and a low diffusion intensity is applied to the low-frequency region; Step S323: Multi-directional bidirectional diffusion. The multi-directional diffusion includes diagonals, anti-diagonals, and connections at any angle, forming a multi-dimensional closed-loop diffusion structure. Differentiated chaotic keys are introduced in each diffusion direction to form a diffusion path; Step S324: Fusion of random perturbations. A random perturbation factor is introduced, and a pseudo-random number generator is used to randomly perturb the chaotic key sequence during the diffusion operation; Step S325: Interactive Embedded Diffusion. During the diffusion process, an interactive embedding mechanism of the chaotic key sequence and the scrambled transmission data is introduced to adjust the key sequence; Step S326: Multi-layer Iterative Closed-loop. A multi-level closed-loop structure is introduced to perform closed-loop encryption on the diffused data again. Each layer of closed-loop processing is based on a different chaotic key sequence; Step S327: Feature Verification. After the diffusion is completed, the diffused transmission data is obtained. The integrity of the diffused transmission data is verified through a feature extraction algorithm, and the feature hash value of the diffused data is generated and embedded into the diffused data.

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