An encryption management system and method based on communication security

Abstract

The present invention discloses an encryption management system and method based on communication security, and relates to the field of communication security technology. The method comprises the following steps: collecting agricultural record data in historical transmission, collecting agricultural record data that needs to be transmitted currently, and establishing an agricultural transmission database; obtaining key factors that affect the information entropy of transmission data through statistical analysis results; training an information entropy calculation model according to key factors; collecting key factor data values ​​in current agricultural data in real time, and inputting the collected data into the information entropy calculation model to calculate the information entropy of transmission data; calculating the encryption key length according to the transmission data; calculating the theoretical key length, selecting the key length according to the information entropy, and encrypting and transmitting the data to be transmitted; and recording and updating database information. The present invention reasonably selects the key length, avoiding the waste of computing resources and the burden on users caused by the complexity of encryption.

Description

Technical Field

[0001] The present invention relates to the technical field of communication security, and in particular to an encryption management system and method based on communication security. Background Art

[0002] Communication Security (COMSEC) refers to the use of various technical and management methods to ensure the confidentiality, integrity, availability and non-repudiation of information during the communication process. The goal of communication security is to prevent unauthorized access, tampering, theft or destruction of information.

[0003] Encryption management refers to the management of data encryption and decryption processes and the generation, distribution, storage, use and destruction of related keys. Its purpose is to protect the confidentiality, integrity and authenticity of data and ensure that data is protected from unauthorized access, tampering or loss during transmission and storage.

[0004] With the development of technologies such as the Internet, cloud computing, and the Internet of Things (IoT), the amount of data is growing exponentially, and information security is facing increasingly complex challenges. Encryption technology has become a core means of protecting sensitive information (such as financial transaction data, personal privacy data, and corporate confidential data) from potential threats. Encryption management has therefore become a key area in information security.

[0005] Although encryption management has made significant progress in data protection, key management is difficult in practical applications. In large-scale distributed systems, generating, storing, distributing and destroying encryption keys is a very challenging task. Key lifecycle management involves many steps (such as backup, recovery, revocation, update, etc.), which are prone to human errors or exploitation by attackers. Computing resources are consumed a lot, and powerful encryption algorithms (such as RSA-2048, AES-256) have high demands on computing resources, especially in scenarios that process large amounts of data or require real-time response (such as IoT devices, real-time video transmission), which will significantly affect system performance and user experience. Encryption complexity increases user burden. Excessive encryption and security measures may lead to a decline in user experience and even affect the normal operation of the business. Summary of the invention

[0006] The purpose of the present invention is to provide an encryption management system and method based on communication security to solve the problems raised in the prior art.

[0007] To achieve the above object, the present invention provides the following technical solution: an encryption management method based on communication security, the encryption management method comprising the following steps:

[0008] Step S1, collecting agricultural record data in historical transmission, collecting agricultural record data that needs to be transmitted currently, establishing an agricultural transmission database, and updating the database in stages;

[0009] Step S2, statistically analyzing the agricultural record data, and obtaining key factors affecting the information entropy of the transmitted data through the statistical analysis results;

[0010] Step S3, using key factor data to train the information entropy calculation model;

[0011] Step S4, collecting the key factor data values ​​in the current agricultural record data in real time, and inputting the collected data into the information entropy calculation model to calculate the information entropy of the transmission data;

[0012] Step S5, dynamically adjusting the encryption key length of the current transmission data according to the information entropy of the transmission data;

[0013] Step S6: define a multi-objective function, calculate the theoretical key length, select the key length according to the information entropy, encrypt and transmit the data to be transmitted;

[0014] Step S7, backing up the agricultural record data collected in real time and the calculation process data of the key dynamic adjustment, and inputting them into the agricultural transmission database to update the database information;

[0015] The agricultural transmission database includes agricultural record data and key dynamic adjustment data.

[0016] The key dynamic adjustment data includes all calculated data in the process of predicting the key length;

[0017] In step S1, the agricultural record data includes communication content data, data distribution characteristics, security threat data and encryption algorithm performance data;

[0018] The communication content data includes text data {T1, T2, ..., Tn}, where T1, T2, ..., Tn represent the 1st, 2nd, ..., nth occurrence of text data samples, image data {I1, I2, ..., In}, where I1, I2, ..., In represent the 1st, 2nd, ..., nth types of image data samples, and audio data {A1, A2, ..., An}, where A1, A2, ..., An represent the 1st, 2nd, ..., nth sampled audio data, respectively; these data are samples of real-time communication traffic or historical communication records.

[0019] The data distribution features include character frequency distribution {f(c1), f(c2), ..., f(ck)}, where f(c1), f(c2), ..., f(ck) represent the character frequency distribution, byte distribution and packet length distribution of the 1st, 2nd, ..., kth time windows respectively;

[0020] The security threat data includes known attack patterns, threat models and attack success rate information of different encryption algorithms;

[0021] The encryption algorithm performance data is the computing performance data of different encryption algorithms under different key lengths, including encryption / decryption time, CPU / GPU consumption and memory usage.

[0022] The representation matrix of the encryption / decryption time is:

[0023] Wherein, L_K1, L_K2, ..., L_Km represent m keys of different lengths respectively, K represents the type of encryption algorithm; t_enc(L_K1), t_enc(L_K2), ..., t_enc(L_Km) represent the encryption time corresponding to the key length L_K1, L_K2, ..., L_Km respectively; t_dec(L_K1), t_dec(L_K2), ..., t_dec(L_Km) represent the decryption time corresponding to the key length L_K1, L_K2, ..., L_Km respectively;

[0024] The representation matrix of the CPU / GPU consumption is:

[0025] Among them, K_1, K_2, ..., K_r represent r different encryption algorithms respectively; C_cpu(K_1), C_cpu(K_2), ..., C_cpu(K_r) represent the CPU occupancy rates of the corresponding encryption algorithms K_1, K_2, ..., K_r respectively; C_gpu(K_1), C_gpu(K_2), ..., C_gpu(K_r) represent the GPU occupancy rates of the corresponding encryption algorithms K_1, K_2, ..., K_r respectively.

[0026] In step S2, the agricultural record data is statistically analyzed, and the specific steps of obtaining the key factors affecting the information entropy of the transmission data through the statistical analysis results are:

[0027] Step S2-1, extracting characteristic factors affecting the information entropy of transmission data {[te1_1, te1_2, ..., te1_i], [te2_1, te2_2, ..., te2_i], ..., [tez_1, tez_2, ..., tez_i]} through the agricultural transmission database, wherein te1, te2, ..., tez respectively represent the 1st, 2nd, ..., zth characteristic factors affecting the information entropy of transmission data; te1_1, te1_2, ..., te1_i respectively represent the i sample values ​​of the characteristic factor te1; te2_1, te2_2, ..., te2_i respectively represent the i sample values ​​of the characteristic factor te2; tez_1, tez_2, ..., tez_i respectively represent the i sample values ​​of the characteristic factor tez;

[0028] The characteristic factors that affect the information entropy of transmitted data include data file size, data information type and quantity, probability distribution of data source, complexity and change rate of data content, data sampling rate and resolution, etc.

[0029] Step S2-2, construct a coordinate pair with the sample value of each characteristic factor as the horizontal coordinate and the size of the information entropy of the transmitted data as the vertical coordinate, plot points in a plane rectangular coordinate system, and connect them with a smooth curve to form a characteristic information entropy curve;

[0030] Step S2-3, calculate the average rate of change of the characteristic information entropy curve,

[0031]

[0032] Among them, ch represents the average change rate of the characteristic information entropy curve; h(u) represents the size of the transmission data information entropy of sample u, and u is the sample label; te_u represents the characteristic factor value of sample u; i represents the total number of samples, 2≤u≤i+1;

[0033] Step S2-4, calculate {ch_1, ch_2, ..., ch_z} in sequence, where ch_1, ch_2, ..., ch_z represent the average change rate of the characteristic information entropy curve corresponding to the characteristic factors te1, te2, ..., tez respectively; the characteristic factor whose average change rate of the characteristic information entropy curve exceeds the change rate threshold is marked as a key factor; the change rate threshold calculation formula is:

[0034]

[0035] Among them, re represents the change rate threshold; ch_1, ch_2, ..., ch_z represent the average change rates of the characteristic information entropy curves corresponding to the characteristic factors te1, te2, ..., tez respectively; z represents the number of characteristic factors.

[0036] In step S3, the specific steps of using key factor data to train the information entropy calculation model are:

[0037] Step S3-1, recording the data values ​​of key factors in the transmitted data through the agricultural transmission database, and counting the number of sample values ​​of each key factor;

[0038] Step S3-2: Calculate the frequency of occurrence of each key factor sample value according to the number of each key factor sample value.

[0039]

[0040] Among them, p(x_j) represents the frequency of occurrence of the key factor sample value x_j; count(x_j) represents the number of occurrences of the key factor sample value x_j; count(total) represents the number of all sample values ​​in the key factor;

[0041] Step S3-3, information entropy calculation,

[0042] H(X)=-∑ s p(x_j)log2p(x_j)

[0043] Among them, H(X) represents the information entropy of the transmitted data X; s represents the number of key factors; p(x_j) represents the frequency of occurrence of the key factor sample value x_j.

[0044] In order to ensure that the choice of key length L is closely related to the data information entropy H(X), in order to ensure security and minimize costs, an exponential mapping function is designed:

[0045] In step S5, the specific formula for dynamically adjusting the encryption key length of the current transmission data according to the information entropy of the transmission data is:

[0046]

[0047] Where L represents the key length for encrypting transmitted data; L min represents the minimum key length; α is a tuning parameter that controls the sensitivity of the key length to changes in data entropy; H min Indicates the minimum value of information entropy of historical data.

[0048] Under this mapping relationship, when the entropy value increases slightly, the key length will also increase rapidly, especially in the range of higher entropy values, which helps to resist stronger attacks.

[0049] We want to strike a balance between security and computational cost, so we can define a multi-objective optimization function to optimize the choice of encryption key length;

[0050] In step S6, a multi-objective function is defined to calculate the theoretical key length. Specifically, the multi-objective function formula is:

[0051]

[0052] sg(L)=w*log2L

[0053] cc(L)=m*L 2

[0054] in, represents the function value of the multi-objective function; sg(L) represents the security gain of the key length L; w is a security constant, which is selected according to different encryption algorithms; cc(L) represents the computing resource consumption; m is the computing complexity constant; μ represents the balance coefficient;

[0055] The theoretical key length calculation formula is:

[0056]

[0057] Among them, L * Indicates the theoretical key length;

[0058] When the key length L≤L * When L * As the key length of the data to be transmitted; when the key length L>L * , continue to use L as the key length of the current data to be transmitted.

[0059] In step S7, the specific steps are:

[0060] Step S7-1, backing up the current agricultural record data and the calculation process data of the key dynamic adjustment;

[0061] Step S7-2: Import the backed-up data into the agricultural transmission database to update the database data.

[0062] An encryption management system based on communication security, the encryption management system includes a data acquisition module, an agricultural transmission database module, a data analysis module, an information entropy calculation model training module, an information entropy calculation module, a key length dynamic adjustment module, an encryption and transmission module and a data recording and updating module;

[0063] The data acquisition module is used to collect agricultural data, including historical transmission record data and current real-time data; historical data is used to establish a basic database, and real-time data is used to calculate information entropy before encryption;

[0064] The agricultural transmission database module is used to store and manage all historical data and current data, serving as the data basis of the system, and providing data support for the training of the information entropy calculation model and the dynamic adjustment of the key;

[0065] The data analysis module is used to perform statistical analysis on the data in the agricultural transmission database and identify the key factors that affect the information entropy of the transmission data;

[0066] The information entropy calculation model training module uses a large amount of agricultural transmission data to train the information entropy calculation model based on the key factors identified by the data analysis module;

[0067] The information entropy calculation module is used to calculate the information entropy of current data based on the agricultural data collected in real time and the trained model;

[0068] The key length dynamic adjustment module is used to dynamically adjust the encryption key length required for data transmission according to the calculation result of information entropy;

[0069] The encryption and transmission module is used for data encryption and transmission to ensure data security;

[0070] The data recording and updating module is used to record the data transmission during the system operation, the decision-making process and results of the key length adjustment, and update the agricultural transmission database.

[0071] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention draws an information entropy curve based on historical transmission data and historical transmission information entropy, obtains key factors affecting information entropy, trains an information entropy calculation model based on the key factors, calculates the information entropy of the current data to be transmitted in real time, calculates the encryption key length based on the information entropy, and reasonably selects the key length, thereby avoiding the waste of computing resources and the user burden caused by complex encryption, and further ensuring the security of encrypted data by calculating the theoretical key length. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] Figure 1 A schematic diagram of a flow chart of an encryption management method based on communication security of the present invention;

[0073] Figure 2 This is a characteristic information entropy curve of an encryption management method based on communication security in the present invention. DETAILED DESCRIPTION

[0074] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0075] Example: Figure 1-Figure 2 As shown, the present invention provides a technical solution, an encryption management method based on communication security, and the encryption management method comprises the following steps:

[0076] Step S1, collecting agricultural record data in historical transmission, collecting agricultural record data that needs to be transmitted currently, establishing an agricultural transmission database, and updating the database in stages;

[0077] The agricultural transmission database includes agricultural record data and key dynamic adjustment data.

[0078] In step S1, the agricultural record data includes communication content data, data distribution characteristics, security threat data and encryption algorithm performance data;

[0079] The communication content data includes text data {T1, T2, ..., Tn}, where T1, T2, ..., Tn represent the 1st, 2nd, ..., nth occurrence of text data samples, image data {I1, I2, ..., In}, where I1, I2, ..., In represent the 1st, 2nd, ..., nth types of image data samples, and audio data {A1, A2, ..., An}, where A1, A2, ..., An represent the 1st, 2nd, ..., nth sampled audio data, respectively; these data are samples of real-time communication traffic or historical communication records.

[0080] The data distribution features include character frequency distribution {f(c1), f(c2), ..., f(ck)}, where f(c1), f(c2), ..., f(ck) represent the character frequency distribution, byte distribution and packet length distribution of the 1st, 2nd, ..., kth time windows respectively;

[0081] The security threat data includes known attack patterns, threat models and attack success rate information of different encryption algorithms;

[0082] The encryption algorithm performance data is the computing performance data of different encryption algorithms under different key lengths, including encryption / decryption time, CPU / GPU consumption and memory usage.

[0083] The representation matrix of the encryption / decryption time is:

[0084] Wherein, L_K1, L_K2, ..., L_Km represent m keys of different lengths respectively, K represents the type of encryption algorithm; t_enc(L_K1), t_enc(L_K2), ..., t_enc(L_Km) represent the encryption time corresponding to the key length L_K1, L_K2, ..., L_Km respectively; t_dec(L_K1), t_dec(L_K2), ..., t_dec(L_Km) represent the decryption time corresponding to the key length L_K1, L_K2, ..., L_Km respectively;

[0085] The representation matrix of the CPU / GPU consumption is:

[0086] Among them, K_1, K_2, ..., K_r represent r different encryption algorithms respectively; C_cpu(K_1), C_cpu(K_2), ..., C_cpu(K_r) represent the CPU occupancy rates of the corresponding encryption algorithms K_1, K_2, ..., K_r respectively; C_gpu(K_1), C_gpu(K_2), ..., C_gpu(K_r) represent the GPU occupancy rates of the corresponding encryption algorithms K_1, K_2, ..., K_r respectively.

[0087] Step S2, statistically analyzing the agricultural record data, and obtaining key factors affecting the information entropy of the transmitted data through the statistical analysis results;

[0088] In step S2, the step of performing statistical analysis on the agricultural record data and obtaining the key factors affecting the information entropy of the transmission data through the statistical analysis results is specifically as follows:

[0089] Step S2-1, extracting characteristic factors affecting the information entropy of transmission data {[te1_1, te1_2, ..., te1_i], [te2_1, te2_2, ..., te2_i], ..., [tez_1, tez_2, ..., tez_i]} through the agricultural transmission database, wherein te1, te2, ..., tez respectively represent the 1st, 2nd, ..., zth characteristic factors affecting the information entropy of transmission data; te1_1, te1_2, ..., te1_i respectively represent the i sample values ​​of the characteristic factor te1; te2_1, te2_2, ..., te2_i respectively represent the i sample values ​​of the characteristic factor te2; tez_1, tez_2, ..., tez_i respectively represent the i sample values ​​of the characteristic factor tez;

[0090] The characteristic factors that affect the information entropy of transmitted data include data file size, data information type and quantity, probability distribution of data source, complexity and change rate of data content, data sampling rate and resolution, etc.

[0091] Sample data of characteristic factors:

[0092] Data file size (KB): [100, 200, 300, 400, 500];

[0093] Type and quantity of data information (number of types): [3,5,7,9,11];

[0094] Probability distribution of data source: [0.1, 0.2, 0.3, 0.15, 0.25];

[0095] Data content complexity and change rate (unit change rate): [1, 1.5, 2, 2.5, 3];

[0096] Data sampling rate and resolution (sampling rate Hz): [10, 20, 30, 40, 50];

[0097] Input data for information entropy calculation:

[0098] Frequency of occurrence of characteristic factors:

[0099] Data sequence length: [40, 10, 50, 30, 35];

[0100] Type and quantity of data information: [10, 12, 6, 8, 14];

[0101] Data sampling frequency: [2,4,3,4,4];

[0102] like Figure 2 As shown, draw the characteristic information entropy curve:

[0103] Step S2-2, construct a coordinate pair with the sample value of each characteristic factor as the horizontal coordinate and the size of the information entropy of the transmitted data as the vertical coordinate, plot points in a plane rectangular coordinate system, and connect them with a smooth curve to form a characteristic information entropy curve;

[0104] Step S2-3, calculate the average rate of change of the characteristic information entropy curve,

[0105]

[0106] Among them, ch represents the average change rate of the characteristic information entropy curve; h(u) represents the size of the transmission data information entropy of sample u, and u is the sample label; te_u represents the characteristic factor value of sample u; i represents the total number of samples, 2≤u≤i+1;

[0107] Step S2-4, calculate {ch_1, ch_2, ..., ch_z} in sequence, where ch_1, ch_2, ..., ch_z represent the average change rate of the characteristic information entropy curve corresponding to the characteristic factors te1, te2, ..., tez respectively; the characteristic factor whose average change rate of the characteristic information entropy curve exceeds the change rate threshold is marked as a key factor; the change rate threshold calculation formula is:

[0108]

[0109] Among them, re represents the change rate threshold; ch_1, ch_2, ..., ch_z represent the average change rates of the characteristic information entropy curves corresponding to the characteristic factors te1, te2, ..., tez respectively; z represents the number of characteristic factors.

[0110] Step S3, using key factor data to train the information entropy calculation model;

[0111] In step S3, the use of key factor data to train the information entropy calculation model specifically includes the following steps:

[0112] Step S3-1, recording the data values ​​of key factors in the transmitted data through the agricultural transmission database, and counting the number of sample values ​​of each key factor;

[0113] Step S3-2: Calculate the frequency of occurrence of each key factor sample value according to the number of each key factor sample value.

[0114]

[0115] Among them, p(x_j) represents the frequency of occurrence of the key factor sample value x_j; count(x_j) represents the number of occurrences of the key factor sample value x_j; count(total) represents the number of all sample values ​​in the key factor;

[0116] Step S3-3, information entropy calculation,

[0117] H(X)=-∑ s p(x_j)log2p(x_j)

[0118] Among them, H(X) represents the information entropy of the transmitted data X; s represents the number of key factors; p(x_j) represents the frequency of occurrence of the key factor sample value x_j.

[0119] In order to ensure that the choice of key length L is closely related to the data information entropy H(X), in order to ensure security and minimize costs, an exponential mapping function is designed:

[0120] Step S4, collecting the key factor data values ​​in the current agricultural record data in real time, and inputting the collected data into the information entropy calculation model to calculate the information entropy of the transmission data;

[0121] Step S5, dynamically adjusting the encryption key length of the current transmission data according to the information entropy of the transmission data;

[0122] In step S5, the specific formula for dynamically adjusting the encryption key length of the current transmission data according to the information entropy of the transmission data is:

[0123]

[0124] Where L represents the key length for encrypting transmitted data; L min represents the minimum key length; α is a tuning parameter that controls the sensitivity of the key length to changes in data entropy; H min Indicates the minimum value of information entropy of historical data.

[0125] Historical data information entropy: H min =1.5, H max =4.5;

[0126] Key length adjustment parameters:

[0127] Minimum key length: L min =128;

[0128] For the frequency of occurrence of file sequence length:

[0129] H(size)=-[0.2*log2(40)+0.15*log2(40)+0.25*log2(50)+0.1*log2(30)+0.3*

[0130] log2(35)]

[0131] Frequency of occurrence of data types and quantities:

[0132] H(type)=-[0.18log2(2)+0.2*log2(4)+0.3*log2(3)+0.2*log2(4)+0.2*log2](4)

[0133] Information entropy of data file size:

[0134] H(size):2.23;

[0135] Information entropy of data types and quantities

[0136] H(type):2.25;

[0137] Based on the information entropy of the data file size, the key length H(size): about 221 bits;

[0138] Based on the information entropy of the type and amount of data information, the key length H(type): about 224 bits;

[0139] Under this mapping relationship, when the entropy value increases slightly, the key length will also increase rapidly, especially in the range of higher entropy values, which helps to resist stronger attacks.

[0140] We want to strike a balance between security and computational cost, so we can define a multi-objective optimization function to optimize the choice of encryption key length;

[0141] Step S6: define a multi-objective function, calculate the theoretical key length, select the key length according to the information entropy, encrypt and transmit the data to be transmitted;

[0142] In step S6, a multi-objective function is defined to calculate the theoretical key length. Specifically, the multi-objective function formula is:

[0143]

[0144] sg(L)=w*log2L

[0145] cc(L)=m*L 2

[0146] in, represents the function value of the multi-objective function; sg(L) represents the security gain of the key length L; w is a security constant, which is selected according to different encryption algorithms; cc(L) represents the computing resource consumption; m is the computing complexity constant; μ represents the balance coefficient;

[0147] Adjustment parameters: α = 256;

[0148] Optimize function weights:

[0149] Safety constant: w = 0.7;

[0150] Computational complexity constant: m = 2;

[0151] Balance coefficient: μ = 0.5;

[0152] The theoretical key length calculation formula is:

[0153]

[0154] Among them, L * Indicates the theoretical key length;

[0155] When the key length L≤L * When L * As the key length of the data to be transmitted; when the key length L>L * , continue to use L as the key length of the current data to be transmitted.

[0156] Step S7, backing up the agricultural record data collected in real time and the calculation process data of the key dynamic adjustment, and inputting them into the agricultural transmission database to update the database information;

[0157] In step S7, the specific steps are:

[0158] Step S7-1, backing up the current agricultural record data and the calculation process data of the key dynamic adjustment;

[0159] Step S7-2: Import the backed-up data into the agricultural transmission database to update the database data.

[0160] An encryption management system based on communication security, the encryption management system includes a data acquisition module, an agricultural transmission database module, a data analysis module, an information entropy calculation model training module, an information entropy calculation module, a key length dynamic adjustment module, an encryption and transmission module and a data recording and updating module;

[0161] The data acquisition module is used to collect agricultural data, including historical transmission record data and current real-time data; historical data is used to establish a basic database, and real-time data is used to calculate information entropy before encryption;

[0162] The agricultural transmission database module is used to store and manage all historical data and current data, serving as the data basis of the system, and providing data support for the training of the information entropy calculation model and the dynamic adjustment of the key;

[0163] The data analysis module is used to perform statistical analysis on the data in the agricultural transmission database and identify the key factors that affect the information entropy of the transmission data;

[0164] The information entropy calculation model training module uses a large amount of agricultural transmission data to train the information entropy calculation model based on the key factors identified by the data analysis module;

[0165] The information entropy calculation module is used to calculate the information entropy of current data based on the agricultural data collected in real time and the trained model;

[0166] The key length dynamic adjustment module is used to dynamically adjust the encryption key length required for data transmission according to the calculation result of information entropy;

[0167] The encryption and transmission module is used for data encryption and transmission to ensure data security;

[0168] The data recording and updating module is used to record the data transmission during the system operation, the decision-making process and results of the key length adjustment, and update the agricultural transmission database.

[0169] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. An encryption management method based on communication security, characterized in that: The encryption management method comprises the following steps: Step S1, collecting agricultural record data in historical transmission, collecting agricultural record data that needs to be transmitted currently, establishing an agricultural transmission database, and updating the database in stages; Step S2, statistically analyzing the agricultural record data, and obtaining key factors affecting the information entropy of the transmitted data through the statistical analysis results; In step S2, the agricultural record data is statistically analyzed, and the specific steps of obtaining the key factors affecting the information entropy of the transmission data through the statistical analysis results are: Step S2-1, extracting characteristic factors affecting the information entropy of transmission data {[te1_1, te1_2, ..., te1_i], [te2_1, te2_2, ..., te2_i], ..., [tez_1, tez_2, ..., tez_i]} through the agricultural transmission database, wherein te1, te2, ..., tez respectively represent the 1st, 2nd, ..., zth characteristic factors affecting the information entropy of transmission data; te1_1, te1_2, ..., te1_i respectively represent the i sample values ​​of the characteristic factor te1; te2_1, te2_2, ..., te2_i respectively represent the i sample values ​​of the characteristic factor te2; tez_1, tez_2, ..., tez_i respectively represent the i sample values ​​of the characteristic factor tez; Step S2-2, construct a coordinate pair with the sample value of each characteristic factor as the horizontal coordinate and the size of the information entropy of the transmitted data as the vertical coordinate, plot points in a plane rectangular coordinate system, and connect them with a smooth curve to form a characteristic information entropy curve; Step S2-3, calculate the average rate of change of the characteristic information entropy curve, Among them, ch represents the average change rate of the characteristic information entropy curve; h(u) represents the size of the transmission data information entropy of sample u, and u is the sample label; te_u represents the characteristic factor value of sample u; i represents the total number of samples, 2≤u≤i+1; Step S2-4, calculate {ch_1, ch_2, ..., ch_z} in sequence, where ch_1, ch_2, ..., ch_z represent the average change rate of the characteristic information entropy curve corresponding to the characteristic factors te1, te2, ..., tez respectively; the characteristic factor whose average change rate of the characteristic information entropy curve exceeds the change rate threshold is marked as a key factor; the change rate threshold calculation formula is: Wherein, re represents the change rate threshold; ch_1, ch_2, ..., ch_z represent the average change rates of the characteristic information entropy curves corresponding to the characteristic factors te1, te2, ..., tez respectively; z represents the number of characteristic factors; Step S3, using key factor data to train the information entropy calculation model; In step S3, the specific steps of using key factor data to train the information entropy calculation model are: Step S3-1, extracting data values ​​of key factors in the transmission data through the agricultural transmission database, and counting the number of sample values ​​of each key factor; Step S3-2: Calculate the frequency of occurrence of each key factor sample value according to the number of each key factor sample value. Among them, p(x_j) represents the frequency of occurrence of the key factor sample value x_j; count(x_j) represents the number of occurrences of the key factor sample value x_j; count(total) represents the number of all sample values ​​in the key factor; Step S3-3, information entropy calculation, H(x)=-∑ s p(x_j)log2p(x_j) Where H(X) represents the information entropy of the transmitted data X; s represents the number of key factors; p(x_j) represents the frequency of occurrence of the key factor sample value x_j; Step S4, collecting the key factor data values ​​in the current agricultural record data in real time, and inputting the collected data into the information entropy calculation model to calculate the information entropy of the transmission data; Step S5, dynamically adjusting the encryption key length of the current transmission data according to the information entropy of the transmission data; In step S5, the specific formula for dynamically adjusting the encryption key length of the current transmission data according to the information entropy of the transmission data is: Where L represents the key length for encrypting transmitted data; L min represents the minimum key length; α is a tuning parameter that controls the sensitivity of the key length to changes in data entropy; H min Indicates the minimum value of historical data information entropy; Step S6: define a multi-objective function, calculate the theoretical key length, select the key length according to the information entropy, encrypt and transmit the data to be transmitted; Step S7, backing up the agricultural record data collected in real time and the calculation process data of the key dynamic adjustment, and inputting them into the agricultural transmission database to update the database information; The agricultural transmission database includes agricultural record data and key dynamic adjustment data.

2. The encryption management method based on communication security according to claim 1, characterized in that: In step S1, the agricultural record data includes communication content data, data distribution characteristics, security threat data and encryption algorithm performance data; The communication content data includes text data {T1, T2, ..., Tn}, where T1, T2, ..., Tn represent the 1st, 2nd, ..., nth occurrence of text data samples, image data {I1, I2, ..., In}, where I1, I2, ..., In represent the 1st, 2nd, ..., nth types of image data samples, and audio data {A1, A2, ..., An}, where A1, A2, ..., An represent the 1st, 2nd, ..., nth sampling of audio data, respectively; The data distribution features include character frequency distribution {f(c1), f(c2), ..., f(ck)}, where f(c1), f(c2), ..., f(ck) represent the character frequency distribution, byte distribution and packet length distribution of the 1st, 2nd, ..., kth time windows respectively; The security threat data includes known attack patterns, threat models and attack success rate information of different encryption algorithms; The encryption algorithm performance data is the computing performance data of different encryption algorithms under different key lengths, including encryption / decryption time, CPU / GPU consumption and memory usage.

3. The encryption management method based on communication security according to claim 2 is characterized in that: The representation matrix of the encryption / decryption time is: Wherein, L_K1, L_K2, ..., L_Km represent m keys of different lengths respectively, K represents the type of encryption algorithm; t_enc(L_K1), t_enc(L_K2), ..., t_enc(L_Km) represent the encryption time corresponding to the key length L_K1, L_K2, ..., L_Km respectively; t_dec(L_K1), t_dec(L_K2), ..., t_dec(L_Km) represent the decryption time corresponding to the key length L_K1, L_K2, ..., L_Km respectively; The representation matrix of the CPU / GPU consumption is: Among them, K_1, K_2, ..., K_r represent r different encryption algorithms respectively; C_cpu(K_1), C_cpu(K_2), ..., C_cpu(K_r) represent the CPU occupancy rates of the corresponding encryption algorithms K_1, K_2, ..., K_r respectively; C_gpu(K_1), C_gpu(K_2), ..., C_gpu(K_r) represent the GPU occupancy rates of the corresponding encryption algorithms K_1, K_2, ..., K_r respectively.

4. The encryption management method based on communication security according to claim 3 is characterized in that: In step S6, a multi-objective function is defined to calculate the theoretical key length. Specifically, the multi-objective function formula is: sg(L)=w*log2L cc(L)=m*L 2 in, represents the function value of the multi-objective function; sg(L) represents the security gain of the key length L; w is a security constant, which is selected according to different encryption algorithms; cc(L) represents the computing resource consumption; m is the computing complexity constant; μ represents the balance coefficient; The theoretical key length calculation formula is: Among them, L * Indicates the theoretical key length; When the key length L≤L * When L * As the key length of the data to be transmitted; when the key length L>L * When L is used as the key length of the data to be transmitted, 5. The encryption management method based on communication security according to claim 4 is characterized in that: In step S7, the specific steps are: Step S7-1, backing up the current agricultural record data and the calculation process data of the key dynamic adjustment; Step S7-2: Import the backed-up data into the agricultural transmission database to update the database data.

6. An encryption management system based on communication security, applying an encryption management method based on communication security as claimed in any one of claims 1 to 5, characterized in that: The encryption management system includes a data acquisition module, an agricultural transmission database module, a data analysis module, an information entropy calculation model training module, an information entropy calculation module, a key length dynamic adjustment module, an encryption and transmission module, and a data recording and updating module; The data acquisition module is used to collect agricultural data, including historical transmission record data and current real-time data; historical data is used to establish a basic database, and real-time data is used to calculate information entropy before encryption; The agricultural transmission database module is used to store and manage all historical data and current data, serving as the data basis of the system, and providing data support for the training of the information entropy calculation model and the dynamic adjustment of the key; The data analysis module is used to perform statistical analysis on the data in the agricultural transmission database and identify the key factors that affect the information entropy of the transmission data; The information entropy calculation model training module uses a large amount of agricultural transmission data to train the information entropy calculation model based on the key factors identified by the data analysis module; The information entropy calculation module is used to calculate the information entropy of current data based on the agricultural data collected in real time and the trained model; The key length dynamic adjustment module is used to dynamically adjust the encryption key length required for data transmission according to the calculation result of information entropy; The encryption and transmission module is used for data encryption and transmission to ensure data security; The data recording and updating module is used to record the data transmission during the system operation, the decision-making process and results of the key length adjustment, and update the agricultural transmission database.

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