A data security storage method under 5G network security management

Abstract

The application relates to the technical field of data security, in particular to a data security storage method under 5G network security management, which comprises the following steps: collecting communication data in a 5G network to determine to-be-encrypted data; clustering the communication data to obtain data clusters; performing interpolation processing on a numerical sequence based on the length of standard data of the data clusters to obtain a communication feature matrix; determining a template feature vector by using a dimension reduction technology; determining the regional difference degree of a maximum value in a neighborhood range based on the distribution of elements on both sides of the maximum value in a distance sequence of each row element; determining a key sequence based on the regional difference degree; determining an offset coefficient according to the key sequence; determining the encryption key of data in each row element based on the offset coefficient by using a logistics transformation encryption technology; completing the encryption of the communication data based on the encryption key; and saving the ciphertext of all data. The application improves the secure encryption effect of the communication data by using an adaptive key encryption mode.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data security, in particular to a data security storage method under 5G network security management. BACKGROUND

[0002] With the rapid development of network communication technology, the amount of communication data in different fields continues to grow, and the number of 5G network base stations is also gradually increasing, so that the traffic of communication data under the 5G network is getting larger and larger, and the information communication load of network bandwidth, receiving end and communication sending end is gradually increasing. The possibility of information leakage caused by the huge data transmission amount of 5G network is also gradually increasing, which needs to be ensured by identity verification, data encryption and intrusion detection technology to ensure data security.

[0003] In the process of big data transmission of 5G network, data encryption and privacy protection become crucial. By encrypting the data transmitted in 5G network, it is ensured that sensitive information and private information will not be stolen or tampered with in the transmission process. At present, the security protection of communication data is realized by using encryption algorithm, but when using encryption algorithm to protect data, the same degree or the same key is usually used to encrypt the data to be encrypted, ignoring the difference between data, resulting in insufficient security of encrypted ciphertext to resist malicious attacks, still facing network security problems. SUMMARY

[0004] The present application provides a data security storage method under 5G network security management to solve the existing problems.

[0005] One embodiment of the present application provides a data security storage method under 5G network security management, which adopts the following technical scheme:

[0006] Collecting communication data in 5G network to determine data to be encrypted;

[0007] Clustering the communication data to obtain a plurality of data clusters, performing interpolation processing on the numerical sequence based on the length of the communication data corresponding to each data point in each data cluster to obtain a communication feature matrix, and determining a template feature vector of the communication feature matrix by using dimension reduction technology;

[0008] Determining the regional difference degree of the maximum value in the neighborhood range based on the distribution of the elements on both sides of the maximum value in the distance sequence of each row element in the communication feature matrix, determining the key sequence of each row element based on the regional difference degree, and determining the offset coefficient of each row element according to the key sequence of each row element in the communication feature matrix;

[0009] Determining the encryption key of each data in each row element based on the offset coefficient of each row element by using logistics transformation encryption technology;

[0010] Based on the encryption key, the encryption of the communication data is completed, and the ciphertext of all data is saved.

[0011] In one embodiment, the clustering processing of the communication data obtains a plurality of data clusters, including the following specific steps:

[0012] Each piece of communication data is converted into a decimal number form through a UTF-8 encoding conversion technology, and a numerical sequence corresponding to each piece of communication data is obtained.

[0013] The mean value of all numerical values in the numerical sequence corresponding to each piece of communication data is taken as the first characteristic value of each piece of communication data.

[0014] The number of numerical values in the numerical sequence corresponding to each piece of communication data is taken as the data length of each piece of communication data, and a clustering space is constructed according to the length and the first characteristic value as the horizontal and vertical coordinates.

[0015] All communication data is divided into a plurality of data clusters based on the first characteristic value and the data length of the corresponding data points in the clustering space by using a data clustering algorithm.

[0016] In one embodiment, the interpolation processing of the numerical sequence based on the length of the communication data corresponding to the data points in each data cluster obtains a communication feature matrix, including the following specific steps:

[0017] For any data cluster, the communication data with the maximum data length in the communication data corresponding to the data points in each data cluster is taken as the standard data of each data cluster.

[0018] For the communication data corresponding to the data points other than the standard data, the numerical sequence corresponding to the other data points is interpolated and expanded to the data length of the standard data by using the mean value filling method.

[0019] The standard data of each data cluster and the interpolation processing result of the numerical sequence corresponding to the data points in each data cluster are taken as a row element in the matrix, and the communication feature matrix of each data cluster is obtained.

[0020] In one embodiment, the template feature vector of the communication feature matrix is determined by using a dimension reduction technology, including the following specific steps:

[0021] A plurality of feature vectors of the communication feature matrix and the characteristic value of each feature vector are obtained by using a data dimension reduction technology, and the feature vector corresponding to the maximum value of all characteristic values is taken as the template feature vector of each data cluster.

[0022] In one embodiment, the determination method of the regional difference degree of the maximum value in the neighborhood range is as follows:

[0023] calculating absolute values of element difference between each row element in the communication feature matrix of each data cluster and the template feature vector of each data cluster, and taking a sequence composed of all the absolute values of the element difference as a distance sequence of each row element;

[0024] For any maximum value in the distance sequence, taking the first quantity of adjacent elements on the left and right of each maximum value as a first neighborhood range of each maximum value;

[0025] In the value range determined according to the length of the distance sequence, the first quantity is iterated once with a step of 1 to obtain a second quantity, and the second quantity of adjacent elements on the left and right of each maximum value is taken as a second neighborhood range of each maximum value;

[0026] Taking a mapping result of the cumulative sum of all elements in the second neighborhood range of each maximum value in the sigmoid function and a mapping result of the cumulative sum of all elements in the first neighborhood range of each maximum value in the sigmoid function as a regional difference degree of each maximum value in the second neighborhood range.

[0027] In one embodiment, the key sequence of each row element is determined based on the regional difference degree, and the specific steps include the following:

[0028] t0: respectively obtaining the regional difference degree of each maximum value in the same neighborhood range, and calculating a first quartile of the regional difference degrees of all maximum values in the same neighborhood range as a decision threshold of the neighborhood range;

[0029] t1: calculating the regional difference degree of each maximum value in each neighborhood range, and if the regional difference degree of the maximum value in a neighborhood range is less than the decision threshold of the neighborhood range, iterating the neighborhood range until the regional difference degree of the maximum value in the neighborhood range is greater than the decision threshold of the neighborhood range;

[0030] t2: taking a set composed of all elements in the neighborhood range of each maximum value as a screening area set of the distance sequence of the maximum value when the regional difference degree of each maximum value in the neighborhood range is greater than the decision threshold of the neighborhood range;

[0031] Taking a sequence composed of all screening area sets in each distance sequence in the order of the first element value from small to large as a key sequence of each row element in the communication feature matrix;

[0032] t3: determining the maximum value in the distance sequence of each row element in the communication feature matrix, repeating the above steps t0-t2, and respectively obtaining the key sequence of each row element in the communication feature matrix.

[0033] In one embodiment, the offset coefficient of each row element is determined in the following manner:

[0034] The product of the number of maximum values in the distance sequence of each row element and the kurtosis of the key sequence of each row element is taken as the offset coefficient of each row element.

[0035] In one embodiment, the encryption key of each data in each row element is determined in the following manner:

[0036] The iteration key sequence of each row element is determined based on the first characteristic value of the corresponding communication data of each row element; the encryption key of each data in each row element is determined based on the iteration key sequence:

[0037]

[0038] In the formula, key i,m is the encryption key of the mth data in the ith row element, bin is the binary conversion operator symbol, 256 is the base of the binary number, V m,i is the iteration key of the mth data in the ith row element, >> is the binary right shift, length(i) is the number of data in the ith row element, mod is the remainder operation, q i is the offset coefficient of the ith row element, is the upward rounding symbol.

[0039] In one embodiment, the iteration key sequence of each row element is determined in the following manner:

[0040] The iteration keys of all elements in each row element are arranged in the order of element position to obtain the iteration key sequence of each row element.

[0041] In one embodiment, the encryption of the communication data based on the encryption key comprises the specific formula as follows:

[0042] IM i,m = bin(Z i,m ) xor key i,m

[0043] In the formula, IM i,m is the ciphertext of the mth data in the ith row element, Z i,m is the mth data in the ith row element, xor is the exclusive or operator, key i,m is the encryption key of the mth data in the ith row element, and bin is the binary conversion operator symbol.

[0044] The beneficial effects of the technical scheme of the present application are: firstly, the clustering space is constructed by the first characteristic value of the communication data and the data length, so as to realize the clustering division of the communication data; secondly, the numerical value sequence corresponding to all data points is interpolated and filled by the length of the standard data in each data cluster, which can ensure that the length is filled while the distribution of the original data in the numerical value sequence is as little affected as possible, and the data security can be further enhanced after filling the additional data; then, considering that there may be repetitive content and high privacy content between different communication data, the regional difference degree of the maximum value in the neighborhood range is determined based on the distribution of the elements on both sides of the maximum value in the distance sequence of each row element, so as to filter the data points corresponding to the important content in the communication data by gradually expanding the neighborhood range; then, the key sequence of each row element and the iteration coefficient are determined by the way of loop iteration, so as to facilitate the subsequent adaptive adjustment of the shift length during key generation; finally, the communication data is encrypted based on the generated encryption key, and the ciphertext of all data is securely saved. The present application enhances the secure encryption effect of communication data by using the adaptive key encryption method. BRIEF DESCRIPTION OF DRAWINGS

[0045] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description only constitute some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor.

[0046] Figure 1 A step flow chart of a data security storage method under 5G network security management provided by an embodiment of the present application;

[0047] Figure 2 A flow chart of obtaining a template feature vector of a data cluster provided by an embodiment of the present application. DETAILED DESCRIPTION

[0048] The technical solutions in the embodiments of the present application will be described clearly and completely in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments only constitute some embodiments of the present application, rather than all the embodiments. All other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0049] Please refer to Figure 1 which shows a step flow chart of a data security storage method under 5G network security management provided by an embodiment of the present application. The method comprises the following steps:

[0050] Step S001: collecting communication data in a 5G network to determine data to be encrypted.

[0051] The purpose of the present application is to protect the security of communication data by encrypting the communication data in the 5G network, so it is necessary to collect the communication data first.

[0052] Specifically, in the present application, the way of data communication in the 5G network includes simplex communication, half-duplex communication and full-duplex communication, and the way of obtaining communication data includes but is not limited to communication sending end, communication receiving end and network node. The present application does not make special restrictions on the way of data communication in the 5G network and the place of obtaining communication data.

[0053] Preferably, as an embodiment of the present application, taking the communication in the call-answer mode in the full-duplex communication mode as an example, the communication data in the form of a strip generated by the caller and the respondent in the communication is collected, and the UTF-8 (Unicode Transformation Format-8) encoding conversion technology is used to convert each piece of communication data into a decimal number form to obtain a numerical sequence corresponding to each piece of communication data as the data to be encrypted. The UTF-8 encoding conversion technology is a common technology in the field of data communication, and the specific process is not described here.

[0054] Step S002: clustering the communication data to obtain a plurality of data clusters, constructing a communication feature matrix of the data clusters, and determining a template feature vector of the data clusters.

[0055] It should be noted that the communication exchange in the call-answer mode usually relies on standard forms for reply, so the similarity of the communication content between the communication exchange information of the callers in the same field is high. For example, in the process of communication between the civil aviation tower in the aviation field and the tower, the same department only has differences in flight number and content in the communication process with the tower. Such information with differences is usually considered as data information with high privacy. In the process of communication between the ship and the shore station in the maritime field, the call symbol of the ship needs to be confirmed first, and then the information transmission such as inquiry and notification is carried out. In the communication between different ships and the shore station, the call symbol of the ship and the transmitted information have obvious differences, while the position, direction and sailing speed are usually similar due to the relevant system constraints. Therefore, such information with obvious differences is usually considered as important information for encryption and protection.

[0056] Therefore, the embodiment of the present application constructs a clustering space according to the overall distribution of each piece of communication data, and clusters all pieces of communication data to obtain a plurality of data clusters; the communication data in each data cluster has a high degree of similarity, and only the elements belonging to the important information region are different. Therefore, the embodiment of the present application obtains the difference of the important information region of each piece of communication data in a data cluster, and obtains an offset coefficient according to the important region difference, which is used to generate a key.

[0057] Specifically, the embodiment obtains the mean value of all values in the value sequence corresponding to each piece of communication data as the first characteristic value of each piece of communication data, and obtains the data length of each piece of communication data, the data length being the number of values in the value sequence of each piece of communication data, and constructs a clustering space according to the length and the first characteristic value, wherein the horizontal axis of the clustering space is the data length, and the vertical axis is the first characteristic value.

[0058] Further, the first characteristic value and the data length of all pieces of communication data are obtained respectively, and based on the first characteristic value and the data length, the corresponding data point of each piece of communication data in the clustering space is determined, and all the data points are divided into k data clusters by using a data clustering algorithm, and the data points in each data cluster correspond to communication data between which the data length and the first characteristic value are relatively close.

[0059] It should be noted that data clustering is a common technique in the field of data processing, and the data clustering algorithm includes but is not limited to AP (Affinity Propagation) clustering algorithm, K-means clustering algorithm, and hierarchical clustering algorithm, which are not specially limited by the present application.

[0060] Preferably, as an embodiment, the AP clustering algorithm is used to divide all data points into k data clusters. In particular, for the clustering algorithm that needs to preset the number of clustering clusters, the elbow chart method is used to determine the preset number of clustering clusters, and the elbow chart method is a common technique in the field of data processing, and the specific process will not be repeated.

[0061] Further, for any data cluster, the embodiment of the present application further obtains a communication feature matrix for constructing the data cluster, and determines a template feature vector of the data cluster. The obtaining process of the template feature vector is as shown in Figure 2 , which includes steps 201 and 202:

[0062] 201, obtaining the standard data of each data cluster, and performing interpolation processing on the value sequence of other communication data.

[0063] Firstly, the communication data with the largest data length in the communication data corresponding to the data points in each data cluster is taken as the standard data of each data cluster. In particular, if there are multiple pieces of communication data corresponding to the largest data length, the communication data corresponding to the data point with the closest distance to the cluster center of the data cluster is taken as the standard data. The distance is the distance between data points in the clustering process.

[0064] Secondly, for the communication data corresponding to the data points other than the data point corresponding to the standard data in each data cluster, the other communication data is respectively interpolated and expanded to the data length of the standard data. The interpolation method is mean interpolation method, and the mean value of the data in the numerical sequence corresponding to each communication data is interpolated. The purpose of such processing is to ensure that the length is filled while the distribution of the original data in the numerical sequence is as little affected as possible, and to further enhance data security after filling additional data, which is convenient for subsequent encryption processing. The mean interpolation method is a common technique in the field of data processing, and the specific process will not be described again.

[0065] 202, construct a communication feature matrix of the data cluster, and determine a template feature vector of the data cluster.

[0066] Subsequently, the numerical sequence after interpolation processing of all communication data corresponding to the data points in each data cluster is used to construct a communication feature matrix of each data cluster. Specifically, the numerical sequence corresponding to the standard data of each data cluster is taken as an element in a row of the matrix, and each numerical sequence after interpolation processing is taken as an element in a row of the matrix. The obtained matrix is taken as the communication feature matrix.

[0067] Then, a plurality of feature vectors of the communication feature matrix and the eigenvalues of each feature vector are obtained by using a data dimension reduction technique. The feature vector corresponding to the maximum value of all eigenvalues is taken as the template feature vector of each data cluster.

[0068] The data dimension reduction technique is a common technique in the field of data processing, including but not limited to principal component analysis (PCA) and linear discriminant analysis (LDA). The data dimension reduction technique is not specially limited in the present application. Preferably, as an embodiment, the PCA is used to obtain the template feature vector of each data cluster.

[0069] Step S003: determining the regional difference degree of the maximum value in the neighborhood range based on the distribution of the elements on both sides of the maximum value in the distance sequence of each row element in the communication feature matrix; and determining the key sequence of each row element based on the regional difference degree.

[0070] Specifically, the absolute value of the difference between each element in each row of the communication feature matrix of each data cluster and each element in the template feature vector of each data cluster is calculated, and a sequence composed of the absolute values of all the differences corresponding to each element in the row is taken as a distance sequence A of each element in the row. The regional difference degree of each maximum value in the distance sequence A is determined according to the difference between adjacent elements in different neighborhood ranges.

[0071] Further, for any maximum value in the distance sequence A, the neighborhood elements in the neighborhood range n centered on the maximum value are obtained, that is, n adjacent elements on the left and right of the maximum value are taken, and 0 is supplemented if the number of elements on the left and right is less than n. The distribution difference of adjacent elements in different neighborhood ranges is analyzed, so as to evaluate the diffusion degree of the unstable feature of each maximum value in each neighborhood range. If the difference between adjacent elements increases as the neighborhood range increases, it indicates that there may be a large difference between the adjacent elements and the template feature vector of the data cluster, and the possibility that the neighborhood range determined by the adjacent elements belongs to the unimportant information interval of the row element in the communication feature matrix corresponding to the distance sequence A is greater. The value range of the neighborhood range n is M is the length of the distance sequence A, and round() is a down-rounding function.

[0072] The calculation formula of the regional difference degree of the pth maximum value in the distance sequence A under the neighborhood range n is:

[0073] U p,n (A) = sigmoid(Δp A,n ) - sigmoid(Δp A,n-1 )

[0074] In the formula, U p,n (A) is the regional difference degree of the pth maximum value in the distance sequence A under the neighborhood range n, sigmoid() is a sigmoid growth curve function, Δp A,n is the cumulative sum of all elements in the neighborhood range n of the pth maximum value in the distance sequence A, and Δp A,n-1 is the cumulative sum of all elements in the neighborhood range n-1 of the pth maximum value in the distance sequence A.

[0075] The greater the difference between the adjacent elements in the neighborhood range n and the neighborhood range n-1 of the pth maximum value and the elements in the template feature vector, the greater the difference between the elements in the communication feature matrix corresponding to the adjacent elements on the left and right of the pth maximum value and the template feature vector of the data cluster, the greater the deviation of the data distribution of the communication feature matrix as a whole, and the greater the value of U p,n (A). The greater the difference between the elements in the neighborhood range n of the pth maximum value in the distance sequence A and the elements in the template feature vector.

[0076] Further, the region difference degree of each maximum value in the same neighborhood range is obtained respectively, and the first quartile of the region difference degrees of all maximum values in the same neighborhood range is calculated as the decision threshold of the neighborhood range. The key sequence of each row element in the communication feature matrix is determined based on the decision threshold, and the acquisition process of the key sequence is as follows:

[0077] T1: The region difference degree of each maximum value in each neighborhood range is calculated. If the region difference degree of a certain maximum value in the neighborhood range n is less than the decision threshold of the neighborhood range, it indicates that there is a small difference between the data in the communication feature matrix corresponding to the elements adjacent to the maximum value and the elements in the template feature vector of the data cluster, and the data in the communication feature matrix may be data existing in multiple interpolated numerical sequences. Then, n = n + 1 is set, the region difference degree of the maximum value in the neighborhood range n + 1 is calculated, and the iteration is continued within the value range of n, until the region difference degree in the neighborhood range is greater than the decision threshold of the neighborhood range. At this time, it indicates that there is a large difference between the data in the communication feature matrix corresponding to the elements adjacent to the maximum value and the elements in the template feature vector of the data cluster, and the data in the communication feature matrix may be data with high privacy and low repetition in each communication data, which has obvious difference with the common data corresponding to the repeated content in multiple communication data.

[0078] T2: When the region difference degree of each maximum value in the neighborhood range is greater than the decision threshold of the neighborhood range, the set of all elements in the neighborhood range of each maximum value is taken as the screening region set of the distance sequence where the maximum value is located. The sequence composed of all screening region sets in each distance sequence A in the order from small to large according to the order value of the first element in the set is taken as the key sequence C of the corresponding row elements in the communication feature matrix.

[0079] T3: The key sequence of each row element in the communication feature matrix is obtained by repeating the steps T1-T2.

[0080] Step S004: The offset coefficient of each row element is obtained according to the key sequence of each row element in the communication feature matrix.

[0081] The key sequence of each row element in the communication feature matrix is determined based on the difference between each row element and the common data features of all row elements, i.e., the template feature vector. Before the communication data is encrypted, the distribution characteristics of the key sequence of each row element and the aggregation degree of the data with high privacy and low repetition in the communication data corresponding to each row element are determined based on the distance sequence A of each row element, so as to obtain the offset coefficient of each row element, which facilitates the subsequent determination of the shift of the numerical sequence corresponding to each row element when generating the key.

[0082] Specifically, the calculation formula for the offset coefficient of the i-th row element is:

[0083] q i =count(A i )*l(C i )

[0084] Where q i is the offset coefficient of the i-th row element, A i is the distance sequence of the elements in row i, count(A i ) is the number of maximum values ​​in the distance sequence of the elements in the i-th row, C i is the key sequence of the elements in row i, l(C i ) is the kurtosis of the key sequence of the i-th row element.

[0085] Among them, the more maximum values ​​there are, the more personalized important data there are in the elements of the i-th row. The larger the kurtosis of the key sequence, the more concentrated the important data is, and the more it is necessary to adjust the distribution of the key to achieve enhanced encryption effect.

[0086] Step S005: using the logistics transformation encryption technology to determine the encryption key of each data in each row element based on the offset coefficient of each row element, and completing the encryption of the communication data based on the encryption key.

[0087] According to the above steps, the offset coefficients of each row element in the communication feature matrix are calculated respectively. Furthermore, the offset coefficients of each row element in the communication feature matrix and the first eigenvalue of the communication data corresponding to each row element are combined to complete the encryption of the communication data using the logistics transformation encryption technology. The specific process is as follows:

[0088] First, the normalized result of the first eigenvalue of the communication data corresponding to each row element is used as the initial key for encrypting each row element, and iteration is performed based on the initial key. Normalization is a common technique in the field of data processing, and includes but is not limited to Min-Max normalization and Z-score standardization. Preferably, as an embodiment, Min-Max normalization is used to determine the initial key for encrypting each row element.

[0089] Secondly, the iterative formula of the initial key is determined by using the logistics transformation encryption technology, and the initial key for encrypting the elements in the i-th row is recorded as V 0,i , the iterative formula for m iterations is:

[0090] V m,i =μ*V m-1,i ×(1-V m-1,i )

[0091] In the formula, V m,i is the initial key V 0,i is the result of iteration m times, μ is a preset parameter in the logistics transformation encryption technology, and the size of μ is an empirical value of 4, V m-1,i is the initial key v 0,i is the result of iteration m-1 times.

[0092] Then, according to the above process, the initial key V 0,i is calculated in turn, and each time the result of iteration is used as the iteration key of each data in the ith row element. The corresponding relationship is that the result of iteration 1 times of the initial key V 0,i is used as the iteration key of the first data in the ith row element, the result of iteration m times of the initial key V 0,i is used as the iteration key of the mth data in the ith row element, and so on. The iteration keys of all elements in the ith row element are arranged in the order of element positions to obtain the iteration key sequence of the ith row element.

[0093] Subsequently, the encryption key of each data in the ith row element is obtained in combination with the offset coefficient of the ith row element and the iteration key sequence of the ith row element. The encryption key of the mth data in the ith row element is obtained in the following manner:

[0094]

[0095] In the formula, key i,m is the encryption key of the mth data in the ith row element, bin is a binary conversion operator, 256 represents the base of a binary number, V m,i is the iteration key of the mth data in the ith row element, >> is a binary right shift, length(i) is the number of data in the ith row element, mod is a remainder operation, q i is the offset coefficient of the ith row element, is a rounding up operation.

[0096] Finally, the numerical sequence corresponding to the ith row element is encrypted based on the encryption key of each data in the ith row element to obtain ciphertext.

[0097] The mth data is encrypted using the encryption key of the mth data in the ith row element to obtain the ciphertext of the mth data:

[0098] IM i,m = bin(Z i,m ) xor key i,m

[0099] In the formula, IM i,m is the ciphertext of the mth data in the ith row element, Z i,mis the mth data in the ith row element, xor is an exclusive or operator, key i,m is the encryption key of the mth data in the ith row element, bin is a binary conversion operator.

[0100] Further, according to the above steps, the ciphertext of each data in each row element in the communication feature matrix is obtained respectively, and the ciphertext of all data is saved to a database or a hardware device, thereby completing the encryption of the communication data.

[0101] It should be noted that the above-described embodiments are only used to illustrate the technical solutions of the present application, but not limit the same; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that the technical solutions recorded in the foregoing embodiments can still be modified, or some technical features can be replaced by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

[0102] Each of the embodiments in the specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other. Each embodiment mainly describes the difference from other embodiments.

Claims

1. A data security storage method under 5G network security management, characterized in that: The method comprises the following steps: Collect communication data in the 5G network and determine the data to be encrypted; Clustering the communication data to obtain multiple data clusters, interpolating the numerical sequence based on the length of the communication data corresponding to the data points in each data cluster to obtain a communication feature matrix, and using dimensionality reduction technology to determine the template feature vector of the communication feature matrix; Determining the degree of regional difference of the maximum value within a neighborhood range based on the distribution of elements on both sides of the maximum value in the distance sequence of each row element in the communication feature matrix; determining the key sequence of each row element based on the regional difference degree; determining the offset coefficient of each row element according to the key sequence of each row element in the communication feature matrix; The encryption key of each data in each row element is determined based on the offset coefficient of each row element using the logistics transformation encryption technology; The communication data is encrypted based on the encryption key, and the ciphertext of all data is saved.

2. The data security storage method under 5G network security management according to claim 1 is characterized in that: The clustering process for the communication data to obtain multiple data clusters includes the following specific steps: Each communication data is converted into a decimal number using UTF-8 encoding conversion technology to obtain a numerical sequence corresponding to each communication data; The mean of all values ​​in the numerical sequence corresponding to each communication data is used as the first eigenvalue of each communication data; The number of values ​​in the numerical sequence corresponding to each communication data is used as the data length of each communication data, and a clustering space is constructed based on the length and the first eigenvalue as the horizontal and vertical coordinates; A data clustering algorithm is used to divide all communication data into multiple data clusters based on the first eigenvalue and the data points corresponding to the data length in the clustering space.

3. The data security storage method under 5G network security management according to claim 1 is characterized in that: The interpolation process of the numerical sequence based on the length of the communication data corresponding to the data points in each data cluster to obtain the communication feature matrix includes the following specific steps: For any data cluster, the communication data with the largest data length among the communication data corresponding to the data points in each data cluster is used as the standard data of each data cluster; For communication data corresponding to other data points other than the data points corresponding to the standard data, the numerical sequences corresponding to the other data points are interpolated and expanded to reach the data length of the standard data by using the mean filling method; The standard data of each data cluster and the interpolation results of the numerical sequence corresponding to the data points in each data cluster are used as a row element in the matrix to obtain the communication feature matrix of each data cluster.

4. The data security storage method under 5G network security management according to claim 3 is characterized in that: The specific steps of determining the template feature vector of the communication feature matrix by using the dimensionality reduction technology are as follows: The data dimensionality reduction technology is used to obtain multiple eigenvectors of the communication feature matrix and the eigenvalues ​​of each eigenvector, and the eigenvector corresponding to the maximum value of all eigenvalues ​​is used as the template eigenvector of each data cluster.

5. The data security storage method under 5G network security management according to claim 1 is characterized in that: The determination method of the regional difference degree of the maximum value in the neighborhood range is: Calculate the absolute value of the difference between each row element in the communication feature matrix of each data cluster and the template feature vector of each data cluster in the same order, and use the sequence composed of the absolute values ​​of all the differences corresponding to each row element as the distance sequence of each row element; For any maximum value in the distance sequence, the first number of adjacent elements on the left and right of each maximum value are taken to form the first neighborhood range of each maximum value; Within the value range determined by the length of the distance sequence, the first quantity is iterated once with a step size of 1 to obtain the second quantity, and the second neighborhood range of each maximum value is formed by taking the second number of adjacent elements on the left and right of the center point; The mapping result of the sigmoid function on the cumulative sum of all elements in the second neighborhood of each maximum value and the mapping result of the sigmoid function on the cumulative sum of all elements in the first neighborhood of each maximum value are taken as the regional difference degree of each maximum value in the second neighborhood.

6. The data security storage method under 5G network security management according to claim 1 is characterized in that: The step of determining the key sequence of each row element based on the regional difference degree includes the following specific steps: t0: Obtain the regional difference degree of each maximum value in the same neighborhood range, and calculate the first quartile of the regional difference degree of all maximum values ​​in the same neighborhood range as the decision threshold of the neighborhood range; t1: Calculate the regional difference degree of each maximum value in each neighborhood range. If the regional difference degree of the maximum value in a neighborhood range is less than the decision threshold of this neighborhood range, iterate the neighborhood range until the regional difference degree of the maximum value in the neighborhood range is greater than the decision threshold of the neighborhood range; t2: When the regional difference of each maximum value in the neighborhood range is greater than the decision threshold of the neighborhood range, the set of all elements of each maximum value in the neighborhood range is used as a screening area set of the distance sequence where the maximum value is located; The sequence of all the screening area sets in each distance sequence in ascending order of the first element sequence value is used as the key sequence of the elements corresponding to a row in the communication feature matrix of each distance sequence; t3: Determine the maximum value in the distance sequence of each row element in the communication feature matrix, repeat the above steps t0-t2, and obtain the key sequence of each row element in the communication feature matrix respectively.

7. The data security storage method under 5G network security management according to claim 1 is characterized in that: The offset coefficient of each row element is determined as follows: The product of the number of maximum values ​​in the distance sequence of each row of elements and the kurtosis of the key sequence of each row of elements is used as the offset coefficient of each row of elements.

8. The data security storage method under 5G network security management according to claim 1, characterized in that: The encryption key for each data in each row of elements is determined as follows: Determine an iterative key sequence for each row element based on a first characteristic value of communication data corresponding to each row element; and determine an encryption key for each data in each row element based on the iterative key sequence: In the formula, key i,m is the encryption key for the mth data in the i-th row, bin is the binary conversion operator, 256 is the base of the binary number, V m,i is the iterative key for the mth data in the i-th row, >> is a binary right shift, length(i) is the number of data in the i-th row, mod is the remainder operation, q i is the offset coefficient of the i-th row element, The symbol for rounding up.

9. The data security storage method under 5G network security management according to claim 8, characterized in that: The iterative key sequence of each row of elements is determined as follows: Arrange the iteration keys of all elements in each row of elements in order of element positions to obtain an iteration key sequence for each row of elements.

10. The data security storage method under 5G network security management according to claim 1, characterized in that: The encryption of the communication data based on the encryption key includes the following specific formula: IM i,m =bin(Z i,m )xor key i,m Among them, IM i,m is the ciphertext of the mth data in the i-th row element, Z i,m is the mth data in the i-th row element, xor is the exclusive OR operator, key i,m is the encryption key for the mth data in the i-th row, and bin is the binary conversion operator.

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