Secure transmission method for 5g communication technology

Abstract

The application discloses a kind of security transmission methods for 5G communication technology, comprising: the sensitivity evaluation of the data to be transmitted is carried out, feature extraction, classification mark and data block adjustment, and data block set is generated;Combined with key exchange protocol to generate key, according to the characteristics of key Suitable encryption algorithm is selected to encrypt data block, form encrypted data block set;Through preliminary authentication, hash chain check in transmission process and integrity verification at the end of transmission, the security verification of identity and data is realized, and the encrypted data block authenticated is output;Homomorphic encryption processing or data mask protection is carried out to obtain encrypted protected data stream, and transmission is carried out;Real-time monitoring is carried out to the transmission protected data stream, and the flow and abnormal score are predicted through the flow anomaly identification model based on LSTM, and whether there is abnormality is judged based on the set adaptive risk threshold to carry out tracing and attack blocking.

Description

Technical Field

[0001] This invention relates to the technical field of communication information security, and in particular to a secure transmission method for 5G communication technology. Background Technology

[0002] With the rapid development of 5G communication technology, the high bandwidth, low latency and massive connectivity of the network have made 5G an important driving force for the digital transformation of various industries. However, the widespread application of 5G technology is also accompanied by many challenges in information security.

[0003] Currently, traditional transmission security technologies mainly rely on data encryption and security protocol constraints during transmission. This approach can prevent common data theft and tampering to a certain extent. However, facing increasingly complex network attack methods, such as man-in-the-middle attacks, side-channel attacks, and decryption threats from quantum computing, existing secure transmission methods have certain shortcomings in ensuring data integrity and privacy. In addition, due to the multi-layered and heterogeneous nature of 5G network architecture, data security verification and authentication during transmission also face higher challenges. Especially under the real-time requirements of data transmission, existing security strategies are difficult to balance security and efficiency, often resulting in excessively complex encryption algorithms or excessively long transmission authentication times, affecting the overall performance of data transmission.

[0004] In existing technologies, most encryption methods fail to effectively combine the sensitivity and characteristic information of data for flexible encryption and classification protection. Instead, they adopt a uniform encryption strategy. This not only leads to overprotection of highly sensitive data and security risks to low-sensitivity data, but also increases the computational burden and data recovery difficulty during decryption. In addition, anomaly detection and threat identification during transmission mainly rely on preset rules or fixed algorithms, making it difficult to respond flexibly to rapidly changing attack patterns. Therefore, existing technologies are inadequate in terms of data sensitivity assessment, flexibility of transmission encryption, reliability of authentication and verification mechanisms, and real-time monitoring of the transmission process. Summary of the Invention

[0005] The purpose of this section is to outline some aspects of the embodiments of the present invention and to briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section, as well as in the abstract and title of the present application, to avoid obscuring the purpose of this section, the abstract and title of the invention. Such simplifications or omissions shall not be used to limit the scope of the present invention.

[0006] In view of the aforementioned existing problems, the present invention is proposed.

[0007] Therefore, the technical problem solved by this invention is the lack of data sensitivity assessment, poor encryption flexibility, and low reliability of transmission authentication in existing 5G transmission processes.

[0008] To solve the above-mentioned technical problems, the present invention provides the following technical solution: performing sensitivity assessment, feature extraction, classification labeling, and data block adjustment on the data to be transmitted, generating a data block set containing sensitivity identifiers and feature labels;

[0009] A key is generated by combining a key exchange protocol, and an appropriate encryption algorithm is selected based on the key characteristics to encrypt the data blocks in the data block set, thus forming an encrypted data block set;

[0010] By performing preliminary authentication on the encrypted data block set, hash chain verification during transmission, and integrity verification at the end of transmission, secure verification of identity and data is achieved, and authenticated encrypted data blocks are output.

[0011] The authenticated encrypted data block is subjected to homomorphic encryption or data mask protection to obtain an encrypted protected data stream, which is then transmitted.

[0012] The transmitted protected data stream is monitored in real time. Traffic and anomaly scores are predicted using an LSTM-based traffic anomaly identification model. Anomalies are determined based on a set adaptive risk threshold to facilitate source tracing and attack blocking.

[0013] As a preferred embodiment of the secure transmission method for 5G communication technology described in this invention, generating the data block set includes:

[0014] The system receives data streams to be transmitted from a 5G network, performs sensitivity analysis on the data to be transmitted using a sensitivity assessment model, outputs a sensitivity score, and classifies the data to be transmitted into three levels: high sensitivity, medium sensitivity, and low sensitivity based on the sensitivity score.

[0015] An algorithm based on term frequency-inverse document frequency is used to extract keywords from the data content of the data to be transmitted, and the extracted keywords are used as data features;

[0016] Based on sensitivity level and network status, the size of the data blocks to be transmitted is adjusted using a priority dynamic block adjustment algorithm, and the adjusted data block set (Adjusted) is output. D ata B Locks contain sensitivity indicators, feature keywords, and adjusted data blocks.

[0017] In a preferred embodiment of the secure transmission method for 5G communication technology described in this invention, the sensitivity score ranges from 0 to 1, wherein:

[0018] A sensitivity score S > 0.7 indicates high sensitivity.

[0019] When 0.4 ≤ sensitivity score S ≤ 0.7, it is considered medium sensitivity;

[0020] When the sensitivity score S < 0.4, it is considered low sensitivity.

[0021] As a preferred embodiment of the secure transmission method for 5G communication technology described in this invention, the encrypted data block set is formed, comprising:

[0022] Generate key pairs using key exchange protocols (K A ,K B The key is then encrypted and enhanced using an elliptic curve cryptography algorithm.

[0023] Select the optimal encryption algorithm based on the sensitivity level and key features of the data block;

[0024] Specifically, high-sensitivity data blocks are encrypted using the AES-256-GCM algorithm, medium-sensitivity data blocks using the ChaCha20 algorithm, and low-sensitivity data blocks using the AES-128 encryption algorithm. The output is a set of encrypted data blocks. D ata B Locks, each data block contains a corresponding key and encryption algorithm identifier.

[0025] As a preferred embodiment of the secure transmission method for 5G communication technology described in this invention, the step of encrypting and enhancing the key using an elliptic curve cryptography algorithm includes:

[0026] Generate public keys for both parties (P) A Q A ) and private key pair (P B Q B ( ), where the two parties are the transmitting end and the receiving end;

[0027] Both parties generate a shared key K through public key exchange. shared ,in,

[0028] The shared key K is encrypted using an elliptic curve cryptography algorithm. shared Encryption is performed to form the final key K, and the output is "Encrypted". K ey.

[0029] As a preferred embodiment of the secure transmission method for 5G communication technology described in this invention, the output of authenticated encrypted data blocks includes:

[0030] Before data transmission, the identities of both parties are authenticated using an elliptic curve digital signature algorithm, and each party generates a signature Sig. A and Sig B The system verifies the other party's public key. If the verification fails, the system immediately terminates the transmission and triggers an alarm.

[0031] During transmission, a hash value H is calculated for each data block, forming a hash chain;

[0032] After transmission is completed, the receiving end verifies the hash chain. If the hash chain is broken, the data packet is identified as having been tampered with.

[0033] The integrity of the transmitted data is then verified using the digital certificate of the receiving end. After verification, the verified encrypted data block is output.

[0034] As a preferred embodiment of the secure transmission method for 5G communication technology described in this invention, obtaining the encrypted protected data stream includes:

[0035] For highly sensitive data blocks, the Paillier homomorphic encryption algorithm is used for encryption, resulting in a homomorphically encrypted protected data stream. E ncrypted B locks;

[0036] For moderately sensitive data, data masking technology is used to replace sensitive fields, resulting in a data stream protected by a data mask. D ata B locks;

[0037] For low-sensitivity data, the AES-128 algorithm is used for encryption. The calculation method is as follows:

[0038] C L =AES128_Encrypt(K AES128 D L )

[0039] Among them, C L This indicates the encrypted data block; AES128_Encrypt indicates the AES-128 encryption operation.

[0040] As a preferred embodiment of the secure transmission method for 5G communication technology described in this invention, a data flow anomaly identification model is constructed based on a Long Short-Term Memory (LSTM) network algorithm to monitor the privacy-protected data flow in real time. The LSM network algorithm identifies abnormal traffic by learning the time-series changes of data flow, and the output of the data flow anomaly identification model is an anomaly score E for the traffic. s core;

[0041] Based on the statistical characteristics of historical traffic data, including the mean and standard deviation, an adaptive risk threshold T is set. risk The calculation formula is:

[0042] T risk =μ+k·σ

[0043] Where k is the sensitivity coefficient, ranging from 2 to 3, μ is the average historical flow rate, and σ is the standard deviation of the flow rate.

[0044] As a preferred embodiment of the secure transmission method for 5G communication technology described in this invention, the anomaly score E for the current time period is... s core and adaptive risk threshold T risk A comparison is made, in which:

[0045] If E s core>T risk If so, it is considered that abnormal traffic has been detected, triggering an abnormal alarm;

[0046] If E s core≤T risk If the traffic is normal, no alarm needs to be triggered.

[0047] As a preferred embodiment of the secure transmission method for 5G communication technology described in this invention, after an alarm is triggered, a source tracing algorithm is used to analyze the abnormal traffic, and the source path P is traced... a Identify the source of the attack, including:

[0048] The abnormal traffic is subjected to feature analysis to generate a feature vector V. a ;

[0049] The feature vector V is determined by similarity matching. a The closest path P a ;

[0050] For the path P a Perform a blocking operation and record the attack source information.

[0051] The beneficial effects of this invention are:

[0052] 1. Improve the targeting and flexibility of data encryption, reduce the encryption burden, and enhance data transmission efficiency and security;

[0053] 2. Improved data security protection level, optimized encryption algorithm efficiency in different scenarios, ensured data confidentiality, improved system flexibility and transmission efficiency;

[0054] 3. Through multi-layered authentication and verification steps, it can effectively prevent security threats such as man-in-the-middle attacks and data tampering, realize dual verification of identity and data, enhance the security of data transmission, and improve the overall reliability of the system.

[0055] 4. By combining homomorphic encryption and masking protection, dual encryption protection for sensitive data is achieved, effectively preventing the risks of external theft and internal leakage, and improving the security of data transmission.

[0056] 5. It enhances dynamic security during data transmission, and can respond to and deal with possible attacks in a timely manner, thereby improving the overall security and defense capabilities of data transmission. Attached Figure Description

[0057] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein:

[0058] Figure 1 This is a flowchart illustrating the secure transmission method for 5G communication technology according to the present invention.

[0059] Figure 2 This is a schematic diagram illustrating the data block size relationship of data with different sensitivities as shown in this invention;

[0060] Figure 3 This is a schematic diagram of the sensitivity scoring curves for each data point presented in this invention. Detailed Implementation

[0061] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0062] Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without inventive effort should fall within the scope of protection of this invention.

[0063] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0064] According to an embodiment of the present invention, in combination Figure 1The flowchart shown illustrates a secure transmission method for 5G communication technology, which specifically includes the following steps:

[0065] S1. Perform sensitivity assessment, feature extraction, classification labeling, and data block adjustment on the data to be transmitted, generating a data block set containing sensitivity identifiers and feature labels. It should be noted that the generated data block set includes:

[0066] It receives data streams to be transmitted from a 5G network, performs sensitivity analysis on the data to be transmitted using a sensitivity assessment model, outputs a sensitivity score, and classifies the data to be transmitted into three levels: high sensitivity, medium sensitivity, and low sensitivity based on the sensitivity score.

[0067] As an example, the formula for calculating the output data sensitivity score S is:

[0068] S=α·C+β·P+γ·V

[0069] Where C represents the confidentiality of the data, P represents the priority of the data, V represents the risk of exposure of sensitive content of the data, and α, β, and γ are weighting coefficients;

[0070] Furthermore, the sensitivity score ranges from 0 to 1, where:

[0071] A sensitivity score S > 0.7 indicates high sensitivity.

[0072] When 0.4 ≤ sensitivity score S ≤ 0.7, it is considered medium sensitivity;

[0073] When the sensitivity score S < 0.4, it is considered low sensitivity;

[0074] An algorithm based on term frequency-inverse document frequency is used to extract keywords from the data content of the data to be transmitted, and the extracted keywords are used as data features;

[0075] The term frequency (tf) and inverse document frequency (idf) of each data packet Di are calculated using the following formulas:

[0076]

[0077] Where w is the feature word, N is the total number of data packets, and df(w) is the number of data packets containing w;

[0078] Select the top k keywords with the highest tf_idf scores as data features for output;

[0079] Data features and sensitivity levels will be labeled together in the data packet, and the output will be a set of labeled data packets, Data. B locks serve as input for subsequent steps;

[0080] Based on sensitivity level and network status, a priority-based dynamic block adjustment algorithm is used to adjust the size of data blocks to be transmitted, outputting an adjusted set of data blocks. D ata B locjs contains sensitivity indicators, feature keywords, and adjusted data blocks.

[0081] S2. Generate a key using the key exchange protocol, and select an appropriate encryption algorithm based on the key characteristics to encrypt the data blocks in the data block set, forming an encrypted data block set. Note that the following points should be noted in this step:

[0082] Generate key pairs using key exchange protocols (K A ,K B The key is then encrypted and enhanced using an elliptic curve cryptography algorithm.

[0083] Encryption enhancement of the key is achieved through elliptic curve cryptography, including:

[0084] Generate public keys for both parties (P) A Q A ) and private key pair (P B Q B (where the two parties are the transmitting end and the receiving end);

[0085] Both parties generate a shared key K through public key exchange. shared ,in,

[0086] The shared key K is encrypted using the elliptic curve cryptography algorithm. shared Encryption is performed to form the final key K, and the output is "Encrypted". K ey;

[0087] Select the optimal encryption algorithm based on the sensitivity level and key features of the data block;

[0088] Specifically, high-sensitivity data blocks are encrypted using the AES-256-GCM algorithm, medium-sensitivity data blocks using the ChaCha20 algorithm, and low-sensitivity data blocks using the AES-128 encryption algorithm. The output is a set of encrypted data blocks. D ata B Locks, each data block contains a corresponding key and encryption algorithm identifier.

[0089] S3. By performing preliminary authentication of the encrypted data block set, hash chain verification during transmission, and integrity verification at the end of transmission, secure authentication of identity and data is achieved, and authenticated encrypted data blocks are output. It should be noted in this step that the output authenticated encrypted data blocks include:

[0090] Before data transmission, the identities of both parties are authenticated using an elliptic curve digital signature algorithm, and each party generates a signature Sig. A and Sig B Both parties verify each other's signatures using their respective public keys:

[0091] Verify(Sig,PK)=H(Data)=R+S

[0092] Where Sig represents the signature result, PK is the public key, H is the hash function, H(Data) is the result of hashing the data to be signed, R is a signature parameter, representing a randomly generated number, which is an intermediate result of the signature algorithm, and S is another important parameter of ECDSA signature, which is associated with the private key d and R and is used to generate the signature.

[0093] If verification fails, the system immediately stops the transmission and triggers an alarm;

[0094] If the verification passes, the data transmission verification process begins. During transmission, a hash value G(D) is calculated for each data block. n ), forming a hash chain H chain :

[0095] H chain =H(H(D1)+H(D2)+…+H(D n ))

[0096] A hash chain forms a unique check value by adding up the hash values ​​on the chain.

[0097] After transmission is complete, the integrity of the transmitted data is verified using the receiving end's digital certificate. If the received H... chain H calculated by the sender chain If they match, the verification passes, and the verified encrypted data block is output.

[0098] If the hash chain breaks, i.e., is inconsistent, the data packet is identified as having been tampered with, the verification fails, the system records the error and blocks further transmission.

[0099] S4. Perform homomorphic encryption or data masking on the authenticated encrypted data block to obtain an encrypted protected data stream, and then transmit it. It should be noted in this step that the obtained encrypted protected data stream includes:

[0100] For highly sensitive data blocks, the Paillier homomorphic encryption algorithm is used for encryption, resulting in a homomorphically encrypted protected data stream. E ncrypted B locks;

[0101] For example, let plaintext data block m be defined, and its encryption form be E(m), satisfying the following property:

[0102] E(m1+m2)=E(m1)×E(m2);

[0103] For moderately sensitive data, data masking technology is used to replace sensitive fields with "******", resulting in a masked data stream. D ata B locks;

[0104] For low-sensitivity data, the AES-128 algorithm is used for encryption. The calculation method is as follows:

[0105] C L =AES128_Encrypt(K AES128 D L )

[0106] Among them, C L This indicates the encrypted data block; AES128_Encrypt indicates an AES-128 encryption operation.

[0107] S5. Real-time monitoring of the transmitted protected data stream; prediction of traffic and anomaly scoring using an LSTM-based traffic anomaly identification model; and determination of anomaly existence based on a set adaptive risk threshold for source tracing and attack blocking. Note that the following points should be noted in this step:

[0108] A data flow anomaly detection model is built based on the Long Short-Term Memory (LSTM) network algorithm to monitor privacy-preserving data flows in real time. The LSTM algorithm identifies abnormal traffic by learning the time-series changes of the data flow. The output of the data flow anomaly detection model is the predicted traffic X. pred and abnormal score E s core;

[0109] The privacy-protected data stream is segmented chronologically to form a time-series dataset X = x1, x2, ..., x n , where each x n This represents the data flow value for a time slice;

[0110] This model predicts future traffic values ​​by learning the time-series characteristics of historical traffic. The input to the LSTM is a time-series dataset X, where each data point x... n Representing the data flow over a period of time, the output of LSTM is the predicted flow rate X. pred And the anomaly score E_score, the formula for calculating the anomaly score E_score is:

[0111] E_score=|X t -X pred |

[0112] Where X is the time series dataset, i.e., the current actual traffic. pred To predict traffic, the anomaly score E_score represents the difference between the actual traffic and the predicted traffic;

[0113] Based on the statistical characteristics of historical traffic data, including the mean and standard deviation, an adaptive risk threshold T is set. risk The calculation formula is:

[0114] T risk =μ+k·σ

[0115] Where k is the sensitivity coefficient, ranging from 2 to 3, μ is the average historical flow rate, and σ is the standard deviation of the flow rate;

[0116] Adaptive risk threshold detection compares the anomaly score output by the model with a set risk threshold T. risk Determine whether to trigger an abnormal alarm;

[0117] The anomaly score E for the current time period s core and adaptive risk threshold T risk A comparison is made, in which:

[0118] If E s core>T risk If so, it is considered that abnormal traffic has been detected, triggering an abnormal alarm;

[0119] If E s core≤T risk If so, the traffic is considered normal and no alarm needs to be triggered;

[0120] After the alarm is triggered, a source tracing algorithm is used to analyze the abnormal traffic, tracing the source path P. a Identify the source of the attack, including:

[0121] Perform feature analysis on abnormal traffic to generate feature vector V a ;

[0122] The similarity matching is used to determine the relationship with feature vector V. a The closest path P a ;

[0123] For path P a Perform a blocking operation and record the attack source information.

[0124] As an example, a communication system might perform feature analysis on abnormal traffic, generating a feature vector V for the abnormal traffic.a The feature vector must contain at least the IP address, port number, packet size, and transmission rate of the abnormal traffic.

[0125] As an example, using the feature vector V a Match the path P with the known attack signature database in the system to find the closest path. a The formula for calculating path similarity is:

[0126]

[0127] Among them, V k For each feature vector in the attack feature library, Sim represents the cosine similarity between the vectors.

[0128] As an example, find the most similar path P a Afterwards, the system will pinpoint the exact location of the attack source, such as the attacker's IP address and data packet path.

[0129] As an example, the system blocks the source of the attack by closing the corresponding network ports and blocking the attacking IP address. At the same time, the system records detailed information about the attack for subsequent analysis and strategy adjustment.

[0130] It should be noted that the method of this invention achieves flexible encryption and classification protection of data by performing sensitivity assessment and feature extraction on the data to be transmitted. At the same time, it uses a key exchange protocol to generate keys and combines data block adjustment strategies to further improve the security and integrity verification of data transmission. Through multiple security measures such as preliminary authentication, hash chain verification during transmission, integrity verification at the end of transmission, and homomorphic encryption, the trustworthiness of identity and data is ensured. In addition, an adaptive anomaly detection algorithm is used to monitor the protected data stream in real time, effectively identifying potential threats and tracing and blocking attacks in a timely manner.

[0131] Preferably, to verify the effectiveness of the multi-layered data security transmission method proposed in this embodiment, a typical 5G network environment was selected as the experimental platform. The experimental environment includes a transmitting end and a receiving end, both configured with servers supporting elliptic curve cryptography and Long Short-Term Memory (LSTM) network models. Various types of data streams were used in the experiment, including financial transaction data. A User browsing history (Data) B News and Information (Data) C ), social media information (Data) D Location information (Data) E Sensor data F ) and system logs (Data) G ).

[0132] The data stream to be transmitted is preprocessed, and a sensitivity assessment model is used to score the sensitivity of each data packet. The calculation formula is as follows:

[0133] S=αC+βP+γV

[0134] Where C represents the confidentiality of the data, P represents the priority of the data, V represents the risk of exposure of sensitive content of the data, and α, β, γ are weighting coefficients. In this experiment, the weighting coefficients are set to α = 0.5, β = 0.3, and γ = 0.2.

[0135] Based on the calculations, the sensitivity scores for each data point are obtained: Data A The value is 0.85, Data B The value is 0.65, Data C For 0.30, Data D The value is 0.55, Data E The value is 0.90, Data F The value is 0.40, Data G The score is 0.70; based on the rating, the Data... A and Data E Data is categorized as highly sensitive. B Data D and Data G Data is categorized as medium sensitivity data. C and Data F Classified as low-sensitivity data;

[0136] An algorithm based on term frequency-inverse document frequency (TF-IDF) is used to extract keywords from the data content. For each data packet D... i The term frequency (TF) and inverse document frequency (IDF) are calculated using the following formulas:

[0137]

[0138] Where w is the feature word, N is the total number of data packets, and df(w) is the number of data packets containing w. The top k keywords with the highest scores are selected as the data features output, for example, Data A Five keywords were extracted, including "account", "password", and "transaction amount". B Four keywords were extracted: Data C Three keywords were extracted, and so on;

[0139] Based on the sensitivity level and network status, the data block size is adjusted using a priority dynamic block adjustment algorithm. High-sensitivity data blocks are adjusted to 512 bytes, medium-sensitivity data blocks to 1024 bytes, and low-sensitivity data blocks to 2048 bytes. This adjustment ensures the security of high-sensitivity data and the transmission efficiency of low-sensitivity data.

[0140] During the key generation phase, the transmitting and receiving ends generate key pairs using a key exchange protocol and enhance the keys with elliptic curve cryptography. Both parties generate their own public and private keys, and generate a shared key K through public key exchange. shared And further encrypt it to form the final key K;

[0141] Based on the sensitivity level of the data block, select an appropriate encryption algorithm. High-sensitivity data (Data...) A Data E The AES-256-GCM algorithm is used for medium-sensitivity data (Data). B Data D Data G The ChaCha20 algorithm is used for low-sensitivity data. C Data F Using the AES-128 algorithm, each data block in the encrypted data block set contains a corresponding key and encryption algorithm identifier;

[0142] Before data transmission, the identities of both parties are authenticated using an elliptic curve digital signature algorithm to generate a signature Sig. The signature of the other party is verified using their respective public keys. If the verification is successful, the data transmission process begins; otherwise, the transmission is stopped and an alarm is triggered.

[0143] During transmission, a hash value is calculated for each data block, forming a hash chain:

[0144] H chain =H(H(D1)+H(D2)++H(D n ))

[0145] After the transmission is completed, the integrity of the transmitted data is verified by the digital certificate of the receiving end to ensure that the data has not been tampered with;

[0146] For highly sensitive data blocks (Data A Data E Furthermore, the Paillier homomorphic encryption algorithm is used for encryption, satisfying the homomorphic property:

[0147] E(m1)×E(m2)=E(m1+m2)

[0148] Medium sensitivity data (Data) BData D Data G Data masking technology is used to replace sensitive fields with "******", while low-sensitivity data (Data...) is masked. C Data F Continue to use the AES-128 algorithm for encryption;

[0149] During data transmission, an LSTM-based traffic anomaly detection model is used to monitor the data stream in real time. The privacy-protected data stream is segmented according to time sequence to form a time-series dataset, which is then input into the LSTM model to predict future traffic values. It also calculates anomaly scores and sets adaptive risk thresholds based on historical traffic data;

[0150] If the abnormal score exceeds the threshold, an abnormal alarm is triggered, and a source tracing algorithm is used to locate and block the attack source.

[0151] The experimental results are shown in the table below:

[0152] Table 1. Experimental Data Table for Multi-Level Data Security Transmission

[0153]

[0154]

[0155] Referring to Table 1, it can be clearly seen that the method of the present invention reflects an accurate assessment of the sensitivity of data through sensitivity scoring. For example, Data A and Data E The scores were 0.85 and 0.90 respectively, both correctly identified as highly sensitive data. In contrast, Data C and Data F The scores were low, at 0.30 and 0.40 respectively, and the data was classified as low-sensitivity data. This accurate classification helps to implement targeted encryption and protection measures in subsequent steps.

[0156] Preferably, to visually demonstrate the transmission of data with different sensitivities, the simulation experiment used MATLAB to generate a diagram illustrating the relationship between sensitivity scores and data block size, such as... Figure 2 As shown in the figure, different types of lines are used to represent the data transmission trends of different sensitivity levels. Solid lines represent high-sensitivity data, dashed lines represent medium-sensitivity data, and dotted lines represent low-sensitivity data.

[0157] Reference Figure 2 It can be seen intuitively that highly sensitive data (Data) A Data EThe corresponding block size is relatively small, at 512 bytes, which is beneficial for improving security. This is suitable for medium-sensitivity data. B Data D Data G The block size is 1024 bytes, balancing security and efficiency, and suitable for low-sensitivity data. C Data F The larger block size of 2048 bytes improves transmission efficiency. This dynamic adjustment strategy of data block size enables the system to achieve a good balance between security and performance.

[0158] Preferably, to further demonstrate the effectiveness of sensitivity scoring in data classification, sensitivity score curves for each data point were generated based on the experimental data in Table 1, such as... Figure 3 As shown.

[0159] Reference Figure 3 This allows you to visually see the distribution of sensitivity scores for each data point, such as Data. A Data E The sensitivity scores were 0.85 and 0.90, respectively, which are located at a high position in the curve graph, indicating high sensitivity; Data B Data D Data G The sensitivity scores were 0.65, 0.55, and 0.70, respectively, located in the middle region of the curve, indicating that these data have a moderate level of sensitivity; C Data F The sensitivity scores were low, at 0.30 and 0.40 respectively, located in the lower part of the curve, which validates the rationale for classifying them as low-sensitivity data.

[0160] The graph clearly shows the sensitivity scores of each data point, verifying the effectiveness of the sensitivity assessment model. The scores of high-sensitivity data are significantly higher than those of medium and low-sensitivity data, supporting the accuracy of data classification. The sensitivity score graph intuitively demonstrates the advantages of this invention in data sensitivity assessment. Compared with existing technologies, it can accurately assess the sensitivity of data, which helps to achieve refined security management.

[0161] Ideally, all data anomaly scores are below the set threshold (assuming the threshold is 0.1), and no alarm is triggered, indicating good system stability.

[0162] Compared with traditional methods, this invention has achieved significant improvements in data security and transmission efficiency. For example, in terms of data block size adjustment, the method of this invention can dynamically adjust according to the sensitivity level, which improves the security of high-sensitivity data and the transmission efficiency of low-sensitivity data. Moreover, the LSTM-based traffic anomaly identification model effectively monitors the data transmission process without false alarms or missed alarms, thus providing a guarantee for secure data transmission in the 5G network environment.

[0163] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A secure transmission method for 5G communication technology, characterized in that, include: The system performs sensitivity assessment, feature extraction, classification and labeling, and data block adjustment on the data to be transmitted, generating a set of data blocks containing sensitivity identifiers and feature labels. The sensitivity score is obtained by weighting the data confidentiality, data priority, and risk of sensitive content exposure. The top k keywords with the highest word frequency-inverse document frequency scores are selected as data features. These data features and the sensitivity level are labeled together in the data packet as input for subsequent processing. The data block size is adjusted according to the sensitivity level and network status, so that the data block corresponding to high-sensitivity data is smaller than the data block corresponding to medium-sensitivity data, and the data block corresponding to medium-sensitivity data is smaller than the data block corresponding to low-sensitivity data. A key is generated by combining a key exchange protocol, and an appropriate encryption algorithm is selected based on the key characteristics to encrypt the data blocks in the data block set, thus forming an encrypted data block set; The encryption algorithm is selected based on the sensitivity level and key features of the data block, and each encrypted data block contains a corresponding key identifier and encryption algorithm identifier. By performing preliminary authentication on the encrypted data block set, hash chain verification during transmission, and integrity verification at the end of transmission, secure verification of identity and data is achieved, and authenticated encrypted data blocks are output. During transmission, the hash value of each data block is calculated separately. The hash values ​​of each data block are combined and hashed again to generate a unique check value. After transmission, the unique check value obtained by the receiving end is compared with the unique check value calculated by the sending end. If the two are inconsistent, the data packet is recorded as tampered and transmission is prevented from continuing. The authenticated encrypted data block is subjected to homomorphic encryption or data mask protection to obtain an encrypted protected data stream, which is then transmitted. The transmitted protected data stream is monitored in real time. The traffic and anomaly scores are predicted by an LSTM-based traffic anomaly identification model. Anomalies are determined based on a set adaptive risk threshold to trace the source and block attacks. Specifically, the privacy-protected data stream is segmented into a traffic time series according to time sequence. The absolute value of the difference between the current actual traffic and the predicted traffic is used as the anomaly score. An adaptive risk threshold is generated based on the mean, standard deviation, and sensitivity coefficient of historical traffic. When the anomaly score exceeds the adaptive risk threshold, an abnormal traffic feature vector containing at least IP address, port number, packet size, and transmission rate is generated. The abnormal traffic feature vector is then matched with feature vectors in a known attack feature library using cosine similarity to locate the attack source. The corresponding network port is then closed and the attacking IP address is blocked.

2. The secure transmission method for 5G communication technology according to claim 1, characterized in that, Generating the data block set includes: The system receives data streams to be transmitted from a 5G network, performs sensitivity analysis on the data to be transmitted using a sensitivity assessment model, outputs a sensitivity score, and classifies the data to be transmitted into three levels: high sensitivity, medium sensitivity, and low sensitivity based on the sensitivity score. An algorithm based on term frequency-inverse document frequency is used to extract keywords from the data content of the data to be transmitted, and the extracted keywords are used as data features; Based on the sensitivity level and network status, the size of the data blocks to be transmitted is adjusted using a priority dynamic block adjustment algorithm, and the adjusted data block set is output. It includes sensitivity indicators, feature keywords, and adjusted data blocks.

3. The secure transmission method for 5G communication technology according to claim 2, characterized in that, The sensitivity score ranges from 0 to 1, where: When sensitivity score At that time, it is highly sensitive; when At that time, the sensitivity was medium. When sensitivity score At that time, the sensitivity was low.

4. The secure transmission method for 5G communication technology according to claim 1, characterized in that, The set of encrypted data blocks includes: Generate key pairs using key exchange protocols Furthermore, the key is encrypted and enhanced using an elliptic curve cryptography algorithm; Select the optimal encryption algorithm based on the sensitivity level and key features of the data block; Specifically, high-sensitivity data blocks are encrypted using the AES-256-GCM algorithm, medium-sensitivity data blocks using the ChaCha20 algorithm, and low-sensitivity data blocks using the AES-128 encryption algorithm. The resulting set of encrypted data blocks is then output. Each data block contains a corresponding key and encryption algorithm identifier.

5. The secure transmission method for 5G communication technology according to claim 4, characterized in that, The encryption enhancement of the key using the elliptic curve cryptography algorithm includes: Generate public keys for both parties. and private key pair Wherein, the two parties are the transmitting end and the receiving end; Both parties generate a shared key through public key exchange. ,in, ; The shared key is encrypted using an elliptic curve cryptography algorithm. Encryption is performed to form the final key K, and the output is... .

6. The secure transmission method for 5G communication technology according to claim 1, characterized in that, The output of authenticated encrypted data blocks includes: Before data transmission, the identities of both parties are authenticated using an elliptic curve digital signature algorithm, and each party generates a signature. and The system verifies the other party's public key. If the verification fails, the system immediately terminates the transmission and triggers an alarm. During transmission, a hash value H is calculated for each data block, forming a hash chain; After transmission is completed, the receiving end verifies the hash chain. If the hash chain is broken, the data packet is identified as having been tampered with. The integrity of the transmitted data is then verified using the digital certificate of the receiving end. After verification, the verified encrypted data block is output.

7. The secure transmission method for 5G communication technology according to claim 1, characterized in that, The encrypted protected data stream includes: For highly sensitive data blocks, the Paillier homomorphic encryption algorithm is used for encryption to obtain the homomorphically encrypted protected data stream. ; For moderately sensitive data, data masking technology is used to replace sensitive fields, resulting in a data stream protected by the data mask. ; For low-sensitivity data, the AES-128 algorithm is used for encryption. The calculation method is as follows: in, This indicates the encrypted data block; AES128_Encrypt indicates the AES-128 encryption operation.

8. The secure transmission method for 5G communication technology according to claim 1 or 7, characterized in that, A data stream anomaly detection model is constructed based on a Long Short-Term Memory (LSTM) network algorithm to monitor privacy-preserving data streams in real time. The LSM algorithm identifies abnormal traffic by learning the time-series changes of the data flow, and the output of the data stream anomaly detection model is an anomaly score for the traffic. ; Based on the statistical characteristics of historical traffic data, including the mean and standard deviation, an adaptive risk threshold is set. The calculation formula is: Where k is the sensitivity coefficient, ranging from 2 to 3, μ is the average historical flow rate, and σ is the standard deviation of the flow rate.

9. The secure transmission method for 5G communication technology according to claim 8, characterized in that, Anomaly rating for the current time period With adaptive risk threshold A comparison is made, in which: if If so, it is considered that abnormal traffic has been detected, triggering an abnormal alarm; if If the traffic is normal, no alarm needs to be triggered.

10. The secure transmission method for 5G communication technology according to claim 9, characterized in that, After the alarm is triggered, a source tracing algorithm is used to analyze the abnormal traffic and trace the source path. Identify the source of the attack, including: Perform feature analysis on the abnormal traffic to generate feature vectors. ; The feature vector is determined by similarity matching. closest path ; For the path Perform a blocking operation and record the attack source information.

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