Wireless communication data decryption method based on dynamic key

Abstract

The invention relates to the technical field of wireless communication security, and discloses a wireless communication data decryption method based on a dynamic key, which comprises the following steps of: acquiring wireless channel characteristic data to obtain wireless channel characteristic information of two communication parties; constructing and normalizing a random matrix, and generating a normalized random matrix based on the acquired channel characteristic information; dynamic key generation: performing eigenvalue decomposition and optimization on the normalized random matrix to generate a dynamic key; dynamic key synchronization: realizing dynamic key synchronization of two communication parties by constructing a channel state Markov chain model; and ciphertext decryption: verifying the index consistency based on the generated dynamic key and decrypting the received ciphertext data, and the wireless channel characteristic data acquisition comprises wireless channel characteristic data acquisition. According to the invention, through wireless channel characteristic modeling, a random matrix theory and Markov chain optimization, high-randomness, high-synchronism and high-security dynamic key generation and decryption effects are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of wireless communication security, and in particular to a wireless communication data decryption method based on a dynamic key. Background Art

[0002] In modern wireless communications, with the rapid development of technologies such as the Internet of Things and the Internet of Vehicles, the importance of data security has become increasingly prominent. Data in wireless communication environments are vulnerable to attacks such as eavesdropping, tampering, and replay. Traditional static key mechanisms are difficult to meet the needs of dynamically changing network environments and are easily maliciously cracked or reused. Therefore, how to generate dynamic, highly random, and synchronized keys in complex and changeable wireless channels has become a key issue in ensuring the security of communication data.

[0003] At present, some solutions based on pseudo-random number generator (PRNG), pre-shared key (Pre-SharedKey) or third-party key negotiation have been proposed in the prior art. In these methods, a key consistent with both communicating parties can be generated through mathematical algorithms or pre-shared information and used for encryption and decryption operations. The advantages of these technologies are that they are relatively simple to implement, do not rely on complex physical layer characteristics, and can also better adapt to the key management requirements of certain fixed scenarios.

[0004] However, the existing technologies still have many shortcomings in practical applications. On the one hand, the key scheme based on pseudo-random number generator lacks randomness when facing complex attacks (such as statistical analysis or machine learning prediction), and the generated keys are at risk of being speculated. On the other hand, the pre-shared key mechanism relies on the initially distributed keys and lacks dynamics. Once the keys are leaked, the security of the entire system will be reduced. In addition, the third-party negotiation-based approach often requires additional channel bandwidth, increases communication resource consumption, and is not easy to deploy in resource-constrained scenarios, especially in wireless channels. These existing technologies are difficult to effectively utilize the physical characteristics of the channel, resulting in the key synchronization process being susceptible to noise and signal fading, further reducing the security and consistency of the keys. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides a wireless communication data decryption method based on a dynamic key, which solves the problems in the prior art of insufficient key randomness, lack of a dynamic update mechanism and poor synchronization reliability.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: A wireless communication data decryption method based on a dynamic key comprises the following steps:

[0007] Wireless channel characteristic data collection, obtaining wireless channel characteristic information of both communicating parties;

[0008] Random matrix construction and normalization, generating a normalized random matrix based on the collected channel characteristic information;

[0009] Dynamic key generation, by performing eigenvalue decomposition and optimization on the normalized random matrix to generate dynamic keys;

[0010] Dynamic key synchronization: dynamic key synchronization between communicating parties is achieved by constructing a channel state Markov chain model;

[0011] Ciphertext decryption verifies index consistency based on the generated dynamic key and decrypts the received ciphertext data.

[0012] Preferably, the wireless channel characteristic data collection includes:

[0013] Acquire channel state information from a wireless communication channel and construct a channel state information matrix;

[0014] Extracting amplitude data of a channel state information matrix;

[0015] The amplitude data is quantized to obtain a channel characteristic matrix in real form.

[0016] Preferably, the random matrix construction and normalization includes:

[0017] Construct a random matrix based on the quantized channel characteristic matrix;

[0018] Calculate the mean and standard deviation of a random matrix;

[0019] The random matrix is ​​normalized to obtain a normalized random matrix.

[0020] Preferably, the dynamic key generation includes:

[0021] Perform eigenvalue decomposition on the normalized random matrix to obtain the eigenvalue diagonal matrix and the orthogonal eigenvector matrix;

[0022] Calculate the distribution entropy of the eigenvalues;

[0023] If the distribution entropy value is less than the preset threshold, the eigenvalue is randomly perturbed and optimized;

[0024] The optimized feature value is mapped to a dynamic key through a hash function.

[0025] Preferably, the random perturbation optimization of the eigenvalues ​​includes:

[0026] Add random noise based on Gaussian distribution to the eigenvalues;

[0027] Adjust the random noise weight factor and recalculate the eigenvalue distribution entropy value;

[0028] If the entropy value still does not meet the preset threshold, it is iteratively adjusted until the entropy value reaches the preset threshold.

[0029] Preferably, the dynamic key synchronization includes:

[0030] Construct a channel state sequence based on channel characteristic information and quantize and generate a finite state set;

[0031] Construct a Markov chain model based on the channel state sequence and generate a state transfer matrix;

[0032] Optimize the state transfer matrix by adjusting the state quantization step size;

[0033] When both communicating parties achieve the same state, dynamic key synchronization is completed.

[0034] Preferably, the optimization of the state quantization step size includes:

[0035] Calculate the variance of channel noise;

[0036] Adjust the state quantization step size according to the relationship between the noise variance and the quantization step size;

[0037] Regenerate the state transfer matrix and iteratively adjust it until the synchronization failure probability meets the requirements.

[0038] Preferably, the ciphertext decryption includes:

[0039] Generate index based on dynamic key;

[0040] The communicating parties verify the consistency of the index and confirm the correctness of the dynamic key;

[0041] Use the dynamic key to decrypt the ciphertext and output the decrypted plaintext data.

[0042] Preferably, the index consistency verification includes:

[0043] The communicating parties generate indexes based on the same random matrix eigenvalue sequence;

[0044] Compare the indexes generated by both parties to see if they are consistent;

[0045] If the indexes are consistent, it is determined that the dynamic key is available.

[0046] Preferably, the decryption includes:

[0047] Symmetrically decrypt the ciphertext using a dynamic key;

[0048] Verify the integrity of the decrypted data;

[0049] If the decryption fails, re-request the ciphertext data and repeat the decryption steps.

[0050] The present invention provides a wireless communication data decryption method based on a dynamic key, which has the following beneficial effects:

[0051] 1. The present invention uses wireless channel characteristic modeling and random matrix eigenvalue optimization technology, combined with entropy calculation to dynamically adjust the eigenvalue distribution, to generate a key with high randomness and dynamic changes. Compared with the prior art solutions that rely on pseudo-random number generators or fixed keys, the present invention effectively solves the problem that the key is easily predictable or lacks randomness, and significantly improves the security of the system.

[0052] 2. The present invention adopts the channel state Markov chain model, and realizes dynamic key synchronization between the communicating parties by accurately quantifying the channel state sequence and modeling the state transition probability. Compared with the synchronization method that requires additional channel overhead, the present invention does not rely on additional negotiation signals or auxiliary information, solves the problem of high synchronization failure rate or high overhead in traditional technologies, and improves the reliability and efficiency of synchronization.

[0053] 3. The present invention combines dynamic key index verification and data integrity verification mechanisms to ensure the integrity of data during transmission by verifying the consistency of key indexes during decryption and performing hash verification on the decrypted data. Compared with the prior art that fails to detect tampering of decrypted data, the present invention strengthens the security verification capability of decrypted data and effectively resists replay attacks and key leakage risks through dynamic keys. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0055] The following will be combined with the drawings in the specification of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0056] Please refer to the attached Figure 1 The embodiment of the present invention provides a method for decrypting wireless communication data based on a dynamic key, comprising the following steps:

[0057] S1. Wireless channel characteristic data collection, obtaining wireless channel characteristic information of both communicating parties;

[0058] The physical characteristics of the wireless channel (such as channel state information CS I) are random and unique in different time and space. By collecting and processing the channel characteristic data, a high-quality random source can be provided for the subsequent construction of the random matrix and the generation of the dynamic key. The collected data must meet the requirements of real-time and integrity to ensure that the generation of the dynamic key and the decryption process are synchronized. This embodiment describes the collection and processing of the wireless channel characteristic data in detail.

[0059] First, channel state information (CSI) is collected from the wireless communication channel. In general, CSI can reflect the propagation characteristics of the signal in the channel, including path loss, multipath effect, and signal attenuation. These characteristics are usually expressed in complex form, which includes two parts: amplitude and phase. In one possible implementation, the transmitting device transmits the signal through a multi-antenna array, the receiving device receives the signal through the antenna array, and measures the gain value of the received signal, thereby obtaining the channel gain matrix H(t) between the communicating parties. The matrix can be expressed as:

[0060] H(t)={H ij (t)}, i∈[1,m], j∈[1,n]

[0061] Where: H ij (t) represents the complex channel gain between the i-th transmitting antenna and the j-th receiving antenna; m is the number of transmitting antennas; n is the number of receiving antennas; and t is the acquisition time step.

[0062] In some embodiments, the collected channel state information may include channel response values ​​of multiple frequency subcarriers. Gain information of these frequency subcarriers constitutes specific elements in the matrix H(t).

[0063] Secondly, the collected channel gain matrix H(t) is subjected to data preprocessing. Specifically, as an option, the amplitude information can be extracted from the channel gain matrix and the phase part can be removed. The amplitude is the most important random component in the channel gain and can more intuitively reflect the physical change characteristics of the channel. Therefore, in a possible implementation of the present invention, after the amplitude of the channel gain matrix is ​​extracted, it is converted into a channel characteristic matrix in real number form:

[0064] H ij (t)=|H ij (t)|

[0065] Where: |H ij (t)| represents the amplitude of the channel gain between the i-th transmitting antenna and the j-th receiving antenna; H ij (t) represents the channel characteristics in real form.

[0066] As an option, after the amplitude is extracted, the data can be further denoised, for example, by using a sliding window filter method to smooth the time variation curve of the amplitude data to reduce the impact of high-frequency noise caused by environmental interference or equipment measurement errors.

[0067] Next, the channel characteristic matrix is ​​normalized. Generally, the amplitude distribution range of the channel gain matrix is ​​large. In order to eliminate the data amplitude difference under different communication environments, the normalization operation is used in this embodiment to scale the matrix data to a standardized range. Specifically, the normalization formula is as follows:

[0068]

[0069] Where: M(t) is the normalized channel characteristic random matrix; H(t) is the channel characteristic matrix; μ H Represents the mean of the matrix H(t), and the calculation formula is:

[0070]

[0071] Where: H ij (t): element in the channel characteristic matrix; m: number of transmitting antennas; n: number of receiving antennas; t: time step; μH: mean value of the channel characteristic matrix; The inverse of the number of matrix elements;

[0072] σ H Represents the standard deviation of the matrix H(t), and the calculation formula is:

[0073]

[0074] In one possible implementation, the random matrix M(t) obtained after normalization has zero mean and unit standard deviation. This normalization form can eliminate the influence of data magnitude, making the subsequent random matrix analysis more accurate and stable.

[0075] In addition, in some embodiments, the normalized random matrix M(t) may be further divided into submatrices. For example, the matrix M(t) may be decomposed into multiple submatrices according to the grouping of antenna indexes or frequency subcarriers. In this way, the local characteristics of the channel may be processed separately, thereby improving the flexibility of subsequent dynamic key generation.

[0076] Finally, the present invention particularly emphasizes that the collection of wireless channel characteristic data needs to match the dynamic key generation process. The channel characteristic data H(t) collected by both communicating parties at the same time step t must be as consistent as possible to ensure that the subsequently generated dynamic key has synchronization and consistency;

[0077] Through the above steps, the collection and processing of wireless channel characteristic data are completed.

[0078] S2, random matrix construction and normalization, generating a normalized random matrix based on the collected channel characteristic information;

[0079] Based on the channel characteristic data obtained in the above step S1, this step converts this information into a mathematical form suitable for subsequent random matrix analysis to ensure that the random matrix can fully reflect the physical randomness of the channel and provide a high-quality random source for the generation of dynamic keys. The construction and normalization of the random matrix not only needs to consider the diversity of channel characteristic data, but also needs to eliminate the impact of environmental interference on the data range and distribution to improve randomness and computational stability.

[0080] The present invention first generates a random matrix based on the channel characteristic data collected from the wireless communication channel. In general, the channel characteristic data is obtained through signal transmission and reception of a multi-antenna array, and its physical meaning is the gain and attenuation of the signal when it propagates on different paths. In a possible implementation, the channel characteristic matrix H(t) obtained in the aforementioned step S1 is an m×n real number matrix, where m and n are the number of transmitting antennas and receiving antennas, respectively, and t is the time step. For each time step t, the channel characteristic matrix H(t) can be directly used as the initial version of the random matrix.

[0081] In order to further improve the randomness and applicability of the data, the channel characteristic matrix H(t) is normalized in this embodiment. Specifically, the normalization operation is based on the distribution characteristics of the channel characteristic data, and the dynamic range of the data is adjusted to a standardized range, so that the mean is 0 and the standard deviation is 1, so as to eliminate the amplitude difference between different data sources.

[0082] In some embodiments, in order to reduce the impact of environmental interference and device noise on the normalized matrix M(t), the channel characteristic matrix H(t) can be filtered before normalization. For example, the data is smoothed using a sliding window mean filter to eliminate high-frequency noise. At the same time, the amplitude data of the matrix H(t) can be truncated to remove abnormal data points that exceed a preset threshold range. These steps help improve the quality of the matrix after normalization, making it more suitable for subsequent random matrix analysis.

[0083] After the normalization process is completed, the mean of the matrix M(t) is adjusted to 0 and the standard deviation is adjusted to 1. This standardized form makes the element distribution of the matrix more symmetrical, which helps to improve the stability and efficiency of the calculation in the eigenvalue decomposition of random matrices.

[0084] As an option, in some special implementations, the normalized matrix M(t) can be further processed in blocks. For example, for a communication system with multiple frequency subcarriers, the matrix M(t) can be divided into multiple submatrices according to the frequency range of the subcarriers. Each submatrix corresponds to the channel characteristics of a frequency subband, thereby capturing the frequency domain randomness of the channel in a more fine-grained manner. This block processing method is suitable for high-throughput, multi-band wireless communication scenarios.

[0085] In another possible implementation, a time dimension may be added to the normalized random matrix M(t). Specifically, a time dimension may be added to the normalized random matrix M(t) at multiple consecutive time steps t1, t2, ..., t k The matrix of is superimposed to form a three-dimensional random matrix M′(t), whose dimension is m×n×k. This operation can incorporate the time-related channel characteristics into the random matrix analysis, further improving the dimension of randomness.

[0086] Finally, it should be noted that the normalization process must match the data collection in the aforementioned step S1. Only when the real-time and consistency of the channel characteristic data collection are guaranteed, can the normalized random matrix M(t) provide reliable input for subsequent steps (such as eigenvalue decomposition and dynamic key generation);

[0087] Through the construction and normalization operation of the above random matrix, the obtained matrix M(t) can not only fully reflect the physical randomness of the channel, but also has good mathematical stability and standardization characteristics.

[0088] S3, dynamic key generation, generating dynamic keys by performing eigenvalue decomposition and optimization on the normalized random matrix;

[0089] Dynamic key generation is one of the core steps of the present invention. By eigenvalue decomposition and distribution optimization of the above-mentioned normalized random matrix M(t), a highly random and dynamically changing key is generated. The normalized random matrix is ​​derived from the wireless channel characteristic data and has randomness and unpredictability. Eigenvalue decomposition can extract the inherent random characteristics of the matrix, and distribution optimization further enhances the randomness and uniformity of the key. The generated dynamic key is not only closely related to the physical state of the communication channel, but also can be updated in real time, effectively improving the security of wireless communication data decryption.

[0090] First, the normalized random matrix M(t) is subjected to eigenvalue decomposition. In general, eigenvalue decomposition is a commonly used matrix decomposition method, which is used to represent a matrix as a combination of eigenvalues ​​and eigenvectors. In the present invention, the normalized random matrix M(t) is an m×n real number matrix, where m and n are the number of rows and columns of the matrix, respectively. Through eigenvalue decomposition, the following expression can be obtained:

[0091]

[0092] Among them: Q is an orthogonal matrix, representing the eigenvector matrix of M(t); Λ is a diagonal matrix, representing the eigenvalue matrix of M(t), and its form is:

[0093] Λ=diag(λ1,λ2,…,λ k )

[0094] Where: λ1 is the i-th eigenvalue of the matrix M(t);

[0095] In a possible implementation, the calculation of the eigenvalues ​​may be based on conventional methods in numerical linear algebra, such as QR decomposition or singular value decomposition (SVD), which can effectively extract the eigenvalues ​​and eigenvectors of a matrix.

[0096] Secondly, the randomness of the distribution of the eigenvalues ​​is analyzed. Specifically, the present invention evaluates the randomness of the eigenvalues ​​by calculating the Shannon entropy of the eigenvalue distribution. The entropy value is an important indicator for measuring randomness and information content. The calculation formula of the entropy value H(Λ) of the eigenvalue distribution is as follows:

[0097]

[0098] in: Denotes the eigenvalue λ i The normalized probability distribution of ; k is the total number of eigenvalues;

[0099] In some embodiments, in order to improve the randomness of the eigenvalue, the entropy value of the eigenvalue distribution can be compared with the preset threshold H min In general, if H(∧)≥H min , then the randomness of the eigenvalues ​​is considered to meet the requirements and no further optimization is required; otherwise, the eigenvalues ​​need to be randomly perturbed to increase the entropy value.

[0100] Next, if the entropy value does not reach the preset threshold, the eigenvalue is optimized. Specifically, as an option, the eigenvalue sequence {(λ1, λ2, ..., λ k )}Add random noise and adjust its distribution to increase the entropy value. The optimized eigenvalue λ′ i The calculation formula is as follows:

[0101] λ′ i =λ i +αR i

[0102] Where: λ′ i is the optimized eigenvalue; i is the original eigenvalue; α is the weight factor of random noise; It means that the mean is 0 and the variance is σ 2 Gaussian distributed random noise.

[0103] In a possible implementation, the value of α can be gradually increased while recalculating the optimized entropy value until H(Λ′)≥H is satisfied. min This iterative optimization method can ensure that the final generated eigenvalue sequence has sufficient randomness.

[0104] Finally, the optimized feature value is mapped to a dynamic key. In this embodiment, the generation of the dynamic key is based on a secure hash function (such as SHA-256). Specifically, the optimized feature value sequence {λ′1, λ′2, ..., λ′ k} as input, a fixed-length dynamic key K is generated through a hash function t The key generation formula is:

[0105] K t =Hash({λ′1,λ′2,...,λ′ k})

[0106] Where: K t represents the dynamic key generated at time step t; Hash represents a hash function used to map the input to an output of fixed length.

[0107] In some embodiments, the interception range of the feature value sequence can be adjusted to select some feature values ​​for the generation of dynamic keys. For example, the first q largest values ​​of the feature values ​​are selected as input to form a subsequence {λ′1, λ′2, ..., λ′ q}. This method can further improve the flexibility and adaptability of key generation.

[0108] Through the above steps, the dynamic key generation process realizes the transformation from random matrix to high randomness key.

[0109] S4, dynamic key synchronization, realizes dynamic key synchronization between the communicating parties by constructing a channel state Markov chain model;

[0110] Dynamic key synchronization is a key step to ensure that both communicating parties can use the same key for encryption and decryption. By constructing a channel state Markov chain model and utilizing the physical characteristics of the wireless channel, the present invention realizes the generation of synchronized dynamic keys without additional channel overhead. The channel state is quantified by the channel characteristic matrix. The Markov chain model can accurately describe the transition law of the channel state and further realize the synchronous update of the dynamic key. This step is closely related to the aforementioned random matrix construction, eigenvalue decomposition and dynamic key generation links, ensuring the security and real-time performance of the dynamic key.

[0111] First, the communicating parties quantize the channel characteristic matrix H(t) to generate a channel state sequence. Generally, the channel state sequence is obtained based on the discretization of the channel characteristic data. In a possible implementation, each element H of the channel characteristic matrix H(t) is first quantized. ij (t) is mapped to the preset discrete state set S = {s1, s2, ..., s k}. The quantization rule can be implemented by segmenting the channel gain range, for example:

[0112]

[0113] Where: s1 is the discrete channel state; τ1 is the segmented threshold for state quantization.

[0114] As an option, the segmentation thresholds for channel state quantization may be dynamically adjusted according to the statistical distribution of historical channel characteristic data, such as a method based on quantile or histogram uniform distribution.

[0115] Next, the communicating parties construct a Markov chain model based on the quantized channel state sequence. Specifically, the Markov chain model consists of a state set and a state transition probability matrix P, where the state transition probability P ij Indicates that the system changes from state s i Transfer to state s j In general, the transition probability can be obtained by statistical analysis of the channel state sequence:

[0116]

[0117] Where: P ij Indicates that from state s i Transfer to state s j The probability of t =s i ,s t+1 =s j ) represents the channel state at time t from s i Transfer to j The number of occurrences; times (s t =s i ) indicates state s i The total number of occurrences of .

[0118] In a possible implementation, in order to improve the robustness of the transition probability matrix, the transition probability can be smoothed. For example, a smoothing factor ∈ is introduced to obtain the adjusted probability matrix:

[0119]

[0120] Where: P ij is the smoothed state transition probability; ∈ is the smoothing factor; k is the size of the state set.

[0121] Then, based on the constructed Markov chain model, the communicating parties can predict and synchronize the dynamic key update process. In general, the Markov chain model predicts the next state based on the current state. Therefore, the communicating parties can start from the same initial state and generate a synchronized state sequence {s t}. These state sequences are further used for index generation of dynamic keys.

[0122] In this embodiment, in order to reduce the probability of synchronization failure, it is necessary to optimize the state quantization rule and the Markov chain model parameters. As an option, the state quantization step size can be adjusted based on the statistical characteristics of the channel noise. Specifically, the state quantization step size Δ s The optimization formula is as follows:

[0123]

[0124] Where: P(F) is the probability of key synchronization failure; σ 2 is the variance of channel noise; Δ s is the state quantization step size.

[0125] By adjusting Δ s The size of can be used to trade off synchronization accuracy and computational overhead.

[0126] Finally, the communicating parties generate dynamic key indexes based on the synchronized channel state sequence. t} is mapped to dynamic key index I t , the formula is as follows:

[0127] I t =Hash(s t , t)

[0128] Where: I t is the index of the dynamic key; Hash is a secure hash function; s t is the channel state at the current time step; t is the time step.

[0129] Through the above steps, the communicating parties can realize the synchronous update of dynamic keys based on the channel state Markov chain model.

[0130] S5, ciphertext decryption, verifying index consistency based on the generated dynamic key and decrypting the received ciphertext data;

[0131] Ciphertext decryption is mainly used to decrypt the received ciphertext data based on the dynamic key generated synchronously by both parties of the communication, and verify the integrity of the decrypted data. This step depends on the key index I generated in the aforementioned dynamic key generation and synchronization module. t and dynamic key K t By verifying the consistency of the key index of both communicating parties, the security and accuracy of the decryption process can be ensured. The decrypted ciphertext data is output as plaintext and can be checked for integrity as needed. The implementation of this step effectively connects dynamic key synchronization and communication data decryption, ensuring the secure transmission of wireless communication data.

[0132] First, the receiving device calculates the dynamic key index I based on the channel state sequence and time step t t In general, the key index is an identifier of the dynamic key generation of the communicating parties, which is used to verify whether the two parties have generated the same key;

[0133] In a possible implementation, the receiving device can directly obtain the channel state sequence {s t} to extract the channel state s at the current time step t , and combined with time step t, it is input into the hash function to calculate the key index.

[0134] Next, the receiving device calculates the key index I t Compare it with the key index sent by the sender. Generally, if the two communicating parties are consistent during the dynamic key synchronization process, the calculated key index I t Should be consistent with the sender. As an option, index comparison can be achieved by directly comparing whether the index values ​​are the same:

[0135]

[0136] in: The key index calculated by the receiving end; The key index that the sender appends to the ciphertext.

[0137] If the key indexes are inconsistent, the receiving end cannot perform subsequent decryption operations and requests the sending end to resend the encrypted data or renegotiate the key.

[0138] After the key index is verified, the receiver generates a dynamic key K based on the synchronization. t For the received ciphertext data C t Decryption is performed. In general, the dynamic key K t It is generated by eigenvalue decomposition and optimization. The decryption operation is completed using a symmetric encryption algorithm (such as AES), and its formula is:

[0139]

[0140] Where: M t is the plaintext data obtained by decryption; K t is a dynamic key; C t is the ciphertext received at the current time step; Dec is the decryption algorithm.

[0141] Specifically, the receiving end converts the ciphertext C t and dynamic key K t Input into the symmetric decryption algorithm, and after operation, the decrypted plaintext data Mt is obtained. As an option, a block encryption mode (such as AES-CBC, AES-GCM) can be selected to implement data decryption to take into account both data integrity and encryption efficiency.

[0142] In a possible implementation, in order to further verify the integrity of the decrypted data, a hash operation can be performed on the decrypted plaintext Mt and compared with the integrity check value attached by the sender. For example:

[0143]

[0144] Where: Hash(M t ) is the plaintext hash value calculated by the receiving end; A plaintext hash value appended by the sender.

[0145] If the integrity verification passes, the decrypted data M t If the verification fails, it means that the decrypted data may have been tampered with, and the receiving end can choose to request the ciphertext data again.

[0146] Finally, after decryption is completed, the receiving end passes the plaintext data to the upper-layer application module. As an option, the plaintext data can be further parsed and processed, such as parsing the payload data in the communication protocol or executing specific business logic;

[0147] Key index verification ensures the key consistency of both communicating parties, while integrity verification further guarantees the reliability of decrypted data; the entire decryption process relies on dynamic key generation and synchronization modules, making full use of the randomness and dynamism of the wireless channel, providing a guarantee for the secure transmission of wireless communication data.

[0148] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for decrypting wireless communication data based on a dynamic key, characterized in that: The following steps are involved: Wireless channel characteristic data collection, obtaining wireless channel characteristic information of both communicating parties; Random matrix construction and normalization, generating a normalized random matrix based on the collected channel characteristic information; Dynamic key generation, by performing eigenvalue decomposition and optimization on the normalized random matrix to generate dynamic keys; Dynamic key synchronization: dynamic key synchronization between communicating parties is achieved by constructing a channel state Markov chain model; Ciphertext decryption verifies index consistency based on the generated dynamic key and decrypts the received ciphertext data.

2. A method for decrypting wireless communication data based on a dynamic key according to claim 1, characterized in that: The wireless channel characteristic data collection includes: Acquire channel state information from a wireless communication channel and construct a channel state information matrix; Extracting amplitude data of a channel state information matrix; The amplitude data is quantized to obtain a channel characteristic matrix in real form.

3. A method for decrypting wireless communication data based on dynamic keys according to claim 1, characterized in that: The random matrix construction and normalization include: Construct a random matrix based on the quantized channel characteristic matrix; Calculate the mean and standard deviation of a random matrix; The random matrix is ​​normalized to obtain a normalized random matrix.

4. The method for decrypting wireless communication data based on dynamic keys according to claim 1, characterized in that: The dynamic key generation includes: Perform eigenvalue decomposition on the normalized random matrix to obtain the eigenvalue diagonal matrix and the orthogonal eigenvector matrix; Calculate the distribution entropy of the eigenvalues; If the distribution entropy value is less than the preset threshold, the eigenvalue is randomly perturbed and optimized; The optimized feature value is mapped to a dynamic key through a hash function.

5. A method for decrypting wireless communication data based on dynamic keys according to claim 4, characterized in that: The random perturbation optimization of the eigenvalues ​​includes: Add random noise based on Gaussian distribution to the eigenvalues; Adjust the random noise weight factor and recalculate the eigenvalue distribution entropy value; If the entropy value still does not meet the preset threshold, it is iteratively adjusted until the entropy value reaches the preset threshold.

6. A method for decrypting wireless communication data based on dynamic keys according to claim 1, characterized in that: The dynamic key synchronization comprises: Construct a channel state sequence based on channel characteristic information and quantize and generate a finite state set; Construct a Markov chain model based on the channel state sequence and generate a state transfer matrix; Optimize the state transfer matrix by adjusting the state quantization step size; When both communicating parties achieve the same state, dynamic key synchronization is completed.

7. A method for decrypting wireless communication data based on dynamic keys according to claim 6, characterized in that: The optimization of the state quantization step size includes: Calculate the variance of the channel noise; Adjust the state quantization step size according to the relationship between the noise variance and the quantization step size; Regenerate the state transfer matrix and iteratively adjust it until the synchronization failure probability meets the requirements.

8. A method for decrypting wireless communication data based on dynamic keys according to claim 1, characterized in that: The ciphertext decryption comprises: Generate index based on dynamic key; The communicating parties verify the consistency of the index and confirm the correctness of the dynamic key; Use the dynamic key to decrypt the ciphertext and output the decrypted plaintext data.

9. A method for decrypting wireless communication data based on dynamic keys according to claim 8, characterized in that: The index consistency verification includes: The communicating parties generate indexes based on the same random matrix eigenvalue sequence; Compare the indexes generated by both parties to see if they are consistent; If the indexes are consistent, it is determined that the dynamic key is available.

10. A method for decrypting wireless communication data based on dynamic keys according to claim 8, characterized in that: The decryption includes: Symmetrically decrypt the ciphertext using a dynamic key; Verify the integrity of the decrypted data; If the decryption fails, re-request the ciphertext data and repeat the decryption steps.

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