Identity authentication method based on nonlinear signal characteristics and electronic equipment

Abstract

The invention discloses an identity authentication method based on nonlinear signal features and electronic equipment, and the method comprises the steps: collecting communication signal data of to-be-authenticated equipment, obtaining IQ data corresponding to a frame preamble sequence of the to-be-authenticated equipment, carrying out the standardization, obtaining the standardized frame preamble IQ data, and extracting a conventional physical layer feature value of the to-be-authenticated equipment; converting the standardized frame preamble IQ data into a target reference symbol, and calculating a nonlinear signal characteristic value of the device; combining the nonlinear signal characteristic value and the conventional physical layer characteristic value; and finally, calculating the distance between the combined feature vector and the feature vector of the legal equipment, and obtaining an identity authentication result of the to-be-authenticated equipment according to the distance. According to the scheme, the non-linear signal features are introduced, and the non-linear signal feature values and the conventional physical layer feature values are constructed and combined to form the combined feature vector containing more information of the to-be-authenticated equipment, so that the hardware features of the to-be-authenticated equipment can be described more comprehensively, and the identity authentication accuracy is improved.

Description

Technical Field

[0001] The present invention relates to the field of information security technology, and in particular to an identity authentication method and electronic equipment based on nonlinear signal characteristics. Background Art

[0002] At present, wireless communication has become the main communication channel of the Internet, providing convenience and efficiency for connecting massive devices. However, differentiated wireless communication devices have brought about problems with the authenticity of device identity and communication security. In addition, since wireless communication networks use an open channel transmission mechanism, they are vulnerable to threats such as illegal device access, man-in-the-middle attacks, and signal forgery.

[0003] Therefore, device identity authentication technology is not only a necessary means to maintain network order, but also an important guarantee to prevent illegal access and protect user privacy. Among the related technologies, there is the identity authentication technology based on physical layer characteristics, which uses the inherent physical characteristics of wireless signals to achieve efficient and reliable device identity authentication without relying on additional keys.

[0004] However, conventional physical layer features in related technologies, such as carrier frequency deviation, phase error, and amplitude error, still have certain limitations in practical applications. On the one hand, these physical layer features are few in number and cannot fully characterize the hardware characteristics of the device; on the other hand, the authentication accuracy that relies on these physical layer features is still insufficient and it is difficult to meet the needs of high-security scenarios. Summary of the invention

[0005] In order to solve the above problems existing in the prior art, the present invention provides an identity authentication method and electronic device based on nonlinear signal characteristics. The technical problem to be solved by the present invention is achieved through the following technical solutions:

[0006] According to a first aspect of an embodiment of the present invention, there is provided an identity authentication method based on nonlinear signal features, the method comprising:

[0007] Collect the communication signal data of the device to be authenticated, obtain the in-phase component I and the orthogonal component Q corresponding to its frame preamble sequence, and perform standardization processing to obtain standardized frame preamble IQ data;

[0008] Converting the standardized frame leading IQ data into target reference symbols, and calculating the nonlinear signal characteristic value of the device to be authenticated based on the target reference symbols;

[0009] Extracting conventional physical layer feature values ​​of the device to be authenticated based on the standardized frame leading IQ data;

[0010] Constructing and combining the nonlinear signal characteristic value and the conventional physical layer characteristic value to obtain a combined characteristic vector to be authenticated;

[0011] The distance between the combined feature vector of the to-be-authenticated device and the feature vector of the legal device is calculated, and the identity authentication result of the to-be-authenticated device is obtained according to the distance.

[0012] In one embodiment of the present invention, the nonlinear signal characteristic values ​​include correlation dimension CD, Lyapunov exponent LE, fractal dimension FD and Hurst exponent HE;

[0013] The converting the standardized frame leading IQ data into a target reference symbol, and calculating the nonlinear signal characteristic value of the device to be authenticated based on the target reference symbol, comprises:

[0014] The standardized frame leading IQ data is phase rotated to obtain a target reference symbol; and the correlation dimension CD, Lyapunov exponent LE, fractal dimension FD and Hurst exponent HE of the device to be authenticated are calculated based on the target reference symbol.

[0015] In one embodiment of the present invention, the target reference symbol includes the i-th sampling point S(i) of the frame preamble sequence and the j-th sampling point S(j) of the frame preamble sequence;

[0016] The calculating the correlation dimension CD of the device to be authenticated based on the target reference symbol comprises:

[0017] By formula Calculating the correlation dimension CD;

[0018] Where Θ() represents the Heaviside step function, r k Indicates the preset threshold, L S(i),S(j) represents the Euclidean distance between S(i) and S(j), and N represents the number of sampling points of the frame preamble sequence.

[0019] In one embodiment of the present invention, the target reference symbol includes the tth sampling point S(t) of the frame preamble sequence;

[0020] The calculating the Lyapunov exponent LE of the device to be authenticated based on the target reference symbol comprises:

[0021] By using the formula D(t)=[S(t), S(t+τ), ..., S(t+(d-1)τ)]; the frame preamble sequence is time-delay embedded to obtain a high-dimensional embedding space sequence;

[0022] Where d represents the embedding dimension, τ represents the time delay, and t represents the time index;

[0023] By formula Calculate the logarithmic growth rate LE k (p, q);

[0024] Among them, p represents the embedded point, q is the nearest neighbor of p, k represents the time step, L D(p)，D(q) represents the Euclidean distance between D(p) and D(q);

[0025] By formula Calculating the Lyapunov exponent LE;

[0026] Wherein, m represents the maximum value of the time step k, and N represents the number of sampling points of the frame preamble sequence.

[0027] In one embodiment of the present invention, the calculating the fractal dimension FD of the device to be authenticated based on the target reference symbol includes:

[0028] By formula Calculating the fractal dimension FD;

[0029] Where τ is the interval size, L S(τ),S(n) represents the Euclidean distance between S(τ) and S(i), N represents the number of sampling points of the frame preamble sequence, S(τ) represents the τth sampling point of the frame preamble sequence, and S(i) represents the i-th sampling point of the frame preamble sequence.

[0030] In one embodiment of the present invention, the calculating the Hurst exponent HE of the device to be authenticated based on the target reference symbol includes:

[0031] By formula Calculating the Hurst exponent HE;

[0032] Where τ is the interval size, L S(τ),s(i ) represents the Euclidean distance between S(τ) and S(i), S(τ) represents the τth sampling point of the frame preamble sequence, and S(i) represents the i-th sampling point of the frame preamble sequence.

[0033] In one embodiment of the present invention, the conventional physical layer characteristic values ​​include a phase error PE, an amplitude error AE, and a carrier frequency offset CFO;

[0034] The constructing and combining the nonlinear signal characteristic value and the conventional physical layer characteristic value to obtain a combined characteristic vector to be authenticated includes:

[0035] Normalizing the correlation dimension CD, the Lyapunov exponent LE, the fractal dimension FD, the Hurst exponent HE, the phase error PE, the amplitude error AE, and the carrier frequency offset CFO to obtain a dimensionless eigenvalue v;

[0036] By the formula v = [v CD , v LE , v FD , v HE , v PE , v AE , v CFO ], merging the eigenvalues ​​v to construct a combined eigenvector to be authenticated;

[0037] Among them, v CD represents the normalized correlation dimension CD, v LE represents the normalized Lyapunov exponent LE, v FD represents the normalized fractal dimension FD, v HE Represents the normalized Hurst exponent HE, v PE Represents the normalized phase error PE, v AE represents the normalized amplitude error AE, v CFO Indicates the normalized carrier frequency offset CFO.

[0038] In one embodiment of the present invention, the calculating the distance between the combined feature vector to be authenticated and the feature vector of the legitimate device, and obtaining the identity authentication result of the device to be authenticated according to the distance, includes:

[0039] Calculate the distance between the combined feature vector to be authenticated and the feature vector of the legitimate device by using a pre-built kNN classifier;

[0040] When the distance is less than a preset threshold, an identity authentication result indicating that the authentication is successful is output, otherwise an identity authentication result indicating that the authentication is failed is output.

[0041] In one embodiment of the present invention, pre-constructing the kNN classifier includes:

[0042] Collecting communication signal data of a legitimate device, obtaining an in-phase component I and a quadrature component Q corresponding to a frame leading sequence of the communication signal data, and performing standardization processing to obtain standardized frame leading IQ data of the legitimate device;

[0043] Converting the standardized frame leading IQ data of the legal device, and calculating the nonlinear signal characteristic value of the legal device based on the converted IQ data;

[0044] Extracting conventional physical layer feature values ​​of the legal device based on the standardized frame preamble iQ data of the legal device;

[0045] Constructing and combining the nonlinear signal characteristic value of the legal device and the conventional physical layer characteristic value of the legal device to obtain a combined characteristic vector of the legal device;

[0046] Assigning a corresponding legal identity tag to the combined feature vector of the legal device to obtain a legal target parameter;

[0047] The legitimate target parameters of multiple legitimate devices are constructed into a target parameter data set, and the kNN classifier is constructed based on the target parameter data set.

[0048] According to a second aspect of an embodiment of the present invention, there is provided an electronic device, the device comprising:

[0049] one or more processors;

[0050] A computer readable medium configured to store one or more programs;

[0051] When the one or more programs are executed by the one or more processors, the one or more processors implement the identity authentication method based on nonlinear signal features as described in the first aspect.

[0052] Compared with the prior art, the present invention has the following beneficial effects:

[0053] The identity authentication method and electronic device based on nonlinear signal characteristics provided by the embodiment of the present invention, when performing identity authentication, first collect the communication signal data of the device to be authenticated, obtain the in-phase component I and the orthogonal component Q corresponding to its frame preamble sequence, and perform standardization processing to obtain standardized frame preamble IQ data; then convert the standardized frame preamble IQ data into a target reference symbol, and calculate the nonlinear signal characteristic value of the device to be authenticated based on the target reference symbol; in addition, based on the standardized frame preamble IQ data, the conventional physical layer characteristic value of the device to be authenticated is also extracted; then the nonlinear signal characteristic value and the conventional physical layer characteristic value are constructed and combined to obtain a combined characteristic vector to be authenticated; finally, the distance between the combined characteristic vector and the legal device characteristic vector is calculated, and the identity authentication result of the device to be authenticated is obtained according to the distance. This scheme introduces the nonlinear signal characteristics of the device to be authenticated, expands the signal characteristics of the device to be authenticated, increases the identity authentication dimension, and constructs and combines the nonlinear signal characteristic value and the conventional physical layer characteristic value to form a combined characteristic vector containing more information of the device to be authenticated, which can more comprehensively characterize the hardware characteristics of the device to be authenticated and improve the accuracy of identity authentication.

[0054] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 is a flowchart of the steps of an identity authentication method based on nonlinear signal characteristics provided in the first embodiment of the present invention;

[0056] Figure 2 is a flow chart of a method for constructing a kNN classifier provided in the second embodiment of the present invention;

[0057] Figure 3 It is a structural schematic diagram of an electronic device provided in Embodiment 3 of the present invention. DETAILED DESCRIPTION

[0058] The present invention is further described in detail below with reference to specific embodiments, but the embodiments of the present invention are not limited thereto.

[0059] Embodiment 1

[0060] Reference Figure 1 , shows a step flow chart of an identity authentication method based on nonlinear signal features according to Embodiment 1 of the present invention.

[0061] The text detection method of this embodiment comprises the following steps:

[0062] Step 101: collect communication signal data of the device to be authenticated, obtain the in-phase component I and the orthogonal component Q corresponding to its frame preamble sequence, and perform standardization processing to obtain standardized frame preamble IQ data.

[0063] The device to be authenticated may be a wireless communication device, such as a mobile phone, a walkie-talkie, etc.

[0064] In this embodiment, the communication signal data of the device to be authenticated can be collected by the following formula:

[0065] X0=[[I0,Q0],[I1,Q1],…,[I i , Q i ],…,[I n , Q n ]]

[0066] Where, i = 1, 2, ..., n, n is the IQ sampling length of the complete data frame, [I i , Q i ] is the IQ value of the IQ sampling data corresponding to the data frame at the i-th position.

[0067] When obtaining the in-phase component I and the orthogonal component Q corresponding to the signal data frame leading sequence and performing standardization processing, first, the IQ sampling data X corresponding to the frame leading sequence of the signal data X0 is intercepted:

[0068] X=[[I0,Q0],[I1,Q1],…,[I i , Q i ],…,[I N , Q N ]]

[0069] Where, i = 1, 2, ..., N, N is the number of sampling points of the frame preamble sequence, [I i , Q i ] is the IQ value of the IQ sampling data of the frame leading sequence at the i-th position.

[0070] Then, the two channels of orthogonal IQ sampling data are standardized:

[0071]

[0072] in, is the IQ sampling data after standardization, that is, the standardized frame leading IQ data is obtained, mean is the average value of the IQ sampling data, and std is the variance of the IQ sampling data.

[0073] Step 102: Convert the standardized frame leading IQ data into target reference symbols, and calculate the nonlinear signal characteristic value of the device to be authenticated based on the target reference symbols.

[0074] The nonlinear signal characteristic values ​​of the device to be authenticated include the correlation dimension CD, the Lyapunov exponent LE, the fractal dimension FD and the Hurst exponent HE.

[0075] Specifically, when converting the standardized frame preamble 1Q data into a target reference symbol and calculating the nonlinear signal characteristic value of the device to be authenticated based on the target reference symbol, the phase of the standardized frame preamble IQ data can be rotated to obtain the target reference symbol; then, the correlation dimension CD, Lyapunov exponent LE, fractal dimension FD and Hurst exponent HE of the device to be authenticated are calculated based on the target reference symbol.

[0076] The target reference symbol includes the i-th sampling point S(i) of the frame preamble sequence, the j-th sampling point S(j) of the frame preamble sequence, the t-th sampling point S(t) of the frame preamble sequence, and the τ-th sampling point S(τ) of the frame preamble sequence.

[0077] Furthermore, when calculating the correlation dimension CD of the device to be authenticated based on the target reference symbol, the formula Calculate the correlation dimension CD. Where Θ() represents the Heaviside step function, r k Indicates the preset threshold, L S(i),S(j) represents the Euclidean distance between S(i) and S(j), and N represents the number of sampling points of the frame preamble sequence.

[0078] When calculating the Lyapunov index LE of the device to be authenticated based on the target reference symbol, first, the frame preamble sequence can be time-delayed and embedded to obtain a high-dimensional embedding space sequence through the formula D(t) = [S(t), S(t+τ), ..., S(t+(d-1)τ)]; where d represents the embedding dimension, τ represents the time delay, and t represents the time index. Secondly, the formula Calculate the logarithmic growth rate LE k (p, q); where p represents the embedded point, q is the nearest neighbor of p, k represents the time step, and L D(p)，D(q) represents the Euclidean distance between D(p) and D(q). Then we can use the formula The Lyapunov exponent LE is calculated, where m represents the maximum value of the time step k, and N represents the number of sampling points of the frame preamble sequence.

[0079] When calculating the fractal dimension FD of the device to be authenticated based on the target reference symbol, the formula Calculate the fractal dimension FD; where τ is the interval size, L S(τ),S(n) represents the Euclidean distance between S(τ) and S(i), N represents the number of sampling points of the frame preamble sequence, S(τ) represents the τth sampling point of the frame preamble sequence, and S(i) represents the i-th sampling point of the frame preamble sequence.

[0080] When calculating the Hurst exponent HE of the device to be authenticated based on the target reference symbol, the formula Calculate the Hurst exponent HE; where τ is the interval size, L S(τ),S(i) represents the Euclidean distance between S(τ) and S(i), S(τ) represents the τth sampling point of the frame preamble sequence, and S(i) represents the i-th sampling point of the frame preamble sequence.

[0081] In this embodiment, the nonlinear signal characteristic values ​​of the device to be authenticated are introduced: correlation dimension CD, Lyapunov exponent LE, fractal dimension FD and Hurst exponent HE. Compared with the prior art, the hardware characteristics of the device to be authenticated can be characterized from more dimensions. Introducing the complex dynamic characteristics implicit in the device hardware into the authentication system expands the dimension of the feature vector used in identity authentication, helps to build a richer and more comprehensive feature space, and thus enhances the robustness of the feature.

[0082] Step 103: extract the conventional physical layer feature value of the device to be authenticated based on the above-mentioned standardized frame leading IQ data.

[0083] In this embodiment, conventional physical layer characteristic values ​​include phase error PE, amplitude error AE and carrier frequency offset CFO. Based on the standardized frame preamble 1Q data obtained in step 101, the phase error PE, amplitude error AE and carrier frequency offset CFO of the device to be authenticated can be calculated.

[0084] Specifically, calculate the phase error PE:

[0085]

[0086] Where N represents the number of sampling points of the frame preamble sequence, Δθ i Represents the phase difference between adjacent sampling points i and i+1 in the standardized frame preamble IQ data.

[0087] Calculate the amplitude error AE:

[0088]

[0089] Among them, S k It is the amplitude value of the kth frequency point obtained after the standardized frame preamble IQ data undergoes Fast Fourier Transform (FFT), μ is the average amplitude of all frequency points, and N represents the number of sampling points of the frame preamble sequence.

[0090] Calculate the carrier frequency offset CFO:

[0091]

[0092] Wherein, ∠() represents the phase angle of the complex number (i.e., the standardized frame leading IQ data in this embodiment), Represents the complex signal sequence The conjugate complex number of the sampling points, S(i) represents the i-th sampling point in the complex signal sequence (ie, the standardized frame preamble 1Q data sequence), and N represents the number of sampling points in the frame preamble sequence.

[0093] Obviously, the above step 102 and step 103 can be executed in any order.

[0094] Step 104: construct and combine the nonlinear signal eigenvalues ​​and the conventional physical layer eigenvalues ​​to obtain a combined eigenvector to be authenticated.

[0095] Through step 102 and step 103, the nonlinear signal characteristic value and the conventional physical layer characteristic value of the device to be authenticated are calculated, and then the nonlinear signal characteristic value and the conventional physical layer characteristic value can be constructed and combined to obtain a multi-dimensional combined characteristic vector.

[0096] Specifically, the correlation dimension CD, Lyapunov exponent LE, fractal dimension FD, Hurst exponent HE, phase error PE, amplitude error AE and carrier frequency offset CFO can be normalized to obtain the dimensionless eigenvalue v: Wherein, v is the normalized eigenvalue, v′ is the original eigenvalue (i.e., correlation dimension CD, Lyapunov exponent LE, fractal dimension FD, Hurst exponent HE, phase error PE, amplitude error AE, and carrier frequency offset CFO), m is the mean of the eigenvalue, and s is the standard deviation of the eigenvalue.

[0097] Then, by the formula v = [v CD , v LE , v FD , v HE , v PE , v AE , v CFO ], merge the above eigenvalues ​​v to construct a combined eigenvector v to be authenticated;

[0098] Among them, v CD represents the normalized correlation dimension CD, v LE represents the normalized Lyapunov exponent LE, v FD represents the normalized fractal dimension FD, v HE Represents the normalized Hurst exponent HE, v PE Represents the normalized phase error PE, v AE represents the normalized amplitude error AE, v CFO Indicates the normalized carrier frequency offset CFO.

[0099] Compared with the identity authentication method in the prior art that only relies on conventional physical layer features, in this embodiment, nonlinear signal features are introduced: correlation dimension CD, Lyapunov exponent LE, fractal dimension FD and Hurst exponent HE, which expands at least 4 dimensions of the feature vector; and by constructing and combining the nonlinear signal eigenvalues ​​and the conventional physical layer eigenvalues, a combined feature vector containing more information about the device to be authenticated is formed, which can more comprehensively characterize the hardware characteristics of the device to be authenticated; it can not only reduce the risk of misjudgment caused by inaccurate single features, but also improve the adaptability of the model to small samples and complex environments, thereby achieving a significant improvement in authentication accuracy in a variety of scenarios and meeting the needs of high-security applications.

[0100] Step 105: Calculate the distance between the combined feature vector to be authenticated and the feature vector of the legal device, and obtain the identity authentication result of the device to be authenticated based on the distance.

[0101] Specifically, the distance between the combined feature vector to be authenticated and the feature vector of the legitimate device can be calculated through a pre-built kNN classifier.

[0102] If the combined feature vector to be authenticated is represented by v x , the legal device feature vector is represented by v i , then calculate v x With each v i The Euclidean distance dis(v x , v i ):

[0103]

[0104] Among them, v x,k is the combined feature vector v of the device to be authenticated x The kth eigenvalue in i,k is the legal device feature vector v i The kth eigenvalue of ; obviously, the value of k and v i The number can be set according to actual needs.

[0105] Then, based on the calculated Euclidean distance dis(v x , v i ), according to the principle of closest distance, m legal device feature vectors are selected to form a nearest neighbor set:

[0106] N m ={(v i1 ,y i1 ), (v i2 ,y i2 ),…,(v im ,y im )}

[0107] And calculate the feature vector set to be authenticated {v x ,y x} and the nearest neighbor set N m The average distance of the m eigenvectors in

[0108]

[0109] where y i The identity tag represents the ith legal device, which may be the device identification number and / or serial number and / or device model, etc., which may be obtained from the communication signal or customized. Similarly, the number k may also be set according to actual needs or experience.

[0110] When the distance is less than a preset threshold, an identity authentication result indicating that the authentication is successful is output; otherwise, an identity authentication result indicating that the authentication is failed is output.

[0111] Based on the average distance calculated Compare it with the preset threshold β. If the average distance The device to be authenticated is determined to be a "legal device", and the authentication result indicating that the authentication has passed is output, such as "authentication qualified" or "legal device" and other information. The identity tag corresponding to the device can also be output at the same time:

[0112]

[0113] Among them, δ() is the voting function:

[0114]

[0115] Otherwise, it is marked as an "illegal device" and given a special label The output indicates the authentication result of failed authentication, such as "illegal device" or "authentication failed".

[0116] The identity authentication method based on nonlinear signal characteristics provided by the embodiment of the present invention, when performing identity authentication, first collects the communication signal data of the device to be authenticated, obtains the in-phase component I and the orthogonal component Q corresponding to its frame preamble sequence, and performs standardization processing to obtain standardized frame preamble IQ data; then converts the standardized frame preamble IQ data into a target reference symbol, and calculates the nonlinear signal characteristic value of the device to be authenticated based on the target reference symbol; in addition, based on the standardized frame preamble IQ data, the conventional physical layer characteristic value of the device to be authenticated is also extracted; then the nonlinear signal characteristic value and the conventional physical layer characteristic value are constructed and combined to obtain a combined characteristic vector to be authenticated; finally, the distance between the combined characteristic vector and the legal device characteristic vector is calculated, and the identity authentication result of the device to be authenticated is obtained according to the distance. This scheme introduces the nonlinear signal characteristics of the device to be authenticated, expands the signal characteristic types of the device to be authenticated, increases the identity authentication dimension, and constructs and combines the nonlinear signal characteristic value and the conventional physical layer characteristic value to form a combined characteristic vector containing more information of the device to be authenticated, which can more comprehensively characterize the hardware characteristics of the device to be authenticated and improve the accuracy of identity authentication.

[0117] Specifically, since nonlinear signal features such as correlation dimension CD, Lyapunov exponent LE, and fractal dimension FD are added to the feature vectors of wireless communication devices, the complex dynamic characteristics implicit in the device hardware are introduced into the authentication system, expanding the dimension of the feature vector used in identity authentication. Combined with conventional physical layer features, the multi-dimensional features work together to enable the authentication process to construct a richer and more comprehensive feature space, thereby enhancing the robustness of the features; and reducing the risk of misjudgment caused by the inaccuracy of a single feature, and improving the model's adaptability to small samples and complex environments, thereby achieving a significant improvement in authentication accuracy in a variety of scenarios, meeting the needs of high-security applications, and can be widely used in scenarios such as wireless access security, industrial network management, and self-organizing network protection.

[0118] Embodiment 2

[0119] The pre-construction method of the kNN classifier is as follows, refer to Figure 2 , Figure 2 A flowchart of a method for constructing a kNN classifier is provided:

[0120] Step 201: Collect communication signal data of a legitimate device, obtain the in-phase component I and the orthogonal component Q corresponding to the communication signal data frame leading sequence, and perform standardization processing to obtain standardized frame leading IQ data of the legitimate device.

[0121] Step 202: convert the standardized frame leading IQ data of the legal device, and calculate the nonlinear signal characteristic value of the legal device based on the converted IQ data.

[0122] Step 203: extract the conventional physical layer characteristic value of the legal device based on the standardized frame preamble IQ data of the legal device.

[0123] Step 204: construct and combine the nonlinear signal characteristic value of the legal device and the conventional physical layer characteristic value of the legal device to obtain a combined characteristic vector of the legal device.

[0124] In this embodiment, the method for obtaining the standardized frame preamble IQ data of the legal device is similar to step 101, the method for calculating the nonlinear signal eigenvalues ​​and the conventional physical layer eigenvalues ​​of the legal device is similar to steps 102 and 103, and the construction of the combined eigenvector of the legal device is similar to step 104, which will not be repeated here.

[0125] Step 205: assign corresponding legal identity labels to the combined feature vectors of legal devices to obtain legal target parameters.

[0126] In this embodiment, the communication signals of multiple legitimate devices are collected to obtain the combined feature vector v of each legitimate device; then, for the feature vector v of the i-th devicei , assign the corresponding legal identity tag y according to the identity information of the legal device i , each piece of data is d i =(v i ,y i ), that is, the legal target parameter is expressed as d i =(v i ,y i ).

[0127] Step 206: construct the legitimate target parameters of multiple legitimate devices into a target parameter data set, and construct a kNN classifier based on the target parameter data set.

[0128] If the number of legal devices collected is M, the M legal target parameters are combined into a target parameter data set D. The structure of the target parameter data set is defined as D = {d i |i=1,2,3,...,M}. Then, multiple target parameter data sets can be divided into training sets and test sets in proportion according to actual needs, for example, the ratio of training set to test set is 7:3.

[0129] Then the training set and test set are input into the kNN classifier, and the output of the kNN classifier is judged using common loss functions or evaluation indicators such as 0-1 loss function and cross entropy loss to obtain the constructed kNN classifier.

[0130] Embodiment 3

[0131] The embodiment of the present invention further provides an electronic device, such as Figure 3 As shown, it includes a processor 301, a communication interface 302, a memory 303 and a communication bus 304, wherein the processor 301, the communication interface 302, and the memory 303 communicate with each other through the communication bus 304.

[0132] Memory 303, used for storing computer programs;

[0133] The processor 301 is used to execute the program stored in the memory 303 to implement the following steps:

[0134] The communication signal data of the device to be authenticated is collected, the in-phase component I and the orthogonal component Q corresponding to its frame preamble sequence are obtained, and standardized processing is performed to obtain standardized frame preamble IQ data; the standardized frame preamble IQ data is converted into a target reference symbol, and the nonlinear signal characteristic value of the device to be authenticated is calculated based on the target reference symbol; based on the standardized frame preamble IQ data, the conventional physical layer characteristic value of the device to be authenticated is extracted; the nonlinear signal characteristic value and the conventional physical layer characteristic value are constructed and combined to obtain a combined characteristic vector to be authenticated; the distance between the combined characteristic vector to be authenticated and the characteristic vector of the legitimate device is calculated, and according to the distance, the identity authentication result of the device to be authenticated is obtained.

[0135] According to the identity authentication method and electronic device based on nonlinear signal characteristics of the embodiment of the present invention, when performing identity authentication, the communication signal data of the device to be authenticated is first collected, the in-phase component I and the orthogonal component Q corresponding to the frame preamble sequence are obtained, and standardized processing is performed to obtain standardized frame preamble IQ data; then the standardized frame preamble IQ data is converted into a target reference symbol, and the nonlinear signal characteristic value of the device to be authenticated is calculated based on the target reference symbol; in addition, based on the standardized frame preamble IQ data, the conventional physical layer characteristic value of the device to be authenticated is also extracted; then the nonlinear signal characteristic value and the conventional physical layer characteristic value are constructed and combined to obtain a combined characteristic vector to be authenticated; finally, the distance between the combined characteristic vector and the legal device characteristic vector is calculated, and the identity authentication result of the device to be authenticated is obtained according to the distance. This scheme introduces the nonlinear signal characteristics of the device to be authenticated, expands the signal characteristics of the device to be authenticated, increases the identity authentication dimension, and constructs and combines the nonlinear signal characteristic value and the conventional physical layer characteristic value to form a combined characteristic vector containing more information of the device to be authenticated, which can more comprehensively characterize the hardware characteristics of the device to be authenticated and improve the accuracy of identity authentication.

[0136] The communication bus mentioned in the above electronic device can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The communication bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, only one thick line is used in the figure, but it does not mean that there is only one bus or one type of bus.

[0137] The communication interface is used for communication between the above electronic device and other devices.

[0138] The memory may include a random access memory (RAM) or a non-volatile memory (NVM), such as at least one disk memory. Optionally, the memory may also be at least one storage device located away from the aforementioned processor.

[0139] The above-mentioned processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0140] The method provided in the embodiment of the present invention can be applied to electronic devices. Specifically, the electronic device can be: a desktop computer, a portable computer, an intelligent mobile terminal, a server, etc. This is not limited here, and any electronic device that can implement the present invention belongs to the protection scope of the present invention.

[0141] As for the electronic device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0142] As another aspect, an embodiment of the present invention further provides a computer-readable medium having a computer program stored thereon, which, when executed by a processor, implements the identity authentication method based on nonlinear signal features as described in the above embodiment.

[0143] It should be noted that the electronic device and readable medium of the embodiments of the present invention are respectively an electronic device and a readable medium to which the above-mentioned identity authentication method based on nonlinear signal characteristics is applied. All embodiments of the above-mentioned identity authentication method based on nonlinear signal characteristics are applicable to the electronic device and the readable medium, and can achieve the same or similar beneficial effects.

[0144] By using the terminal device provided by the embodiment of the present invention, proper nouns and / or fixed phrases can be displayed for user selection, thereby reducing user input time and improving user experience.

[0145] The terminal device exists in various forms, including but not limited to:

[0146] (1) Mobile communication devices: These devices are characterized by their mobile communication functions and their main purpose is to provide voice and data communications. These terminals include: smart phones (such as iPhone), multimedia phones, functional phones, and low-end phones.

[0147] (2) Ultra-mobile personal computer devices: These devices fall into the category of personal computers, have computing and processing capabilities, and generally also have mobile Internet access features. These terminals include: PDA, MID and UMPC devices, such as iPad.

[0148] (3) Portable entertainment devices: These devices can display and play multimedia content. They include audio and video players (such as iPods), handheld game consoles, e-books, smart toys, and portable car navigation devices.

[0149] (4) Other electronic devices with data interaction functions.

[0150] In the description of the present invention, “plurality” means two or more than two, unless otherwise clearly and specifically defined.

[0151] Although the present application is described herein in conjunction with various embodiments, in the process of implementing the claimed application, those skilled in the art may understand and implement other variations of the disclosed embodiments by viewing the drawings, the disclosure, and the appended claims. In the claims, the word "comprising" does not exclude other components or steps, and "one" or "an" does not exclude multiple situations. A single processor or other unit may implement several functions listed in a claim. Certain measures are recorded in different dependent claims, but this does not mean that these measures cannot be combined to produce good results.

[0152] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, devices (equipment), or computer program products. Therefore, the present application may adopt the form of complete hardware embodiments, complete software embodiments, or embodiments in combination with software and hardware, which are collectively referred to as "modules" or "systems" herein. Moreover, the present application may adopt the form of computer program products implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program codes. The computer program is stored / distributed in a suitable medium, provided together with other hardware or as a part of hardware, or may adopt other distribution forms, such as by Internet or other wired or wireless telecommunication systems.

[0153] The present application is described with reference to the flowcharts and / or block diagrams of the methods, apparatuses (devices) and computer program products of the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0154] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0155] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0156] The above contents are further detailed descriptions of the present invention in combination with specific preferred embodiments, and it cannot be determined that the specific implementation of the present invention is limited to these descriptions. For ordinary technicians in the technical field to which the present invention belongs, several simple deductions or substitutions can be made without departing from the concept of the present invention, which should be regarded as falling within the protection scope of the present invention.

Claims

1. An identity authentication method based on nonlinear signal characteristics, characterized in that: The method comprises: Collect the communication signal data of the device to be authenticated, obtain the in-phase component I and the orthogonal component Q corresponding to its frame preamble sequence, and perform standardization processing to obtain standardized frame preamble IQ data; Converting the standardized frame leading IQ data into target reference symbols, and calculating the nonlinear signal characteristic value of the device to be authenticated based on the target reference symbols; Extracting conventional physical layer feature values ​​of the device to be authenticated based on the standardized frame leading IQ data; Constructing and combining the nonlinear signal characteristic value and the conventional physical layer characteristic value to obtain a combined characteristic vector to be authenticated; The distance between the combined feature vector to be authenticated and the feature vector of the legal device is calculated, and the identity authentication result of the device to be authenticated is obtained according to the distance.

2. The method according to claim 1, characterized in that The nonlinear signal characteristic values ​​include correlation dimension CD, Lyapunov exponent LE, fractal dimension FD and Hurst exponent HE; The converting the standardized frame leading IQ data into a target reference symbol, and calculating the nonlinear signal characteristic value of the device to be authenticated based on the target reference symbol, comprises: Rotating the phase of the standardized frame leading IQ data to obtain a target reference symbol; The correlation dimension CD, the Lyapunov exponent LE, the fractal dimension FD and the Hurst exponent HE of the device to be authenticated are calculated based on the target reference symbol.

3. The method according to claim 2, characterized in that The target reference symbol includes the i-th sampling point S(i) of the frame preamble sequence and the j-th sampling point S(j) of the frame preamble sequence; The calculating the correlation dimension CD of the device to be authenticated based on the target reference symbol comprises: By formula Calculating the correlation dimension CD; Where Θ() represents the Heaviside step function, r k Indicates the preset threshold, L S(i),S(j) represents the Euclidean distance between S(i) and S(j), and N represents the number of sampling points of the frame preamble sequence.

4. The method according to claim 2, characterized in that: The target reference symbol includes the tth sampling point S(t) of the frame preamble sequence; The calculating the Lyapunov exponent LE of the device to be authenticated based on the target reference symbol comprises: By using the formula D(t)=[S(t),S(t+τ),…,S(t+(d-1)τ)], the frame preamble sequence is time-delayed embedded to obtain a high-dimensional embedding space sequence; Where d represents the embedding dimension, τ represents the time delay, and t represents the time index; By formula Calculate the logarithmic growth rate LE k (p,q); Among them, p represents the embedded point, q is the nearest neighbor of p, k represents the time step, L D(p),D(q) represents the Euclidean distance between D(p) and D(q); By formula Calculating the Lyapunov exponent LE; Wherein, m represents the maximum value of the time step k, and N represents the number of sampling points of the frame preamble sequence.

5. The method according to claim 2, characterized in that: The calculating the fractal dimension FD of the device to be authenticated based on the target reference symbol comprises: By formula Calculating the fractal dimension FD; Where τ is the interval size, L S(τ),S(n) represents the Euclidean distance between S(τ) and S(i), N represents the number of sampling points of the frame preamble sequence, S(τ) represents the τth sampling point of the frame preamble sequence, and S(u) represents the i-th sampling point of the frame preamble sequence.

6. The method according to claim 2, characterized in that The calculating the Hurst exponent HE of the device to be authenticated based on the target reference symbol includes: By formula Calculating the Hurst exponent HE; Where τ is the interval size, L S(τ),S(i) represents the Euclidean distance between S(τ) and S(i), S(τ) represents the τth sampling point of the frame preamble sequence, and S(i) represents the i-th sampling point of the frame preamble sequence.

7. The method according to claim 1, characterized in that The conventional physical layer characteristic values ​​include phase error PE, amplitude error AE and carrier frequency offset CFO; The constructing and combining the nonlinear signal characteristic value and the conventional physical layer characteristic value to obtain a combined characteristic vector to be authenticated includes: Normalizing the correlation dimension CD, the Lyapunov exponent LE, the fractal dimension FD, the Hurst exponent HE, the phase error PE, the amplitude error AE, and the carrier frequency offset CFO to obtain a dimensionless eigenvalue v; By the formula v = [v CD ,v LE ,v FD ,v HE ,v PE ,v AE ,v CFO ], merging the eigenvalues ​​v to construct a combined eigenvector to be authenticated; Among them, v CD represents the normalized correlation dimension CD, v LE represents the normalized Lyapunov exponent LE, v FD represents the normalized fractal dimension FD, v HE Represents the normalized Hurst exponent HE, v PF Represents the normalized phase error PE, v AE represents the normalized amplitude error AE, v CFO Indicates the normalized carrier frequency offset CFO.

8. The method according to claim 1, characterized in that The calculating the distance between the combined feature vector to be authenticated and the feature vector of the legal device, and obtaining the identity authentication result of the device to be authenticated according to the distance, includes: Calculate the distance between the combined feature vector to be authenticated and the feature vector of the legitimate device by using a pre-built kNN classifier; When the distance is less than a preset threshold, an identity authentication result indicating that the authentication is successful is output, otherwise an identity authentication result indicating that the authentication is failed is output.

9. The method according to claim 8, characterized in that The pre-construction of the kNN classifier includes: Collecting communication signal data of a legitimate device, obtaining an in-phase component I and a quadrature component Q corresponding to a frame leading sequence of the communication signal data, and performing standardization processing to obtain standardized frame leading IQ data of the legitimate device; Converting the standardized frame leading IQ data of the legal device, and calculating the nonlinear signal characteristic value of the legal device based on the converted IQ data; Extracting conventional physical layer feature values ​​of the legal device based on the standardized frame leading IQ data of the legal device; Constructing and combining the nonlinear signal characteristic value of the legal device and the conventional physical layer characteristic value of the legal device to obtain a combined characteristic vector of the legal device; Assigning a corresponding legal identity tag to the combined feature vector of the legal device to obtain a legal target parameter; The legitimate target parameters of multiple legitimate devices are constructed into a target parameter data set, and the kNN classifier is constructed based on the target parameter data set.

10. An electronic device, characterized in that: The device comprises: one or more processors; A computer readable medium configured to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the identity authentication method based on nonlinear signal features as described in any one of claims 1 to 9.

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