Equipment fingerprint extraction method based on reference demodulation signal

Abstract

The invention discloses an equipment fingerprint extraction method based on a reference demodulation signal, which comprises the following steps: after a receiver receives a broadband signal, down-converting the broadband signal into a baseband signal, extracting a frequency domain complex signal of reference demodulation, and removing a known local baseband sequence corresponding to the signal to obtain a frequency domain value; performing complex logarithm operation on the frequency domain value, and decentralizing to obtain a cepstrum signal; removing a high-frequency fingerprint component in the cepstrum signal by using discrete wavelet transform to obtain a low-frequency fingerprint component; converting the low-frequency fingerprint component into a time domain signal by using inverse Fourier transform; and carrying out segmented windowing operation on the time domain signal to obtain a signal after channel suppression, and carrying out Fourier transform on the signal to obtain frequency domain feature expression of the device fingerprint so as to complete extraction of the device fingerprint. According to the invention, radio frequency fingerprint extraction is carried out on the reference demodulation signal in broadband communication, hardware fingerprint features irrelevant to symbol sequences and robust in channels can be obtained, and identity recognition can be carried out on a broadband signal transmitter.

Description

Technical Field

[0001] The present invention relates to the field of information security technology, and in particular to a device fingerprint extraction method based on a reference demodulated signal. Background Art

[0002] RFF (Radio Frequency Fingerprint) technology is a technology that identifies and authenticates devices by analyzing hardware features in wireless transmission signals, such as frequency error, clock offset, modulation error, etc. The hardware RF features of each transmitter device vary due to manufacturing processes and environmental factors, thus forming a unique "fingerprint". Since RF fingerprints are unique and cannot be cloned, they can help identify potential attackers and unauthorized devices in wireless networks, thereby providing enhanced security in identity verification and security authentication.

[0003] Currently, identity authentication for LTE / 5G devices mainly uses the SIM (LTE) or USIM (5G) identity module card equipped with the terminal. When the terminal attempts to access the network, the network initiates an authentication request, and the network then sends a random number (RAND) and authentication parameters (AUTN). The terminal uses this information to generate an authentication response (RES) and sends it back to the network for identity authentication. This authentication process is concentrated above the network layer, and the verification mechanism is relatively complex, so it is necessary to design a lightweight wireless device fingerprint extraction method. Summary of the invention

[0004] The purpose of the present invention is to provide a device fingerprint extraction method based on a reference demodulation signal, which performs radio frequency fingerprint extraction on the reference demodulation signal in broadband communications such as LTE / 5G, and can obtain hardware fingerprint features that are independent of the symbol sequence and robust to the channel; it can perform lightweight identity recognition on LTE / 5G terminals for network access authentication of terminal users.

[0005] The present invention adopts the following technical solution:

[0006] A device fingerprint extraction method based on a reference demodulated signal comprises the following steps:

[0007] S1. After receiving the broadband signal, the receiver down-converts it to a baseband signal and extracts the reference demodulated frequency domain complex signal, removes the known local baseband sequence corresponding to the signal, and obtains the frequency domain value.

[0008] S2. Perform complex logarithm operation on the frequency domain value and decenter it to obtain a logarithmic spectrum signal.

[0009] S3. Use discrete wavelet transform to remove high-frequency fingerprint components in the log spectrum signal to obtain low-frequency fingerprint components.

[0010] S4. Use inverse Fourier transform to convert the low-frequency fingerprint component into a time domain signal.

[0011] S5. Perform segmented windowing operation on the time domain signal to obtain a signal after channel suppression.

[0012] S6. Perform Fourier transform on the signal in step S5 to obtain the frequency domain feature expression of the device fingerprint, thereby completing the extraction of the device fingerprint.

[0013] Furthermore, in step S1, the frequency domain value obtained includes the following contents:

[0014] Reference demodulated frequency domain signal Y rx (z) satisfies the following relationship with the local baseband sequence Y(z):

[0015] Y rx (z) = Y(z)·H tx (z)·H c (z)·H rx (z)+δ(z)

[0016] Among them, H c (z) represents the channel state information, H tx (z) represents the filter response of the transmitter, H rx (z) represents the filter response of the receiver, and δ(z) represents the frequency domain expression of Gaussian white noise.

[0017] The calculation formula of frequency domain value is:

[0018]

[0019] Wherein, R(z) represents the frequency domain value.

[0020] Furthermore, in step S2, obtaining the logarithmic spectrum signal includes the following:

[0021] The calculation formula of the log spectrum signal is:

[0022]

[0023] Among them, R L (z) represents the log spectrum signal; H tr (z) represents the hardware-related component of the transmitter terminal, H tr (z) = H tx (z)·H rx (z).

[0024] Furthermore, in step S3, obtaining the low-frequency fingerprint component includes the following:

[0025] The high-frequency fingerprint component in the log spectrum signal is After removal, the low-frequency fingerprint component V(z) is obtained. The specific formula is:

[0026] V(z)=log(H c (z))+log(H tr (z)).

[0027] Furthermore, in step S4, the specific formula for obtaining the time domain signal is:

[0028] v(n)=ifft(V(z))=ifft(log(H c (z)))+ifft(log(H tr (z)))

[0029] Among them, v(n) represents the time domain signal, and ifft() represents the inverse Fourier transform.

[0030] Further, in step S5, obtaining the signal after channel suppression includes the following contents:

[0031] Set K continuous rectangular windows w(n) of length L and a time domain signal v(n) of length K×L; w(n) is a value of all 0 or all 1, that is, w 0 (n) or w 1 (n).

[0032] The kth continuous rectangular window w k The type of (n) is determined by the kth time domain signal v k (n) The variance of the value D(v k (n)) is determined, and the specific expression is:

[0033]

[0034] Among them, μ 1 Represents the empirical threshold of variance.

[0035] The expression of the signal r(n) after channel suppression is:

[0036] r(n)=[v 1 (n) ·w 1 (n),...,v k (n) ·w k (n),...,v K (n) ·w K (n)]

[0037] Among them, v K (n) represents the Kth time domain signal, w K (n) represents the Kth consecutive rectangular window.

[0038] Furthermore, in step S6, the specific formula for expressing the frequency domain feature of the device fingerprint is:

[0039] RFF(z)=fft(r(n))

[0040] Among them, RFF(z) represents the frequency domain feature expression of device fingerprint, and fft() represents Fourier transform.

[0041] Furthermore, the present invention also proposes an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the device fingerprint extraction method based on the reference demodulated signal when executing the computer program.

[0042] Furthermore, the present invention also proposes a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the device fingerprint extraction method based on a reference demodulated signal is executed.

[0043] Compared with the prior art, the present invention adopts the above technical solution and has the following technical effects:

[0044] The method proposed in the present invention is easy to implement, has fast calculation, low cost, and high stability. The acquired radio frequency fingerprint has data and channel robustness, and can realize secure access authentication for LTE / 5G terminals. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 It is an overall implementation flow chart of the present invention.

[0046] Figure 2 It is a constellation diagram for receiving an LTE uplink reference demodulation signal according to an embodiment of the present invention.

[0047] Figure 3 It is a constellation diagram of a theoretical LTE uplink reference demodulation signal according to an embodiment of the present invention.

[0048] Figure 4 It is a comparison diagram before and after the discrete wavelet is used to remove the high-frequency components of the imaginary / real part signals of the LTE DMRS log spectrum signal according to an embodiment of the present invention.

[0049] Figure 5 It is a time domain signal diagram of v(n) under multiple subframes of an embodiment of the present invention.

[0050] Figure 6 This is the frequency domain fingerprint map finally extracted from the seven LTE terminals in the embodiment of the present invention. DETAILED DESCRIPTION

[0051] The present invention will be further described below in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and cannot be used to limit the protection scope of the present invention.

[0052] DMRS (Demodulation Reference Signal) is widely used in the physical layer of wireless mobile communications, especially in LTE and 5G communication systems. Due to its good autocorrelation characteristics and easy access to local demodulation sequences, it can help the receiver estimate channel characteristics and help the user equipment (UE) perform time and frequency synchronization when the system is accessed to ensure that the received signal can be correctly demodulated. Therefore, by obtaining the reference demodulation signal in mobile communications and extracting the RF fingerprint information in the LTE / 5G terminal, it can be used to enhance the network security access of the LTE / 5G terminal.

[0053] To achieve the above object, the present invention proposes a device fingerprint extraction method based on a reference demodulated signal, such as Figure 1 As shown, the specific steps are as follows:

[0054] S1. After receiving the LTE / 5G signal, the receiver down-converts it to the baseband signal and extracts the reference demodulated frequency domain complex signal. In order to remove the influence of the random sequence on the device fingerprint, the known local baseband sequence corresponding to the reference demodulated frequency domain complex signal is removed to obtain the frequency domain value. The specific contents are as follows:

[0055] Reference demodulated frequency domain signal Y rx (z) satisfies the following relationship with the local baseband sequence Y(z):

[0056] Y rx (z) = Y(z)·H tx (z)·H c (z)·H rx (z)+δ(z)

[0057] Among them, H c (z) represents the channel state information, H tx (z) represents the filter response of the transmitter, H rx (z) represents the filter response of the receiver, and δ(z) represents the frequency domain expression of Gaussian white noise.

[0058] H tx (z) can be considered as a fingerprint feature related to the transmitter hardware.

[0059] The calculation formula of frequency domain value is:

[0060]

[0061] Wherein, R(z) represents the frequency domain value.

[0062] From formula (1), we can see that Y(z) is not removed from R(z), and relative to the numerator high-frequency noise δ(z), the denominator H tr (z)·H c (z)·Y(z) is a low-frequency component. Therefore, the overall is the high frequency component in R(z), and H tr (z) and H c (z) is the low-frequency component in R(z).

[0063] Figure 2 is a constellation diagram of a reference demodulation signal of an LTE terminal. In this embodiment, a Zadoff-Chu (ZC) sequence is used in the LTE uplink signal. Figure 3 is the corresponding ideal signal constellation diagram.

[0064] S2. Perform complex logarithm operation on the frequency domain value and decentralize it to obtain the logarithmic spectrum signal. The specific calculation formula is:

[0065]

[0066] Among them, R L (z) represents the log spectrum signal; H tr (z) represents the hardware-related component of the transmitter terminal, H tr (z) = H tx (z)·H rx (z).

[0067] It can be seen from formula (2) that the multiplicative relationship of different frequency components is transformed into an additive relationship, which is conducive to the separation of the above components.

[0068] In this embodiment, only when the receiver is unique, the receiver fingerprint feature H is ignored. rx (z), that is, H tr (z) is the fingerprint representation of the transmitter.

[0069] S3, using discrete wavelet transform to remove the high-frequency fingerprint component in the log spectrum signal. In this embodiment, discrete wavelet transform is used to transform the signal R L (z) Perform three-layer decomposition calculation, and perform wavelet reconstruction after soft threshold quantization on the high-frequency coefficients of each layer to obtain the low-frequency fingerprint component. The specific formula is:

[0070] V(z)=log(H c (z))+log(H tr (z))(3)

[0071] The low-frequency fingerprint components are data-irrelevant components.

[0072] Figure 4 The comparison diagram of the imaginary / real part of the LTE DMRS log spectrum signal before and after the discrete wavelet removal of high-frequency components is shown. It can be seen that after removing the high-frequency components, R L (z) The high-frequency components caused by random data and noise in the signal are effectively eliminated, while the RF fingerprint, which mainly exists in the low-frequency part of the signal, is retained after denoising after wavelet transform.

[0073] S4. In order to further eliminate the channel response component log(H c (z)), the low-frequency fingerprint component is converted into a time domain signal using inverse Fourier transform. The specific calculation formula is:

[0074]

[0075] Among them, v(n) represents the time domain signal, and ifft() represents the inverse Fourier transform.

[0076] Figure 5 The figure shows the time domain signal diagram of v(n) in multiple subframes in a mobile scenario. It can be seen from the figure that the channel components ifft(log(H c The change of (z))) mainly affects the stability of the left and right ends of the time domain fingerprint, while the area in the middle of the dotted line is less affected, that is, the representation of the fingerprint is more stable.

[0077] S5, performing segmented windowing operation on the time domain signal to obtain a signal after channel suppression; the specific content is:

[0078] Set K continuous rectangular windows w(n) of length L and a time domain signal v(n) of length K×L; w(n) is a value of all 0 or all 1, that is, w 0 (n) or w 1 (n).

[0079] The kth continuous rectangular window w k The type of (n) is determined by the kth time domain signal v k (n) The variance of the value is determined, and the specific expression is:

[0080]

[0081] Where D() represents the variance calculation function; μ 1 Represents the empirical threshold of variance, obtained through multiple experiments.

[0082] The expression of the signal r(n) after channel suppression is:

[0083] r(n)=[v 1 (n) ·w 1 (n),...,v k(n) ·w k (n),...,v K (n) ·w K (n)](6)

[0084] Among them, v K (n) represents the Kth time domain signal, w K (n) represents the Kth consecutive rectangular window.

[0085] At this time, the area with greater channel influence can be suppressed, and the area with more stable fingerprint can be retained. 1 The parameters of , K and L can be obtained by cross-validation of the training set and the validation set. When the validation set obtains the maximum recognition rate, the values ​​of the above parameters are determined.

[0086] S6. Perform Fourier transform on the signal in step S5 to obtain the frequency domain feature expression of the device fingerprint, and complete the extraction of the device fingerprint. The specific formula is:

[0087] RFF(z)=fft(r(n))(7)

[0088] Among them, RFF(z) represents the frequency domain feature expression of device fingerprint, and fft() represents Fourier transform.

[0089] Figure 6 The amplitude curves of the fingerprint r(n) extracted from 7 LTE devices are shown. It can be seen from the figure that different devices have obvious differences and can be used to distinguish different transmitter terminals.

[0090] The embodiment of the present invention further provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. It should be noted that when the processor executes the computer program, the specific steps of the method provided in the embodiment of the present invention are corresponding to the specific steps of the method provided in the embodiment of the present invention, and the processor has the functional modules and beneficial effects corresponding to the execution method. For technical details not described in detail in this embodiment, please refer to the method provided in the embodiment of the present invention.

[0091] The embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program. It should be noted that when the computer program is executed by the processor, it corresponds to the specific steps of the method provided in the embodiment of the present invention, and has the functional modules and beneficial effects corresponding to the execution method. For technical details not described in detail in this embodiment, please refer to the method provided in the embodiment of the present invention.

[0092] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A device fingerprint extraction method based on a reference demodulated signal, characterized in that: include: S1. After receiving the broadband signal, the receiver down-converts it to a baseband signal and extracts the reference demodulated frequency domain complex signal, removes the known local baseband sequence corresponding to the signal, and obtains the frequency domain value; S2, performing complex logarithm operation on the frequency domain value and decentralizing it to obtain a logarithmic spectrum signal; S3, using discrete wavelet transform to remove high-frequency fingerprint components in the log spectrum signal to obtain low-frequency fingerprint components; S4, using inverse Fourier transform to convert the low-frequency fingerprint component into a time domain signal; S5, performing a segmented windowing operation on the time domain signal to obtain a signal after channel suppression; S6. Perform Fourier transform on the signal in step S5 to obtain the frequency domain feature expression of the device fingerprint, thereby completing the extraction of the device fingerprint.

2. The device fingerprint extraction method based on reference demodulation signal according to claim 1 is characterized in that: In step S1, the frequency domain value obtained includes the following contents: Reference demodulated frequency domain signal Y rx (z) satisfies the following relationship with the local baseband sequence Y(z): Y rx (z)=Y(z)·H tx (z) H c (z) H rx (z)+δ(z) Among them, H c (z) represents the channel state information, H tx (z) represents the filter response of the transmitter, H rx (z) represents the filter response of the receiver, and δ(z) represents the frequency domain expression of Gaussian white noise; The calculation formula of frequency domain value is: Wherein, R(z) represents the frequency domain value.

3. The device fingerprint extraction method based on reference demodulation signal according to claim 1 is characterized in that: In step S2, obtaining the logarithmic spectrum signal includes the following steps: The calculation formula of the log spectrum signal is: Among them, R L (z) represents the log spectrum signal; H c (z) represents the channel state information; H tr (z) represents the hardware-related component of the transmitter terminal, H tr (z) = H tx (z)·H rx (z), H tx (z) represents the filter response of the transmitter, H rx (z) represents the filter response of the receiver; Y(z) represents the local baseband sequence; δ(z) represents the frequency domain expression of Gaussian white noise.

4. The device fingerprint extraction method based on reference demodulation signal according to claim 1 is characterized in that: In step S3, obtaining the low-frequency fingerprint component includes the following: The high-frequency fingerprint component in the log spectrum signal is After removal, the low-frequency fingerprint component V(z) is obtained. The specific formula is: V(z)=log(H c (z))+log(H tr (z)) Among them, H c (z) represents the channel state information, H tr (z) represents the hardware-related component of the transmitter terminal, and Y(z) represents the local baseband sequence.

5. The device fingerprint extraction method based on reference demodulation signal according to claim 1 is characterized in that: In step S4, the specific formula for obtaining the time domain signal is: v(n)=ifft(V(z)) Among them, v(n) represents the time domain signal, ifft() represents the inverse Fourier transform, and V(z) represents the low-frequency fingerprint component.

6. The device fingerprint extraction method based on reference demodulation signal according to claim 1 is characterized in that: In step S5, obtaining the signal after channel suppression includes the following contents: Set K continuous rectangular windows w(n) of length L and a time domain signal v(n) of length K×L; w(n) is a value of all 0 or all 1, that is, w 0 (n) or w 1 (n); The kth continuous rectangular window w k The type of (n) is determined by the kth time domain signal v k (n) The variance of the value D(v k (n)) is determined, and the specific expression is: Among them, μ1 represents the empirical threshold of variance; The expression of the signal r(n) after channel suppression is: r(n)=[v1(n)·w1(n),...,v k (n)·w k (n),...,v K (n)·w K (n)] Among them, v K (n) represents the Kth time domain signal, w K (n) represents the Kth consecutive rectangular window.

7. The device fingerprint extraction method based on reference demodulation signal according to claim 1 is characterized in that: In step S6, the specific formula for expressing the frequency domain feature of the device fingerprint is: RFF(z)=fft(r(n)) Among them, RFF(z) represents the frequency domain feature expression of device fingerprint, and fft() represents Fourier transform.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the device fingerprint extraction method based on the reference demodulated signal according to any one of claims 1 to 7 are implemented.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the device fingerprint extraction method based on a reference demodulated signal according to any one of claims 1 to 7 is executed.

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