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CN-116075746-B - Computer-implemented method, data processing device, computer program product and computer-readable storage medium for detecting global navigation satellite system signal spoofing

CN116075746BCN 116075746 BCN116075746 BCN 116075746BCN-116075746-B

Abstract

A computer-implemented method for detecting Global Navigation Satellite System (GNSS) signal spoofing. The method comprises storing (120) at the GNSS receiver a sample sequence of a predictable portion and an unpredictable portion of the GNSS signal, wherein the predictable portion comprises predictable bits and the unpredictable portion comprises unpredictable bits, verifying (125) a value from which unpredictable bits of the unpredictable sample sequence are extracted, calculating (130) a first and a second partial correlation between the unpredictable, respectively predictable sample sequence and a locally stored replica of the GNSS signal, calculating (140) a predefined metric from the composite valued partial correlation, and comparing (150) the predefined metric with a predefined threshold. In a zero-delay reproduction attack, the spoofer must estimate the unpredictable bits introduced by the GNSS authentication protocol and thereby introduce distortion into the signal. The method detects such distortion to indicate whether the signal being analyzed is spoofed or authentic.

Inventors

  • D. Gomescasco
  • G. Seco Granados
  • J. A. Lopez Salcedo
  • I. Fernandez

Assignees

  • 欧盟由欧盟委员会为代表

Dates

Publication Date
20260508
Application Date
20210712
Priority Date
20200731

Claims (14)

  1. 1. A computer-implemented method (100) for detecting global navigation satellite system, GNSS, signal spoofing, the method comprising: a) Digitizing, acquiring and tracking (110) at a receiver GNSS signals from at least one GNSS satellite, the GNSS signals comprising a predictable portion and an unpredictable portion, wherein the predictable portion comprises predictable bits and the unpredictable portion comprises unpredictable bits; b) Storing (120), by the receiver, a sequence of samples of the predictable portion of the GNSS signal And a sample sequence of said unpredictable part ; C) Validating (125), by the receiver, a value of the unpredictable bit from which the unpredictable sample sequence was extracted; d) Calculating (130), by the receiver, a first partial correlation between the unpredictable sample sequence and a locally stored replica of the GNSS signal by And a second partial correlation between the sequence of predictable samples and the locally stored replica of the GNSS signal : And (C) sum And removing (134) signs of the first partial correlation, And removing (134) signs of the second partial correlation, Wherein Is the value of the unpredictable bit; e) -calculating (140), by the receiver, a predefined metric R 3 from the first and second partial correlations, the predefined metric R 3 being: And F) The predefined metric is compared (150) with a predefined threshold to detect GNSS signal spoofing.
  2. 2. The method according to claim 1, wherein step b) comprises: sample sequence of beginning part of unpredictable bit Stored as unpredictable sample sequences And sequence of samples of a subsequent portion of the unpredictable bits Stored as a sequence of predictable samples Either (or) Sample sequence of beginning part of unpredictable bit Stored as unpredictable sample sequences And sequences of samples of predictable bits Stored as a sequence of predictable samples 。
  3. 3. The method according to claim 1 or 2, wherein Is the duration of a single stored unpredictable sample sequence, an Is the duration of a single stored predictable sample sequence.
  4. 4. A method according to claim 3, wherein And/or Greater than 0.05 ms and less than 1 ms.
  5. 5. The method according to claim 1, wherein step b) comprises storing a sequence of samples representing at least 50 bits of the unpredictable samples and/or a portion of the predictable samples.
  6. 6. The method of claim 1, wherein the predefined threshold is based on a cumulative density function of a metric R 3 under the assumption that the GNSS signal is authentic Wherein Is the detection threshold and H 0 is the null hypothesis.
  7. 7. The method of claim 6, wherein the predefined threshold is set to a value that results in a false alarm probability of 0.02.
  8. 8. The method of claim 1, wherein step f) includes authenticating the GNSS signal when no signal spoofing is detected by: Authenticating (152) the GNSS signal when a predefined metric of the GNSS signal is below the predefined threshold, and -Detecting (154) GNSS signal spoofing when a predefined metric of the GNSS signal is above the predefined threshold.
  9. 9. The method of claim 1, wherein step a) comprises receiving GNSS signals from at least four different GNSS satellites, the GNSS signals comprising a spreading code and satellite data, the satellite data comprising the unpredictable portion, and wherein the method further comprises: g) Calculating (160) by the receiver the time of arrival of the GNSS signal from the spreading code, and H) The position, velocity and time of the satellite data are calculated (170) by the receiver by demodulating it.
  10. 10. The method of claim 9, wherein step f) includes authenticating the GNSS signal when no signal spoofing is detected by: Authenticating (152) the GNSS signal when a predefined metric of the GNSS signal is below the predefined threshold, and Detecting (154) to GNSS signal spoofing when a predefined measure of the GNSS signal is above the predefined threshold, and Wherein steps g) and h) are only performed when at least four GNSS signals from at least four different GNSS satellites have been authenticated.
  11. 11. The method of claim 1, wherein step b) includes storing the sequence of samples of the unpredictable portion of the GNSS signal based on randomly selected unpredictable bits Either (or) Wherein step d) comprises calculating a first partial correlation between the unpredictable sample sequence and a locally stored replica x (n) of the GNSS signal based on a randomly selected subset of the unpredictable sample sequence 。
  12. 12. A data processing apparatus comprising means for performing the method of any one of claims 1 to 11.
  13. 13. A computer program product comprising instructions which, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 11.
  14. 14. A computer readable storage medium comprising instructions which, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 11.

Description

Computer-implemented method, data processing device, computer program product and computer-readable storage medium for detecting global navigation satellite system signal spoofing Technical Field The present invention relates to a computer-implemented method for detecting Global Navigation Satellite System (GNSS) signal spoofing. The invention also relates to a data processing device for performing the method, as well as to a computer program product and a computer-readable storage medium, both comprising instructions for the method. Background Global Navigation Satellite System (GNSS) spoofing attacks are a type of intentional interference aimed at manipulating the position, velocity, and time (PVT) of a target GNSS receiver. Galileo has recently adopted the Open Service Navigation Message Authentication (OSNMA) function (fernan de-elnan de, i., rijmen, v., seco-Granados, g., simon, j., rodriguez, i., calle, j.d. (2016)). Navigation information authentication advice for galileo open services. Academy of navigation (spring), pages 85-102). In this function, the E1B signal component transmitted from the Galileo satellite includes unpredictable bits in order to allow the GNSS receiver to detect spoofing attacks. Spoofing attacks are disclosed in the IEEE aerospace and electronic systems journal 49, phase 2 (2013): 1073-1090, by hummphreys, todd e. More specifically, a Security Code Estimation and Reproduction (SCER) attack is disclosed that includes two steps. First, the spoofer tracks the signals received from the GNSS satellites and estimates the value of the unpredictable bits for each satellite in view. Second, the spoofer generates a set of GNSS signals that are transmitted to the target GNSS receiver to control the tracking loop and ultimately the user position. Generating SCER attacks is far from a simple task for the spoofer, since the spoofed signal must be synchronized with the real signal. If the two signals are not aligned with each other in the time domain when the spoofer starts an attack, the problem can be detected at the receiver by using the target receiver clock. This is because the stability of the receiver clock is well known and high variations in clock skew over short periods of PVT phases are known to be side effects that may be caused by spoofers. Thus, to perform SCER attacks and not be detected by the receiver clock, the spoofer may perform a zero delay attack, which is based on transmitting a signal that is actually synchronized with the true signal received by the target receiver. By doing so, the spoofer can control the target receiver. Fernan de-elnan de, igna sie and Gong Saluo-seco-glanardos (fernandez-Hern-ndez, ignacio and Gonzalo Seco-Granados). "Galileo NMA signal unpredictability and anti-replay protection" International positioning and GNSS conference (ICL-GNSS) of 2016, IEEE, 6/28 of 2016, proposes the use of Navigation Message Authentication (NMA) to prevent replay attacks. In this approach, the receiver stores a first sample of each unpredictable bit, creating a sequence whose correlation gain will be low if the tracked signal is reproduced by a spoofer. In other words, this method measures the gain drop when unpredictable bits are tracked. There is a brief suggestion in this disclosure that the gain based on the unpredictable sequence is compared with the gain based on the predictable sequence as test statistics for detecting zero-delay attacks, but no detection probability is disclosed for such test statistics. US 2011/102259 A1 discloses a method for combating GNSS fraud by triggering an indicator when an outlier is identified, such as a GNSS bit flip or an unexpected signal correlation curve. Other methods for detecting GNSS signal spoofing are also known in the art, such as disclosed in US 7,956,803 and EP 3495848A1, which rely on comparing GNSS signals with information obtained from alternative sources. US 7,956,803 discloses a method for detecting GNSS signal spoofing. The method includes providing information to the wireless device that allows the wireless device to determine a navigation data message from a reference network. The method also includes receiving navigation data from the GNSS network and comparing the navigation data from the GNSS network with navigation data derived from the reference network to determine whether one or more GNSS signals have been spoofed. EP 3495848A1 discloses a method of detecting GNSS signal spoofing by comparing a first GNSS signal with a second non-GNSS signal and using a threshold to detect signal spoofing. Disclosure of Invention It is an object of the present invention to provide an improved method of detecting GNSS signal fraud, in particular zero delay SCER attacks. According to the invention, this object is achieved by a computer-implemented method for detecting Global Navigation Satellite System (GNSS) signal spoofing, comprising a) digitizing, acquiring and tracking GNSS signals from at least one GNSS satellite at