CN114153227A - Unmanned aerial vehicle cluster key extraction and security authentication method based on GPS (Global positioning System) signals - Google Patents
Unmanned aerial vehicle cluster key extraction and security authentication method based on GPS (Global positioning System) signals Download PDFInfo
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Abstract
The invention relates to an unmanned aerial vehicle cluster key extraction and security authentication method based on GPS signals, and belongs to the technical field of data information processing. The method mainly comprises an information extraction stage and an equipment authentication stage, wherein during initialization, each node in a cluster system generates similar sequence information by using an artificial intelligence technology according to a GPS signal acquired by the node; generating unified information through a fuzzy extractor; finally, generating a session key according to the unified information; the interactive information is encrypted by the session key between the clusters to ensure the communication safety, and in the flight process, the clusters extract the key again according to the GPS signal at intervals to realize the key updating. According to the scheme, the generation of the initial keys on all the unmanned aerial vehicles in the cluster can be realized only through the public channel, and the problem of insufficient randomness of the keys can be well solved. In addition, the authentication mechanism based on the GPS signal in the scheme can also be applied to the scenes of other unmanned equipment ad hoc networks, and has good expansibility and commercial value.
Description
Technical Field
The invention belongs to the technical field of data information processing, and relates to an unmanned aerial vehicle cluster key extraction and security authentication method based on GPS signals.
Background
The unmanned aerial vehicle has been widely used in military and civil fields because of its advantages of low cost, high maneuverability, easy carrying, rapid deployment, convenient use, strong timeliness and the like. Due to the self-organizing characteristic of the unmanned aerial vehicle communication network, the unmanned aerial vehicle communication network is extremely easy to be interfered and hacked, and the safety of unmanned aerial vehicle communication is threatened.
At present, the common communication security aiming at the unmanned aerial vehicle cluster usually adopts an encryption communication process, and in order to ensure the communication security, the unmanned aerial vehicle needs to hold the same key in advance, namely the encryption key needs to be distributed in advance. The solutions proposed so far are: symmetric key distribution, public key encryption, password-based authenticated key exchange schemes, and the like.
The prior related art has obvious limitations: (1) symmetric key distribution: a secure channel is required for key transmission to ensure that the preset key is not compromised during distribution. In practical scenarios, due to low security awareness of users, the preset key is often used multiple times or lacks sufficient randomness. And moreover, the key updating is lacked, and once the key is leaked, the cluster information in the whole flight process is leaked. (2) Public key encryption: a secure channel is not required, but computation and storage overhead is large and efficiency is low. (3) Password-based authenticated key exchange scheme: the problem of insufficient randomness of the preset key is solved, but the scheme still needs to put the password into each unmanned aerial vehicle in advance, and communication overhead is extremely high due to the fact that multiple rounds of interaction are needed in the operation process of the scheme.
Disclosure of Invention
In view of the above, an object of the present invention is to provide a method for extracting keys and performing security authentication on a cluster of unmanned aerial vehicles based on GPS signals, where the method can generate initial keys on all unmanned aerial vehicles in the cluster only through a public channel, and the extracted keys have sufficient information entropy, so as to solve the problem of insufficient randomness of the keys.
In order to achieve the purpose, the invention provides the following technical scheme:
a unmanned aerial vehicle fleet key based on GPS signal extracts and the safe authentication method, the method mainly includes information extraction stage and equipment authentication stage, include 1) while initializing, every node in the fleet system utilizes artificial intelligence technology to produce the similar sequence information according to GPS signal that self gathers; 2) generating unified information by a Fuzzy Extractor (Fuzzy Extractor); 3) finally, generating a session key according to the unified information; the interactive information is encrypted by the session key between the clusters to ensure the communication safety, and in the flight process, the clusters extract the key again according to the GPS signal at intervals to realize the key updating.
Further, the method specifically comprises the following steps: s1, feature extraction: through GPS data receiving processing and an artificial intelligence algorithm, the cluster receives GPS signal data to obtain a sequence with similarity, and data features with similarity are extracted to serve as authentication features; s2, fuzzy extraction: the fuzzy extractor is adopted to further process the characteristic data obtained in the step S1, so that all unmanned aerial vehicles in the cluster obtain the same information; s3, device authentication: the interactive information is encrypted by the session key between the clusters to ensure the communication safety, and in the flight process, the clusters extract the key again according to the GPS signal at intervals to realize the key updating.
Further, in step S1, the method specifically includes the following steps:
s11, GPS signal receiving and analyzing: using an unmanned aerial vehicle antenna and a GPS receiver on the unmanned aerial vehicle, transmitting a received GPS message back to a controller in the unmanned aerial vehicle for processing, and analyzing the message of a GPS signal by an internal processor of the unmanned aerial vehicle; the GPS signal message complies with the NMEA protocol, the message contains the current coordinate, the timestamp and the information of the satellite, the timestamp, the satellite PRN number and the carrier-to-noise ratio signal of the satellite are analyzed by the unmanned aerial vehicle processor and stored in the internal storage device;
s12, GPS data processing: according to the distance between the unmanned aerial vehicle clusters, in the same period of time, the change conditions of the same GPS signal obtained by different unmanned aerial vehicles are analyzed and compared in similarity, and the signal intensity data of each satellite in a period of time, which is directly received, is subjected to data normalization by a statistical method to form a uniform data sequence;
s13, data feature extraction: according to the comparison data obtained among the unmanned aerial vehicles, a machine learning algorithm is utilized to extract the features of the data, the similarity features of the data in a certain range are obtained, and when the distances are close, the fluctuation changes of the data collected among the multiple devices present the similarity features; and after the feature extraction is finished, new received data are used for obtaining a data sequence through data processing, and verification is carried out according to the similarity features of the data sequence.
Further, in step S2, the fuzzy extractor is used for a cryptographic tool for extracting uniformly distributed and accurately reproducible random bits from a random source with noise, which comprises two algorithms:
A. gen (w) → (P, R): generating an algorithm Gen input character string w (a sampling of a noise random source), and outputting a character string R and an open auxiliary string P;
B. rep (w ', P) → R': regeneration algorithm input w '(another sample of the noise random source) and public auxiliary string P, output a string R';
the fuzzy extractor requires that when the input string w is sufficiently close to the regeneration algorithm input w ', the character string R' regenerated by the regeneration algorithm is exactly the same as the character R generated.
Further, the public auxiliary string P is generated by the central machine and broadcasted to surrounding unmanned aerial vehicles, the surrounding unmanned aerial vehicles extract features through self GPS signals to generate an input character string w', and fuzzy extraction is performed to obtain a character string R in combination with the auxiliary string P.
Further, in step S3, the method specifically includes:
A. an initialization stage: before the unmanned aerial vehicle takes off, all unmanned aerial vehicles in the cluster are in a certain range of the central machine, all unmanned aerial vehicles execute an information extraction algorithm, namely initial information R0 (namely a character string R) is obtained through GPS signal analysis, feature extraction and fuzzy extraction, and all unmanned aerial vehicles take R0 as an initial key K0;
C. a flight phase: during the flight, the fleet executes an information extraction algorithm at certain time intervals and generates a new communication key: assuming that at time t, the information extracted by the unmanned aerial vehicle is Rt, at time t +1, the communication key used by the cluster is: kt +1 ═ Kt ≦ Rt ≦ R0 ≦ R1 ≦ … ≦ Rt, which represents an exclusive-or operation;
C. and (3) outlier return: assuming that a unmanned aerial vehicle leaves the cluster at n moments to execute tasks in the flight process, the process of reacquiring the communication key during return flight is as follows: when returning to the air, the outlier holds an outlier communication key Kn, firstly, the outlier sends Kn to a central machine through an identity authentication scheme, the central machine receives the Kn and returns information Rn from n to t-1 after confirmation, wherein the information Rn is (t-1) ═ Rn +1 and at the moment of n to t-1, (t-1) ═ Rn +2 ^ Rt-1 to the outlier, the outlier receives the Rn, and after (t-1), Rt is obtained through calculation according to a GPS signal, and finally, the current intra-cluster communication key Kt is obtained according to the Kn, Rn, (t-1) & gt Rt.
The invention has the beneficial effects that:
because the unmanned aerial vehicle in the cluster only needs the GPS signal acquired by the unmanned aerial vehicle and the public information from the central machine when generating the key, the scheme avoids the problem of key distribution during initialization, and can realize the generation of the initial key on all the unmanned aerial vehicles in the cluster only through a public channel. On the other hand, the characteristics of the fuzzy extractor and the randomness of the GPS signals also ensure that the extracted key has enough information entropy, and the problem of insufficient randomness of the key is solved. In addition, due to the universality of the ad hoc network in the scenes of the internet of vehicles, the internet of things and the like, the authentication mechanism based on the GPS signal in the scheme can be applied to the cluster of the unmanned aerial vehicle and can also be applied in the scenes of other ad hoc networks of the unmanned aerial vehicle, and the authentication mechanism has good expansibility and commercial value.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objectives and other advantages of the invention may be realized and attained by the means of the instrumentalities and combinations particularly pointed out hereinafter.
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For the purposes of promoting a better understanding of the objects, aspects and advantages of the invention, reference will now be made to the following detailed description taken in conjunction with the accompanying drawings in which:
FIG. 1 is a schematic flow chart of the present invention;
FIG. 2 is a result of similarity of satellite signals measured for 2 meters in the example;
fig. 3 shows the similarity result of the satellite signals measured for 70 meters in the example.
Detailed Description
The technical scheme of the invention is explained in detail in the following with the accompanying drawings.
Fig. 1 is a schematic flow chart of the present invention, which proposes a GPS signal-based drone swarm authentication communication mechanism. During initialization, each node in the system generates similar sequence information by using an artificial intelligence technology according to the GPS signal acquired by the node. The unified information is then generated by a Fuzzy Extractor (Fuzzy Extractor). And finally, generating the session key according to the unified information. The cluster encrypts the mutual information through the session key to ensure the communication security. In the flight process, the cluster extracts the key again according to the GPS signal at intervals to realize key updating.
As shown in the figure, the scheme mainly comprises information extraction and equipment authentication, wherein the information extraction is divided into feature extraction and fuzzy extraction, and specifically:
1) feature extraction
The fluctuation of the satellite signal in a period of time has randomness, and the fluctuation of the satellite signal received by the two receivers has similarity under the condition of close distance, and the similarity of the fluctuation condition is reduced along with the increase of the distance between the two devices. Therefore, the cluster receives the GPS signal data to obtain the sequences with similarity, and the data features with similarity are extracted as the authentication features. The method comprises the following specific steps:
A. GPS signal receiving and analyzing: GPS signal receiving and analyzing: and an unmanned aerial vehicle antenna and a GPS receiver on the unmanned aerial vehicle are used for transmitting the received GPS message back to a controller in the unmanned aerial vehicle for processing, and an internal processor of the unmanned aerial vehicle analyzes the message of the GPS signal. The GPS signal message complies with the NMEA protocol, the message contains the current coordinate, the timestamp and the information of the satellite, the timestamp, the satellite PRN number and the carrier-to-noise ratio signal of the satellite are analyzed by the unmanned aerial vehicle processor and stored in the internal storage device;
B. GPS data processing: according to the distance between the unmanned aerial vehicle clusters, in the same period of time, the change conditions of the same GPS signal obtained by different unmanned aerial vehicles are analyzed and compared in similarity. And the signal intensity data of each satellite in a period of time which is directly received is subjected to data normalization by a statistical method to form a uniform data sequence. As shown in fig. 2 and fig. 3, in order to measure the similarity of satellite signals at 2 meters and 70 meters, respectively, the similarity of the same satellite on different devices decreases with increasing distance over time, and within 10 meters, the satellite signals have higher similarity, and when the distance exceeds 50 meters, the similarity decreases.
C. Data feature extraction: according to the contrast data obtained among the unmanned aerial vehicles, feature extraction is carried out on the data, an artificial neural network algorithm is utilized, the number of neural network layers is 3-4, and 100 hidden layer nodes are adopted. The method comprises the steps of firstly using data with different distances as a training set, training the distances as labels, successfully training a neural network, and then successfully matching the labels with the corresponding distances in the same distance range by a satellite, thereby classifying the data with different distances, removing the last layer of the full connection layer of the neural network, obtaining an output value in the node of the previous layer of the network, using the output value as a similarity characteristic of the data in a certain range, and when the distances are close, displaying the similarity characteristic by the fluctuation change of the data collected among a plurality of devices. And after the feature extraction is finished, new received data are used for obtaining a data sequence through data processing, and verification is carried out according to the similarity features of the data sequence.
2) Fuzzy extraction
Because each unmanned aerial vehicle in the cluster has certain similarity according to the characteristics that GPS signal extracted, but all the time is different. Therefore, a Fuzzy Extractor (Fuzzy Extractor) is required to further process the feature data obtained in the first step, so that all drones in the fleet obtain the same information. A fuzzy extractor for a cryptographic tool that extracts uniformly distributed and accurately reproducible random bits from a noisy random source, comprising two algorithms:
A. gen (w) → (P, R): generating an algorithm Gen input character string w (a sampling of a noise random source), and outputting a character string R and an open auxiliary string P;
B. rep (w ', P) → R': regeneration algorithm input w '(another sample of the noise random source) and public auxiliary string P, output a string R'; the fuzzy extractor requires that when the input string w is sufficiently close to the regeneration algorithm input w ', the character string R' regenerated by the regeneration algorithm is exactly the same as the character R generated.
In this embodiment, the public auxiliary string P is generated by the central machine and broadcast to the surrounding drones. And the peripheral unmanned aerial vehicle generates an input character string w' through self GPS signal extraction characteristics, and executes fuzzy extraction to obtain a character string R in combination with the auxiliary string P.
3) Device authentication
A. An initialization stage: before the unmanned aerial vehicle takes off, all unmanned aerial vehicles in the cluster are in a certain range of the central machine. All unmanned aerial vehicles execute an information extraction algorithm, namely initial information R0 (namely a character string R) is obtained through GPS signal analysis, feature extraction and fuzzy extraction, and all unmanned aerial vehicles use R0 as an initial key K0.
B. A flight phase: during the flight, the cluster executes an information extraction algorithm at certain time intervals and generates a new communication key. And assuming that at the time t, the information extracted by the unmanned aerial vehicle is Rt. At time t +1, the communication key used by the cluster is: kt +1 ═ Kt ≦ Rt ≦ R0 ≦ R1 ≦ … ≦ Rt, and ≦ Rt represents an exclusive-or operation.
C. And (3) outlier return: assuming that a drone leaves the cluster at n moments to execute a task in the flight process, the procedure of reacquiring the communication key during the return journey is as follows. And during the return voyage, the outlier holds an outlier communication key Kn, and the outlier firstly sends the Kn to the central machine through an identity authentication scheme. And the central machine receives the Kn and confirms the Kn. And returning information Rn from the time n to the time t-1, (t-1) ═ Rn +1 ≦ Rn +2 ≦ Rt-1 to the outlier. And after the cluster centrifuge receives Rn (t-1), Rt is calculated according to the GPS signal. And finally, obtaining the current intra-cluster communication key Kt according to Kn ^ Rn, (t-1) & ltRt.
Finally, the above embodiments are only used for illustrating the technical solutions of the present invention and not for limiting, although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions, and all that should be covered by the claims of the present invention.
Claims (6)
1. A GPS signal-based unmanned aerial vehicle cluster key extraction and security authentication method is characterized in that: the method mainly comprises an information extraction stage and an equipment authentication stage, and comprises the steps that 1) during initialization, each node in a cluster system generates similar sequence information by using an artificial intelligence technology according to a GPS signal acquired by the node; 2) generating unified information through a fuzzy extractor; 3) finally, generating a session key according to the unified information; the interactive information is encrypted by the session key between the clusters to ensure the communication safety, and in the flight process, the clusters extract the key again according to the GPS signal at intervals to realize the key updating.
2. The method for unmanned aerial vehicle fleet key extraction and security authentication based on GPS signal as claimed in claim 1, wherein: the method specifically comprises the following steps:
s1, feature extraction: through GPS data receiving processing and an artificial intelligence algorithm, the cluster receives GPS signal data to obtain a sequence with similarity, and data features with similarity are extracted to serve as authentication features;
s2, fuzzy extraction: the fuzzy extractor is adopted to further process the characteristic data obtained in the step S1, so that all unmanned aerial vehicles in the cluster obtain the same information;
s3, device authentication: the interactive information is encrypted by the session key between the clusters to ensure the communication safety, and in the flight process, the clusters extract the key again according to the GPS signal at intervals to realize the key updating.
3. The method for unmanned aerial vehicle fleet key extraction and security authentication based on GPS signal as claimed in claim 2, wherein: in step S1, the method specifically includes the following steps:
s11, GPS signal receiving and analyzing: using an unmanned aerial vehicle antenna and a GPS receiver on the unmanned aerial vehicle, transmitting a received GPS message back to a controller in the unmanned aerial vehicle for processing, and analyzing the message of a GPS signal by an internal processor of the unmanned aerial vehicle; the GPS signal message complies with the NMEA protocol, the message comprises the current coordinate, the timestamp and the information of the satellite, and the unmanned aerial vehicle internal processor analyzes the message of the GPS signal to obtain the timestamp, the satellite PRN number and the carrier-to-noise ratio signal of the satellite and stores the time stamp, the satellite PRN number and the carrier-to-noise ratio signal into the storage equipment of the unmanned aerial vehicle;
s12, GPS data processing: according to the distance between the unmanned aerial vehicle clusters, in the same period of time, the change conditions of the same GPS signal obtained by different unmanned aerial vehicles are analyzed and compared in similarity, and the signal intensity data of each satellite in a period of time, which is directly received, is subjected to data normalization by a statistical method to form a uniform data sequence;
s13, data feature extraction: according to the comparison data obtained among the unmanned aerial vehicles, a machine learning algorithm is utilized to extract the features of the data, the similarity features of the data in a certain range are obtained, and when the distances are close, the fluctuation changes of the data collected among the multiple devices present the similarity features; and after the feature extraction is finished, new received data are used for obtaining a data sequence through data processing, and verification is carried out according to the similarity features of the data sequence.
4. The method for unmanned aerial vehicle fleet key extraction and security authentication based on GPS signals according to claim 3, wherein: in step S2, the fuzzy extractor is used for a cryptographic tool that extracts uniformly distributed and accurately reproducible random bits from a noisy random source, and includes two algorithms:
A. gen (w) → (P, R): generating an algorithm Gen input character string w, and outputting a character string R and an open auxiliary string P;
B. rep (w ', P) → R': regeneration algorithm input w 'and public auxiliary string P, output a string R';
the fuzzy extractor requires that when the input string w is sufficiently close to the regeneration algorithm input w ', the character string R' regenerated by the regeneration algorithm is exactly the same as the character R generated.
5. The method for unmanned aerial vehicle fleet key extraction and security authentication based on GPS signals according to claim 4, wherein: the public auxiliary string P is generated by the central machine and broadcasted to surrounding unmanned aerial vehicles, the surrounding unmanned aerial vehicles extract features through self GPS signals to generate an input character string w', and the character string R is obtained by fuzzy extraction through combining the auxiliary string P.
6. The method for unmanned aerial vehicle fleet key extraction and security authentication based on GPS signals according to claim 5, wherein: in step S3, the method specifically includes:
A. an initialization stage: before the unmanned aerial vehicle takes off, all unmanned aerial vehicles in the cluster are in a certain range of the central machine, all unmanned aerial vehicles execute an information extraction algorithm, namely initial information R0 is obtained through GPS signal analysis, feature extraction and fuzzy extraction, and all unmanned aerial vehicles take R0 as an initial key K0;
B. a flight phase: during the flight, the fleet executes an information extraction algorithm at certain time intervals and generates a new communication key: assuming that at time t, the information extracted by the unmanned aerial vehicle is Rt, at time t +1, the communication key used by the cluster is: kt +1 ═ Kt ≦ Rt ≦ R0 ≦ R1 ≦ … ≦ Rt, which represents an exclusive-or operation;
C. and (3) outlier return: assuming that a unmanned aerial vehicle leaves the cluster at n moments to execute tasks in the flight process, the process of reacquiring the communication key during return flight is as follows: when returning to the air, the outlier holds an outlier communication key Kn, firstly, the outlier sends Kn to a central machine through an identity authentication scheme, the central machine receives the Kn and returns information Rn from n to t-1 after confirmation, wherein the information Rn is (t-1) ═ Rn +1 and at the moment of n to t-1, (t-1) ═ Rn +2 ^ Rt-1 to the outlier, the outlier receives the Rn, and after (t-1), Rt is obtained through calculation according to a GPS signal, and finally, the current intra-cluster communication key Kt is obtained according to the Kn, Rn, (t-1) & gt Rt.
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