CN114153227B - Unmanned aerial vehicle group key extraction and security authentication method based on GPS signals - Google Patents
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Abstract
The invention relates to an unmanned aerial vehicle group 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 a device authentication stage, wherein during initialization, each node in a cluster system generates similar sequence information by utilizing an artificial intelligence technology according to GPS signals acquired by the node; generating unified information through a fuzzy extractor; finally, generating a session key according to the unified information; the interaction information is encrypted through the session key among the clusters to ensure the communication safety, and in the flying process, the clusters extract the key again at intervals according to the GPS signals to realize key updating. The scheme can realize the generation of the initial keys on all unmanned aerial vehicles in the cluster only by disclosing the channels, and can well solve the problem of insufficient randomness of the keys. In addition, the authentication mechanism based on the GPS signal in the scheme can also be applied to other unmanned equipment ad hoc network scenes, 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 group key extraction and security authentication method based on GPS signals.
Background
Unmanned aerial vehicle has been widely used in military and civil fields because of its low cost, high mobility, portability, rapid deployment, convenient use, strong timeliness, etc. Due to the self-organizing nature of the unmanned aerial vehicle communication network, it is extremely susceptible to interference and hacking attacks, threatening the security of unmanned aerial vehicle communication.
The current common communication security for the unmanned aerial vehicle group usually adopts an encryption communication process, and in order to ensure the communication security, the unmanned aerial vehicle needs to hold the same secret key in advance, namely the encryption secret key needs to be distributed in advance. The proposal which has been proposed at present is as follows: symmetric key distribution, public key encryption, password-based authentication key exchange schemes, and the like.
Most of the related technologies at present have 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 a practical scenario, the preset key is often used multiple times or lacks sufficient randomness due to low security awareness of the user. In addition, the updating of the secret key is lacking, and once the secret key is leaked, the information of the clusters in the whole flying process is leaked. (2) public key encryption: no secure channel is required, but the calculation and storage overhead is large and the efficiency is low. (3) password-based authentication key exchange scheme: the problem of preset secret key randomness is not enough is solved, but the scheme still needs to put the password into each unmanned aerial vehicle in advance, and because the operation process of the password needs multiple rounds of interaction, the communication expense is extremely high.
Disclosure of Invention
Therefore, the invention aims to provide the unmanned aerial vehicle group key extraction and security authentication method based on the GPS signals, the method can realize the generation of the initial keys on all unmanned aerial vehicles in the unmanned aerial vehicle group only through a public channel, and the extracted keys have enough information entropy, so that the problem of insufficient randomness of the keys can be solved.
In order to achieve the above purpose, the present invention provides the following technical solutions:
the unmanned aerial vehicle group key extraction and safety authentication method based on GPS signals mainly comprises an information extraction stage and an equipment authentication stage, and comprises 1) when in initialization, each node in a group system generates similar sequence information by utilizing an artificial intelligent technology according to the GPS signals acquired by the node; 2) Generating unified information by a Fuzzy Extractor (Fuzzy Extractor); 3) Finally, generating a session key according to the unified information; the interaction information is encrypted through the session key among the clusters to ensure the communication safety, and in the flying process, the clusters extract the key again at intervals according to the GPS signals to realize key updating.
Further, the method specifically comprises the following steps: s1, feature extraction: the method comprises the steps that through GPS data receiving processing and an artificial intelligence algorithm, a 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 can obtain identical information; s3, equipment authentication: the interaction information is encrypted through the session key among the clusters to ensure the communication safety, and in the flying process, the clusters extract the key again at intervals according to the GPS signals to realize key updating.
Further, in step S1, the method specifically includes the steps of:
s11, GPS signal receiving and analyzing: the method comprises the steps that an unmanned aerial vehicle antenna and a GPS receiver on the unmanned aerial vehicle are used, a received GPS message is transmitted back to a controller in the unmanned aerial vehicle for processing, and an internal processor of the unmanned aerial vehicle analyzes the message of a GPS signal; the GPS signal message complies with NMEA protocol, the message contains current coordinate, time stamp and satellite information, the time stamp, satellite PRN number and 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 unmanned aerial vehicle groups, in the same period of time, the same GPS signal change situation obtained by different unmanned aerial vehicles is analyzed and compared in similarity, and the signal intensity data of each satellite in the period of time which is directly received is subjected to data normalization by a statistical method to form a unified data sequence;
s13, extracting data features: according to the obtained comparison data among the unmanned aerial vehicles, performing feature extraction on the data by using a machine learning algorithm to obtain similarity features of the data in a certain range, and when the distances are similar, displaying the similarity features by fluctuation changes of the data collected among the plurality of devices; after the feature extraction is finished, new received data is used for obtaining a data sequence through data processing, and verification is carried out according to the similarity features.
Further, in step S2, the fuzzy extractor is used to extract a cryptographic tool of uniformly distributed and precisely reproducible random bits from a noisy random source, comprising two algorithms:
A. gen (w) → (P, R): the generation algorithm Gen inputs a character string w (one sample of a noise random source) and outputs a character string R and a public auxiliary string P;
B. rep (w ', P) →R' the regeneration algorithm inputs w '(another sampling of the noise random source) and the public auxiliary string P, outputs a character string R';
the fuzzy extractor requires that the character string R 'reproduced by the reproduction algorithm is identical to the character R generated when the input character string w is sufficiently close to the reproduction algorithm input w'.
Further, the disclosed auxiliary string P is generated by the central machine and broadcast to surrounding unmanned aerial vehicles, the surrounding unmanned aerial vehicles generate an input character string w' by extracting features through own GPS signals, and the character string R is obtained by performing fuzzy extraction in combination with the auxiliary string P.
Further, in step S3, specifically including:
A. an initialization stage: before the unmanned aerial vehicle takes off, all unmanned aerial vehicles in the unmanned aerial vehicle group are in a certain range of the central machine at the moment, 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 secret key K0;
C. and (3) flight stage: in the flight process, the cluster 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 =r0 @ r1 @ … @ R @ t @ R @ 52, @ R @ m, @ m represents an exclusive or operation;
C. and (5) outlier return: assuming that the unmanned aerial vehicle leaves the cluster to execute tasks at the moment n in the flight process, the process of reacquiring the communication key during the return is as follows: when returning, the outlier holds the communication key Kn when the outlier is in possession of the outlier, the outlier firstly sends Kn to the central machine through an identity authentication scheme, after the central machine receives the Kn and confirms, the central machine returns information Rn from the moment n to the moment t-1, (t-1) =Rn+2.
The invention has the beneficial effects that:
because the unmanned aerial vehicle in the cluster only needs the GPS signal that itself gathered and the public information from the central machine when generating the secret key, consequently this scheme has avoided the secret key distribution problem when initializing, only need can realize the generation of initial secret key on all unmanned aerial vehicles in the cluster through the public channel. On the other hand, the characteristics of the fuzzy extractor and the randomness of the GPS signal ensure that the extracted secret key has enough information entropy, and the problem of insufficient randomness of the secret key is solved. In addition, because of 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 signals in the scheme can be applied to not only the unmanned aerial vehicle group, but also other unmanned equipment ad hoc networks, and 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 objects and other advantages of the invention may be realized and obtained by means of the instrumentalities and combinations particularly pointed out in the specification.
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For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in the following preferred detail with reference to the accompanying drawings, in which:
FIG. 1 is a flow diagram illustration of the present invention;
FIG. 2 is a graph showing the result of measuring the similarity of satellite signals at 2 meters in the embodiment;
FIG. 3 shows the result of measuring the similarity of satellite signals at 70 m in the embodiment.
Detailed Description
The technical scheme of the invention is described in detail below with reference to the accompanying drawings.
Fig. 1 is a schematic flow chart of the present invention, and the present invention proposes a group authentication communication mechanism of an unmanned aerial vehicle based on GPS signals. During initialization, each node in the system generates similar sequence information by utilizing an artificial intelligence technology according to the GPS signals acquired by the node. The unified information is then generated by a Fuzzy Extractor (Fuzzy Extractor). Finally, a session key is generated based on the unified information. The interaction information is encrypted through the session key among the clusters so as to ensure the communication security. In the flying process, the cluster extracts the secret key again according to the GPS signal at intervals, and the secret key is updated.
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 fluctuations of the satellite signals over a period of time have a randomness and, in the case of similar distances, the fluctuations of the satellite signals received by the two receivers have a similarity, which decreases with increasing distance between the two devices. The data acquisition of the GPS signals is carried out through the cluster, so that sequences with similarity are obtained, and data features with similarity are extracted as authentication features. The method comprises the following specific steps:
A. GPS signal receiving and analyzing: GPS signal receiving and analyzing: and the received GPS message is transmitted back to a controller in the unmanned aerial vehicle for processing by using an unmanned aerial vehicle antenna and a GPS receiver on the unmanned aerial vehicle, and a message of a GPS signal is analyzed by an internal processor of the unmanned aerial vehicle. The GPS signal message complies with NMEA protocol, the message contains current coordinate, time stamp and satellite information, the time stamp, satellite PRN number and 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: and according to the distance between the unmanned aerial vehicle groups, in the same period of time, the same GPS signal change condition obtained by different unmanned aerial vehicles is analyzed and compared for similarity. And carrying out data normalization on the signal intensity data of each satellite in a period of time which is directly received by a statistical method to form a unified data sequence. As shown in fig. 2 and 3, in order to measure the similarity of satellite signals at 2 meters and 70 meters respectively, the same satellite on different devices has reduced similarity with increasing distance, and the satellite signals have higher similarity within 10 meters, while the similarity is reduced when the distance exceeds 50 meters.
C. And (3) data characteristic extraction: and according to the obtained comparison data among the unmanned aerial vehicles, extracting the characteristics of the data, and utilizing an artificial neural network algorithm, wherein the number of the neural network layers is 3-4, and 100 hidden layer nodes are adopted. Firstly, data with different distances are used as training sets, the distances are used as labels for training, after the neural network is successfully trained, the satellite can be successfully matched with the labels with corresponding distances in the same distance range, so that the data with different distances are classified, the last layer of the full-connection layer of the neural network is removed, an output value in a network node of the previous layer is obtained and is used as a similarity feature of the data in a certain range, and when the distances are similar, fluctuation changes of the data collected among a plurality of devices show the similarity feature. After the feature extraction is finished, new received data is used for obtaining a data sequence through data processing, and verification is carried out according to the similarity features.
2) Fuzzy extraction
Because each unmanned aerial vehicle in the fleet has certain similarity according to the characteristics extracted by the GPS signals, the unmanned aerial vehicles are always different. Therefore, a Fuzzy Extractor (Fuzzy Extractor) is needed to further process the feature data obtained in the first step, so that all unmanned aerial vehicles in the fleet obtain identical information. A fuzzy extractor is a cryptographic tool for extracting uniformly distributed and precisely reproducible random bits from a noisy random source, comprising two algorithms:
A. gen (w) → (P, R): the generation algorithm Gen inputs a character string w (one sample of a noise random source) and outputs a character string R and a public auxiliary string P;
B. rep (w ', P) →R' the regeneration algorithm inputs w '(another sampling of the noise random source) and the public auxiliary string P, outputs a character string R'; the fuzzy extractor requires that the character string R 'reproduced by the reproduction algorithm is identical to the character R generated when the input character string w is sufficiently close to the reproduction algorithm input w'.
In this embodiment, the public auxiliary string P is generated by the central machine and broadcast to surrounding unmanned aerial vehicles. The surrounding unmanned aerial vehicle extracts the characteristic to produce the input character string w' through the self GPS signal, combine the auxiliary string P, carry out the fuzzy extraction and get the character string R.
3) Device authentication
A. An initialization stage: before unmanned aerial vehicle takes off, all unmanned aerial vehicles in the fleet are in the certain scope of central plane this moment. And 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.
B. And (3) flight stage: during flight, the cluster executes an information extraction algorithm at certain time intervals and generates a new communication key. At time t, the information extracted by the unmanned aerial vehicle is Rt. Then at time t+1, the communication key used by the cluster is: kt+1=kt =r0 @ r1 @ … @ R @, and R @ 52, and R @ represents an exclusive or operation.
C. And (5) outlier return: in the flight process, if the unmanned aerial vehicle leaves the cluster at the time n to execute the task, the flow of re-acquiring the communication key during the return is as follows. During return, the outlier holds the outlier-time communication key Kn, and the outlier firstly sends Kn to the central machine through the identity authentication scheme. And the central machine receives the Kn and confirms the Kn. Returning information Rn from time n to time t-1, (t-1) =rn+1-n+2. After receiving Rn, (t-1), the outlier calculates Rt according to the GPS signal. Finally, according to Kn, rn, (t-1) and Rt, obtaining the communication key Kt in the current cluster.
Finally, it is noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and 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 the technical solution of the present invention may be modified without departing from the spirit and scope of the technical solution, and all such modifications are included in the scope of the claims of the present invention.
Claims (4)
1. A GPS signal-based unmanned aerial vehicle group key extraction and security authentication method is characterized in that: the method mainly comprises an information extraction stage and a device authentication stage, and comprises 1) when in initialization, each node in a cluster system generates similar sequence information by utilizing an artificial intelligence technology according to GPS signals acquired by the node; 2) Generating unified information through a fuzzy extractor; 3) Finally, generating a session key according to the unified information; the interaction information is encrypted through the session key among the clusters to ensure the communication safety, and in the flying process, the clusters extract the key again according to the GPS signal at intervals to realize key updating;
the method specifically comprises the following steps:
s1, feature extraction: the method comprises the steps that through GPS data receiving processing and an artificial intelligence algorithm, a 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 can obtain identical information;
s3, equipment authentication: the interaction information is encrypted through the session key among the clusters to ensure the communication safety, and in the flying process, the clusters extract the key again according to the GPS signal at intervals to realize key updating;
in step S1, the method specifically includes the following steps:
s11, GPS signal receiving and analyzing: the method comprises the steps that an unmanned aerial vehicle antenna and a GPS receiver on the unmanned aerial vehicle are used, a received GPS message is transmitted back to a controller in the unmanned aerial vehicle for processing, and an internal processor of the unmanned aerial vehicle analyzes the message of a GPS signal; the GPS signal message complies with NMEA protocol, the message contains current coordinate, time stamp, satellite information, the unmanned aerial vehicle internal processor analyzes the GPS signal message to obtain time stamp, satellite PRN number and carrier-to-noise ratio signal of the satellite, and stores the time stamp, satellite PRN number and carrier-to-noise ratio signal in the storage device of the unmanned aerial vehicle;
s12, GPS data processing: according to the distance between unmanned aerial vehicle groups, in the same period of time, the same GPS signal change situation obtained by different unmanned aerial vehicles is analyzed and compared in similarity, and the signal intensity data of each satellite in the period of time which is directly received is subjected to data normalization by a statistical method to form a unified data sequence;
s13, extracting data features: according to the obtained comparison data among the unmanned aerial vehicles, extracting the characteristics of the data, and utilizing an artificial neural network algorithm, wherein the number of the neural network layers is 3-4, and 100 hidden layer nodes are adopted; firstly, using data with different distances as a training set, using the distances as labels to train, after successfully training a neural network, successfully matching the labels with corresponding distances in the same distance range by a satellite, classifying the data with different distances, removing the last layer of a full-connection layer of the neural network, obtaining an output value in a network node of the previous layer, and taking the output value as a similarity characteristic of the data in a certain range, wherein when the distances are similar, the fluctuation change of the data collected among a plurality of devices presents the similarity characteristic; after the feature extraction is finished, new received data is used for obtaining a data sequence through data processing, and verification is carried out according to the similarity features.
2. The unmanned aerial vehicle group key extraction and security authentication method based on GPS signals as claimed in claim 1, wherein the method comprises the following steps: in step S2, the fuzzy extractor is used to extract a cryptographic tool of uniformly distributed and precisely reproducible random bits from a noisy random source, comprising two algorithms:
A. gen (w) → (P, R): the generation algorithm Gen inputs a character string w and outputs a character string R and a public auxiliary string P;
B. rep (w ', P) →R' the regeneration algorithm inputs w 'and the public auxiliary string P, outputs a character string R'; the fuzzy extractor requires that the character string R 'reproduced by the reproduction algorithm is identical to the character R generated when the input character string w is sufficiently close to the reproduction algorithm input w'.
3. The unmanned aerial vehicle group key extraction and security authentication method based on GPS signals as claimed in claim 2, wherein the method comprises the following steps: the disclosed auxiliary string P is generated by a central machine and is broadcast to surrounding unmanned aerial vehicles, the surrounding unmanned aerial vehicles extract characteristics through own GPS signals to generate an input character string w', and the character string R is obtained by performing fuzzy extraction in combination with the auxiliary string P.
4. A method for unmanned aerial vehicle group key extraction and security authentication based on GPS signals according to claim 3, wherein: in step S3, specifically, the method includes:
A. an initialization stage: before the unmanned aerial vehicle takes off, all unmanned aerial vehicles in the unmanned aerial vehicle group are in a certain range of the central machine at the moment, 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 secret key K0;
B. and (3) flight stage: in the flight process, the cluster 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 =r0 @ r1 @ … @ R @ t @ R @ 52, @ R @ m, @ m represents an exclusive or operation;
C. and (5) outlier return: assuming that the unmanned aerial vehicle leaves the cluster to execute tasks at the moment n in the flight process, the process of reacquiring the communication key during the return is as follows: when returning, the outlier holds the communication key Kn when the outlier is in possession of the outlier, the outlier firstly sends Kn to the central machine through an identity authentication scheme, after the central machine receives the Kn and confirms, the central machine returns information Rn from the moment n to the moment t-1, (t-1) =Rn+2.
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