CN112241547A - Vehicle data encryption analysis method, edge server and storage medium - Google Patents
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Abstract
The present disclosure provides a vehicle data encryption analysis method, an edge server and a computer readable memory, wherein the method comprises: the method comprises the steps that an edge server obtains an encryption analysis request of vehicle data sent by an intelligent vehicle; the edge server analyzes the encryption analysis request to obtain an encryption analysis scheme; and the edge server returns the encryption analysis scheme to the intelligent vehicle so that the intelligent vehicle encrypts the vehicle data of the intelligent vehicle based on the encryption analysis scheme. The embodiment of the disclosure can at least solve the problems of high response delay, signaling congestion, high identity authentication cost and the like in the current Internet of vehicles data processing process.
Description
Technical Field
The present disclosure relates to the field of car networking technologies, and in particular, to a method for encrypting and analyzing vehicle data, an edge computing server, and a computer-readable storage medium.
Background
With the rapid development of the 5G (5th-Generation, fifth-Generation communication technology) network, the conventional network attack detection method adopted by the current car networking is gradually unable to adapt to the increasing demands for faster network attack speed and greater destructive power, and the problems of high response delay, signaling congestion, high authentication cost and the like are increasingly prominent.
Disclosure of Invention
The present disclosure provides a vehicle data encryption analysis method, an edge calculation server, and a computer-readable storage medium to at least solve the above-mentioned problems.
According to an aspect of the embodiments of the present disclosure, there is provided a vehicle data encryption analysis method, including:
the method comprises the steps that an edge server obtains an encryption analysis request of vehicle data sent by an intelligent vehicle;
the edge server analyzes the encryption analysis request to obtain an encryption analysis scheme; and the number of the first and second groups,
the edge server returns the encryption analysis scheme to the smart vehicle to cause the smart vehicle to encrypt its vehicle data based on the encrypted analysis scheme.
In one embodiment, the encrypted analysis request carries vehicle data,
after the edge server obtains the encryption analysis request of the vehicle data sent by the intelligent vehicle, the method further comprises the following steps:
the edge server generating a service analysis plan based on the vehicle data; and the number of the first and second groups,
the edge server returns the server analysis scheme to the intelligent vehicle so that the intelligent vehicle can conduct vehicle navigation based on the service analysis scheme.
In one embodiment, the analyzing, by the edge server, the encryption analysis request to obtain an encryption analysis scheme includes:
the edge server generates an initial encryption scheme by using a multi-factor encryption algorithm based on the encryption analysis request;
the edge server determines optimization parameters of the initial encryption scheme; and the number of the first and second groups,
and the edge server analyzes the initial encryption scheme according to the optimization parameters to obtain an encryption analysis scheme.
In one embodiment, the encryption analysis request carries an authentication credential and a timestamp of the encryption analysis request, and the multi-factor encryption algorithm is obtained according to the following formula:
b∈(10，100)
in the formula, S _ N represents an authentication credential ciphertext, S _ N' represents an authentication credential plaintext, T represents a timestamp, and b represents a nonce.
In one embodiment, the analyzing, by the edge server, the initial encryption scheme according to the optimization parameter to obtain an encryption analysis scheme includes:
the edge server sets an iteration initial parameter and a maximum iteration number;
the edge server carries out deep analysis on the initial encryption scheme aiming at the optimization parameters to obtain an intermediate encryption scheme with the optimal matching degree;
the edge server judges whether the intermediate encryption scheme with the optimal matching degree meets a preset evaluation condition or not;
if the preset evaluation condition is met, the edge server selects the intermediate encryption scheme with the optimal matching degree as an encryption analysis scheme;
if the current iteration times are not greater than the maximum iteration times, the edge server judges whether the current iteration times are not greater than the maximum iteration times;
if the number of iterations is not greater than the maximum number of iterations, the edge server performs deep unsupervised learning on the optimization parameter to obtain an optimization parameter with the number of iterations added by 1, and the step of performing deep analysis on the initial encryption scheme by the edge server according to the optimization parameter is returned;
and if the maximum iteration number is greater than the maximum iteration number, the edge server selects the intermediate encryption scheme with the optimal matching degree as an encryption analysis scheme.
In one embodiment, the optimization parameters include a congestion rate, a unit authentication cost, and a response delay rate, and the optimization parameters are stored in the form of a three-dimensional vector as:
wherein,
expressing an optimization parameter during the kth iteration, wherein k is the iteration number; i. j and t are dimensions, and i is [1, m ]],j∈[1,n],t∈[1,q]M, n and q respectively represent the maximum value of the dimension;
the congestion rate at the k iteration is;
the unit authentication cost in the k iteration;
is the response delay rate at the kth iteration.
In one embodiment, the edge server performs a deep analysis on the initial encryption scheme according to the optimization parameters, and the deep analysis is obtained according to the following formula:
in the formula, Min ZKAn intermediate encryption scheme representing the best degree of matching obtained during the kth iteration, CGmin、EGmin、WGminRespectively, a historical minimum unit authentication cost, a historical minimum response delay rate, and a historical minimum congestion rate.
In an embodiment, the edge server performs deep unsupervised learning on the optimization parameter to obtain an optimization parameter obtained by adding 1 to the iteration number, and the optimization parameter is obtained according to the following formula:
in the formula,
an optimization parameter representing the number of iterations at the k +1 st time, comprising
The information vector of the three aspects is that,
representing the unit authentication cost for the k +1 th iteration,
represents the response delay rate at the k +1 th iteration,
indicating the congestion rate at the k +1 th iteration,
representing a depth unsupervised learning enhancement factor when the iteration times are the (k + 1) th time;
wherein the deep unsupervised learning enhancement factor
Obtained according to the following formula:
in the formula, CGminRepresenting the historical minimum unit authentication cost, EGminRepresents the historical minimum response delay rate, WGminIndicating a historical minimum congestion rate.
In an embodiment, the edge server determines whether the intermediate encryption scheme with the optimal matching degree meets a preset evaluation condition, and obtains the intermediate encryption scheme according to the following formula:
in the formula,
representing the unit authentication cost product probability at the kth iteration,
representing the probability of the response delay rate product at the kth iteration,
representing the probability of congestion rate product at the k-th iteration.
According to another aspect of the embodiments of the present disclosure, there is provided an edge server, including a memory and a processor, where the memory stores a computer program, and when the processor runs the computer program stored in the memory, the processor executes the vehicle data encryption analysis method.
According to still another aspect of the embodiments of the present disclosure, there is provided a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the processor executes the vehicle data encryption analysis method.
The technical scheme provided by the embodiment of the disclosure can have the following beneficial effects:
according to the vehicle data encryption analysis method provided by the embodiment of the disclosure, an edge server acquires an encryption analysis request of vehicle data sent by an intelligent vehicle; the edge server analyzes the encryption analysis request to obtain an encryption analysis scheme; and the edge server returns the encryption analysis scheme to the intelligent vehicle so that the intelligent vehicle encrypts the vehicle data of the intelligent vehicle based on the encryption analysis scheme. The embodiment of the disclosure can at least solve the problems of high response delay, signaling congestion, high identity authentication cost and the like in the current Internet of vehicles data processing process.
Additional features and advantages of the disclosure will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by the practice of the disclosure. The objectives and other advantages of the disclosure may be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
The accompanying drawings are included to provide a further understanding of the disclosed embodiments and are incorporated in and constitute a part of this specification, illustrate embodiments of the disclosure and together with the example serve to explain the principles of the disclosure and not to limit the disclosure.
Fig. 1 is a schematic flow chart of a vehicle data encryption analysis method according to an embodiment of the present disclosure;
FIG. 2 is a schematic view of a security scenario of the Internet of vehicles;
fig. 3 is a schematic flow chart illustrating deep analysis of the initial encryption scheme according to the optimized parameters in the embodiment of the present disclosure;
FIG. 4 is a schematic diagram of the storage of optimization parameters in the form of three-dimensional vectors according to an embodiment of the present disclosure;
fig. 5 is another schematic flow chart of a method for encrypted analysis of industrial data according to an embodiment of the present disclosure;
FIG. 6 is a schematic diagram of a convolutional neural network in an embodiment of the present disclosure;
fig. 7 is a schematic flow chart of a vehicle data encryption and analysis method according to another embodiment of the present disclosure;
fig. 8 is a schematic structural diagram of an edge server according to an embodiment of the present disclosure.
Detailed Description
To make the objects, technical solutions and advantages of the embodiments of the present disclosure more apparent, specific embodiments of the present disclosure are described below in detail with reference to the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating the present disclosure, are given by way of illustration and explanation only, not limitation.
It should be noted that the terms "first," "second," and the like in the description and claims of the present disclosure and in the above-described drawings are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order; also, the embodiments and features of the embodiments in the present disclosure may be arbitrarily combined with each other without conflict.
In the following description, suffixes such as "module", "component", or "unit" used to denote elements are used only for the convenience of explanation of the present disclosure, and have no specific meaning in themselves. Thus, "module", "component" or "unit" may be used mixedly.
With the rapid development of 5G networks, the 5G networks refer to the fifth generation networks in the development of mobile communication networks, and compared with the previous four generation mobile networks, the 5G networks show more enhanced functions in practical application processes, and theoretically, the transmission speed can reach 10 GB/s, which is hundreds of times that of the 4G mobile networks. For a 5G network, the network has more obvious advantages and more powerful functions in the practical application process, meanwhile, the traditional network attack detection mode cannot gradually adapt to the increasing requirements of faster network attack speed and higher destructive power, and the problems of high response delay, signaling congestion, high identity authentication cost and the like are increasingly prominent, so that the rapid and continuous development of the safety encryption system and method for the Internet of vehicles is of great significance.
Referring to fig. 1, fig. 1 is a schematic flow chart of a vehicle data encryption and analysis method according to an embodiment of the present disclosure, where the method includes steps S101 to S103.
In step S101, the edge server acquires an encryption analysis request of vehicle data transmitted by the smart vehicle.
Shown in fig. 2, fig. 2 is a car networking security scene diagram, which is mainly divided into three layer parts: 1) an intelligent vehicle edge node layer comprising: intelligent vehicles and intelligent vehicles are equipped with On Board units (OBUs for short), the OBUs have functions of calculation, storage and network, and the OBUs comprise sub-devices such as detection sensors (distance and light detection), Global Positioning System (GPS for short), video and cameras. The intelligent vehicle resource sharing method mainly realizes the sharing of intelligent vehicle resources, and intelligent vehicles in driving or parking states are sharers and sharees. 2) A tie layer comprising: edge servers, fixed edge node Road Side Units (RSUs), Base Stations (BSs), etc., that implement local services, with fixed edge node Road Side Units (RSUs) or typical cellular Base Stations (BSs) in the connectivity layer connected to the edge servers for high computation and storage capacity. The edge server may be co-located at the BS or RSU. The RSU vs BS serves the vehicle with intelligent vehicles and mobile edge nodes due to the proximity to the vehicle. Without RSU coverage, the BS may still provide edge computing services for the smart vehicle. 3) The core layer can contain a large number of high-performance special servers to realize excellent computing and storage capacity.
Firstly, the intelligent vehicle in running or parking state accesses the connection layer through the equipped On Board Unit (OBU), and simultaneously transmits the encryption analysis request of the vehicle data to the edge server through the BS or the RSU, and the edge server acquires the encryption analysis request of the intelligent vehicle.
In step S102, the edge server analyzes the encryption analysis request to obtain an encryption analysis scheme.
In step S103, the edge server returns the encryption analysis scheme to the smart vehicle, so that the smart vehicle encrypts its vehicle data based on the encryption analysis scheme.
Specifically, the edge server returns the encryption analysis scheme to the intelligent vehicle in motion or in a parked state through the BS or RSU.
In this embodiment, an initial encryption scheme is generated by using a multi-factor encryption algorithm, and a final encryption analysis scheme is generated by combining algorithms such as a multilayer convolution neuron and supervised learning, so that the final encryption analysis scheme is sufficiently excellent, and the security of the car networking data is improved, specifically, step S102 includes the following steps:
the edge server generates an initial encryption scheme by using a multi-factor encryption algorithm based on the encryption analysis request;
the edge server determines optimization parameters of the initial encryption scheme; and the number of the first and second groups,
and the edge server analyzes the initial encryption scheme according to the optimization parameters to obtain an encryption analysis scheme.
Further, in this embodiment, a multi-factor encryption algorithm is used to perform authentication and encryption to generate an initial encryption scheme, where the encryption analysis request carries an authentication credential and a timestamp of the encryption analysis request, and the multi-factor encryption algorithm is obtained according to the following formula:
b∈(10，100)
in the formula, S _ N represents an authentication credential ciphertext, S _ N' represents an authentication credential plaintext, T represents a timestamp, and b represents a nonce.
The authentication encryption is identity authentication encryption, namely identity authentication encryption of the intelligent vehicle, the identity of the intelligent vehicle is obtained when an encryption analysis request of the intelligent vehicle is received, and the authentication encryption is firstly carried out by utilizing a multi-factor encryption algorithm to generate an initial encryption scheme, so that an authentication certificate is carried in the initial encryption scheme, and the security of the encryption analysis scheme is improved.
Further, multi-factor encryption authentication analysis: after the model is reached (namely the edge server receives the encryption analysis request), multi-factor encryption authentication analysis is carried out, and S1, S2 and S … Sn are vehicle data analysis requests to be analyzed respectively (the vehicle data analysis requests can be converted into binary codes and stored), and are analyzed into corresponding deep analysis results. The incoming vehicle data analysis request is given a current higher analysis dispatch priority if delayed.
In the embodiment, the initial encryption scheme is dynamically generated by using the multi-factor encryption algorithm, and then the initial encryption scheme is optimized by determining the optimized parameters of the initial encryption, so that the final encryption analysis scheme is migrated to the optimal mode to obtain the optimal encryption scheme, and the problems of high response delay, signaling congestion, high identity authentication cost and the like in the internet of vehicles are further solved.
Further, referring to fig. 3, the edge server analyzes the initial encryption scheme according to the optimization parameter to obtain an encryption analysis scheme, including the following steps:
a. the edge server sets an iteration initial parameter and a maximum iteration number;
b. the edge server carries out deep analysis on the initial encryption scheme aiming at the optimization parameters to obtain an intermediate encryption scheme with the optimal matching degree;
c. and the edge server judges whether the intermediate encryption scheme with the optimal matching degree meets a preset evaluation condition, if so, executes d, otherwise, executes e.
d. The edge server selects the intermediate encryption scheme with the optimal matching degree as an encryption analysis scheme;
e. and the edge server judges whether the current iteration times are not more than the maximum iteration times, if not, f is executed, otherwise, the edge server returns d and selects the intermediate encryption scheme with the optimal matching degree as an encryption analysis scheme.
f. And the edge server carries out deep unsupervised learning on the optimized parameters to obtain the optimized parameters with the iteration times added by 1, and returns to execute the step that the edge server carries out deep analysis on the initial encryption scheme aiming at the optimized parameters.
Specifically, when the intermediate encryption scheme does not meet the evaluation condition, the intermediate encryption scheme which does not meet the condition is further subjected to iterative optimization, then the optimization parameters of the intermediate encryption scheme which is further subjected to iterative optimization are subjected to deep unsupervised learning, and when the iteration times reach the maximum, the intermediate encryption scheme at the iteration point is selected as the final encryption analysis scheme.
It should be noted that, in the present embodiment, deep analysis is performed on the initial encryption scheme in an iterative loop manner, where a maximum iteration parameter may be set to be 50, and in order to avoid infinite iteration optimization, when the number of iterations reaches 50, the scheme is defaulted to have satisfied the evaluation condition.
In one embodiment, the optimization parameters include a congestion rate, a unit authentication cost, and a response delay rate, and the optimization parameters are stored in the form of a three-dimensional vector as:
wherein,
expressing an optimization parameter during the kth iteration, wherein k is the iteration number; i. j and t are dimensions, and i is [1, m ]],j∈[1,n],t∈[1,q]M, n, q respectively identify the maximum value of the dimension;
the congestion rate at the k iteration is;
the unit authentication cost in the k iteration;
is the response delay rate at the kth iteration.
In particular, the intermediate encryption scheme is stored in three dimensions using a sparse matrix, as shown in figure 4,
and storing each optimization parameter in the corresponding dimension position of i, j, t (namely any value on m, n and q coordinates). In some embodiments, optimization of authentication cost, response delay rate, and congestion rate of the initial encryption scheme is achieved in conjunction with a convolutional neural network.
In order to further understand, in the embodiment, the strategy ideas of the multilayer convolutional neurons, the deep unsupervised learning, the multi-factor encryption authentication and the like in each iteration are that in a multi-dimensional space, a plurality of deep analysis schemes migrate to the direction determined by the optimized task priority scheme according to strategy modes of the multilayer convolutional neurons, the deep unsupervised learning, the multi-factor encryption authentication and the like, and as shown in the figure 5, vehicle data analysis is input through a request, and corresponding analysis results are output after the multi-factor encryption authentication analysis a, the multilayer convolutional neurons b and the deep unsupervised learning c. As shown in fig. 6, the multi-layered convolutional neuron network includes: the congestion rate W (the amount of time that the vehicle data analysis request is not received per unit time/the total amount of unit time), the response delay rate E (the amount of time that the vehicle data analysis is invalid and the total amount of unit time per unit time), the unit authentication cost C (the amount of force consumed for authentication per unit time), and the output quantities include: pre-recommendation information for a vehicle data analysis scenario.
In one embodiment, the edge server performs a deep analysis on the initial encryption scheme according to the optimization parameters, and the deep analysis is obtained according to the following formula:
in the formula, Min ZKAn intermediate encryption scheme representing the best degree of matching obtained during the kth iteration, CGmin、EGmin、WGminRespectively historical minimum encryption cost, historical minimum response delay rate, and historical minimum congestion rate.
Specifically, through deep analysis of the initial encryption scheme, a scheme of the historical minimum encryption cost, the historical minimum response delay rate and the historical minimum congestion rate is selected as an intermediate encryption scheme with the optimal matching degree in the k iteration according to the formula.
In an embodiment, the edge server performs deep unsupervised learning on the optimization parameter to obtain an optimization parameter obtained by adding 1 to the iteration number, and the optimization parameter is obtained according to the following formula:
in the formula,
an optimization parameter representing the number of iterations at the k +1 st time, comprising
The information vector of the three aspects is that,
representing the unit authentication cost for the k +1 th iteration,
represents the response delay rate at the k +1 th iteration,
indicating the congestion rate at the k +1 th iteration,
represents the depth unsupervised learning enhancement factor when the iteration number is the (k + 1) th time.
Wherein the deep unsupervised learning enhancement factor
Obtained according to the following formula:
in the formula, CGminRepresenting the historical minimum unit authentication cost, EGminRepresents the historical minimum response delay rate, WGminIndicating a historical minimum congestion rate.
It will be appreciated that the above-described,
to change the increment, it can be understood as a k-th iteration loop recursive excitation function.
In an embodiment, the edge server determines whether the intermediate encryption scheme with the optimal matching degree meets a preset evaluation condition, and obtains the intermediate encryption scheme according to the following formula:
in the formula,
representing the unit authentication cost product probability at the kth iteration,
representing the probability of the response delay rate product at the kth iteration,
representing the probability of congestion rate product at the k-th iteration.
In the embodiment, the edge server is used for carrying out encryption analysis on the encryption analysis request sent by the intelligent vehicle and returning the encryption analysis request to the corresponding intelligent vehicle, on one hand, the problems of high response delay, signaling congestion and the like can be effectively solved in a mode of generating the encryption analysis scheme based on the edge server, on the other hand, the intelligent vehicle does not need to frequently carry out identity authentication based on the scheme, and the authentication cost of the intelligent vehicle for vehicle data can be effectively saved.
Referring to fig. 7, fig. 7 is a schematic flow chart of another vehicle data encryption analysis method provided in the previous embodiment of the present disclosure, on the basis of the previous embodiment, in this embodiment, while providing a vehicle data encryption scheme for an intelligent vehicle, a service scheme is generated for the intelligent vehicle, so as to further improve the processing speed of vehicle network data, specifically, the vehicle data is carried in the encryption analysis request,
after the edge server obtains the encryption analysis request of the vehicle data sent by the intelligent vehicle, the method further comprises the following steps of S701 and S702:
in step S701, the edge server generates a service analysis plan based on the vehicle data; and the number of the first and second groups,
in step S702, the edge server returns the server analysis plan to the smart vehicle, so that the smart vehicle performs vehicle navigation based on the service analysis plan.
Specifically, the intelligent vehicle in the running state or the parking state accesses the connection layer through an equipped on-board unit (OBU), meanwhile, a vehicle data analysis request is transmitted to the edge server through the BS or the RSU, the edge server generates a service analysis scheme according to the vehicle data, the service analysis scheme can provide partial services for the intelligent vehicle, including navigation services, collision avoidance, service station reminding, vehicle overall condition analysis and other services, and the service analysis scheme is returned to the intelligent vehicle in the running state or the parking state through the BS or the RSU.
In some embodiments, to ensure the security of the service analysis scheme, the service analysis scheme may be encrypted by combining the encryption scheme and then returned to the intelligent vehicle, which is not described herein again.
Based on the same technical concept, the embodiment of the present disclosure correspondingly provides an edge server, as shown in fig. 8, the edge server includes a memory 81 and a processor 82, the memory 81 stores a computer program, and when the processor 82 runs the computer program stored in the memory 81, the processor 82 executes the vehicle data encryption analysis method.
Based on the same technical concept, the embodiment of the present disclosure also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the processor executes the vehicle data encryption analysis method.
It will be understood by those of ordinary skill in the art that all or some of the steps of the methods, systems, functional modules/units in the devices disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof. In a hardware implementation, the division between functional modules/units mentioned in the above description does not necessarily correspond to the division of physical components; for example, one physical component may have multiple functions, or one function or step may be performed by several physical components in cooperation. Some or all of the physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on computer readable media, which may include computer storage media (or non-transitory media) and communication media (or transitory media). The term computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data, as is well known to those of ordinary skill in the art. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, Digital Versatile Disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can accessed by a computer. In addition, communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media as known to those skilled in the art.
Finally, it should be noted that: the above embodiments are only used for illustrating the technical solutions of the present disclosure, and not for limiting the same; while the present disclosure has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art will understand that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present disclosure.
Claims (11)
1. A vehicle data encryption analysis method is characterized by comprising the following steps:
the method comprises the steps that an edge server obtains an encryption analysis request of vehicle data sent by an intelligent vehicle;
the edge server analyzes the encryption analysis request to obtain an encryption analysis scheme; and the number of the first and second groups,
the edge server returns the encryption analysis scheme to the smart vehicle to cause the smart vehicle to encrypt its vehicle data based on the encrypted analysis scheme.
2. The method of claim 1, wherein the encrypted analysis request carries vehicle data,
after the edge server obtains the encryption analysis request of the vehicle data sent by the intelligent vehicle, the method further comprises the following steps:
the edge server generating a service analysis plan based on the vehicle data; and the number of the first and second groups,
the edge server returns the server analysis scheme to the intelligent vehicle so that the intelligent vehicle can conduct vehicle navigation based on the service analysis scheme.
3. The method of claim 1, wherein the edge server analyzes the cryptanalysis request to obtain a cryptanalysis scheme, comprising:
the edge server generates an initial encryption scheme by using a multi-factor encryption algorithm based on the encryption analysis request;
the edge server determines optimization parameters of the initial encryption scheme; and the number of the first and second groups,
and the edge server analyzes the initial encryption scheme according to the optimization parameters to obtain an encryption analysis scheme.
4. The method of claim 3, wherein the encryption analysis request carries an authentication credential and a timestamp of the encryption analysis request, and the multi-factor encryption algorithm is obtained according to the following formula:
b∈(10，100)
in the formula, S _ N represents an authentication credential ciphertext, S _ N' represents an authentication credential plaintext, T represents a timestamp, and b represents a nonce.
5. The method of claim 3, wherein the edge server analyzes the initial encryption scheme for the optimization parameters to obtain an encryption analysis scheme, comprising:
the edge server sets an iteration initial parameter and a maximum iteration number;
the edge server carries out deep analysis on the initial encryption scheme aiming at the optimization parameters to obtain an intermediate encryption scheme with the optimal matching degree;
the edge server judges whether the intermediate encryption scheme with the optimal matching degree meets a preset evaluation condition or not;
if the preset evaluation condition is met, the edge server selects the intermediate encryption scheme with the optimal matching degree as an encryption analysis scheme;
if the current iteration times are not greater than the maximum iteration times, the edge server judges whether the current iteration times are not greater than the maximum iteration times;
if the number of iterations is not greater than the maximum number of iterations, the edge server performs deep unsupervised learning on the optimization parameter to obtain an optimization parameter with the number of iterations added by 1, and the step of performing deep analysis on the initial encryption scheme by the edge server according to the optimization parameter is returned;
and if the maximum iteration number is greater than the maximum iteration number, the edge server selects the intermediate encryption scheme with the optimal matching degree as an encryption analysis scheme.
6. The method of claim 5, wherein the optimization parameters include a congestion rate, a unit authentication cost, and a response delay rate, and wherein the optimization parameters are stored in a three-dimensional vector form as:
wherein,
expressing an optimization parameter during the kth iteration, wherein k is the iteration number; i. j and t are dimensions, and i is [1, m ]],j∈[1,n],t∈[1，q]M, n and q respectively represent the maximum value of the dimension;
the congestion rate at the k iteration is;
the unit authentication cost in the k iteration;
is the response delay rate at the kth iteration.
7. The method of claim 6, wherein the edge server performs a deep analysis on the initial encryption scheme according to the optimization parameters, and the deep analysis is obtained according to the following formula:
in the formula, Min ZKIs shown asObtaining an intermediate encryption scheme with an optimal matching degree during k iterations, CGmin、EGmin、WGminRespectively, a historical minimum unit authentication cost, a historical minimum response delay rate, and a historical minimum congestion rate.
8. The method according to claim 6, wherein the edge server performs deep unsupervised learning on the optimization parameter to obtain an optimization parameter obtained by adding 1 to the iteration number, and the optimization parameter is obtained according to the following formula:
in the formula,
an optimization parameter representing the number of iterations at the k +1 st time, comprising
The information vector of the three aspects is that,
representing the unit authentication cost for the k +1 th iteration,
represents the response delay rate at the k +1 th iteration,
represents the time of the iteration number being k +1The rate of the plugging is higher than the rate of the plugging,
representing a depth unsupervised learning enhancement factor when the iteration times are the (k + 1) th time;
wherein the deep unsupervised learning enhancement factor
Obtained according to the following formula:
in the formula, CGminRepresenting the historical minimum unit authentication cost, EGminRepresents the historical minimum response delay rate, WGminIndicating a historical minimum congestion rate.
9. The method according to claim 6, wherein the edge server determines whether the intermediate encryption scheme with the optimal matching degree satisfies a preset evaluation condition, and obtains the result according to the following formula:
in the formula,
representing the unit authentication cost product probability at the kth iteration,
representing the probability of the response delay rate product at the kth iteration,
representing the probability of congestion rate product at the k-th iteration.
10. An edge server, characterized by comprising a memory in which a computer program is stored and a processor that executes the vehicle data encryption analysis method according to any one of claims 1 to 9 when the processor runs the computer program stored in the memory.
11. A computer-readable storage medium, on which a computer program is stored, characterized in that the computer program, when executed by a processor, executes a vehicle data encryption analysis method according to any one of claims 1 to 9.
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