Identity authentication method, device and equipment of Internet of Things terminal, medium and product

Abstract

The invention provides an identity authentication method and device of an Internet of Things terminal, equipment, a medium and a product, and the identity authentication method of the Internet of Things terminal comprises the steps: carrying out the identity authentication of a communication signal of the Internet of Things terminal through employing a trained authentication model, the authentication model is configured to be trained based on a communication signal generated by simulation, a channel impulse response and a twin communication environment generation signal sample, and the twin communication environment is configured to be constructed according to construction information of the communication environment and the user position information. Through the embodiment of the invention, the possibility of over-fitting of the neural network can be reduced, the precision and the error-tolerant rate of the authentication model are improved, the authentication accuracy is enhanced, and the identity safety and the communication reliability of the industrial Internet of Things terminal are improved.

Description

Background Art

[0002] Currently, due to the openness of wireless channels, users at any location can receive signals.

[0003] In related technologies, eavesdroppers can disguise themselves as trusted users by tampering with MAC (Media Access Control) addresses and then launch cloning attacks, disrupting legitimate communications between trusted users and posing huge security threats and property losses to the Industrial Internet of Things.

[0004] However, the defects of traditional identity authentication include at least the following:

[0005] (1) As the computing performance of attackers increases, cryptography-based authentication methods are unable to guarantee the identity security of terminals.

[0006] (2) As the number of terminals increases, the compatibility of identity authentication will decrease because each terminal node has an independent public key certificate.

[0007] (3) For resource-constrained end users, their computing performance is limited and they are not suitable for two-way authentication schemes.

[0008] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present disclosure, and therefore may include information that does not constitute the prior art known to ordinary technicians in the field. Summary of the invention

[0009] The purpose of the present disclosure is to provide an identity authentication method, device, equipment, medium and product for an Internet of Things terminal, which is used to overcome the problems of poor reliability of identity authentication of the Internet of Things terminal due to the limitations and defects of related technologies, at least to a certain extent.

[0010] According to a first aspect of an embodiment of the present disclosure, a method for identity authentication of an Internet of Things terminal is provided, comprising: using a trained authentication model to authenticate a communication signal of the Internet of Things terminal, the authentication model being configured to be trained based on simulated communication signals, channel impulse responses, and signal samples generated by a twin communication environment, and the twin communication environment being configured to be constructed based on construction information of the communication environment and the user location information.

[0011] In an exemplary embodiment of the present disclosure, before using the trained authentication model to authenticate the communication signal of the IoT terminal, the method further includes:

[0012] Obtaining the construction information of the communication environment and user location information corresponding to the IoT terminal;

[0013] Constructing a twin communication environment of the Internet of Things terminal according to the construction information of the communication environment and the user location information;

[0014] Determining the channel impulse response according to preset configuration parameters between the Internet of Things terminal and the receiving end;

[0015] generating a signal sample according to the simulated communication signal, the channel impulse response and the twin communication environment;

[0016] The signal samples are input into the authentication model for training until the loss function of the authentication model meets the convergence condition.

[0017] In an exemplary embodiment of the present disclosure, obtaining the configuration information of the communication environment and the user location information corresponding to the IoT terminal includes:

[0018] Obtaining user location information corresponding to the Internet of Things terminal;

[0019] Performing channel estimation according to the user location information and the address of the receiving end to determine communication channel parameters under the communication environment, wherein the communication channel parameters include at least one of channel state information, power spectrum density, and power amplifier characteristics;

[0020] Acquiring communication environment information through a sensor device associated with the IoT terminal;

[0021] The configuration information is determined according to the communication environment information and the communication channel parameters.

[0022] In an exemplary embodiment of the present disclosure, determining the channel impulse response according to preset configuration parameters between the Internet of Things terminal and the receiving end includes:

[0023] Obtain antenna configuration information and a specified channel model corresponding to the Internet of Things terminal;

[0024] A channel impulse response between the Internet of Things terminal and the receiving end is generated according to the user location information, the antenna configuration information and the specified channel model.

[0025] In an exemplary embodiment of the present disclosure, generating a signal sample according to the simulated communication signal, the channel impulse response, and the twin communication environment includes:

[0026] Modulating a transmission signal of a signal sample to be generated;

[0027] An orthogonal frequency division multiplexing algorithm is used to divide the transmission signal after modulation processing to obtain multiple subcarrier signals;

[0028] Extracting multiple subcarrier signals according to a preset number;

[0029] Performing camouflage processing on the designated subcarrier signal according to a preset attack code strategy to generate a negative sample, and determining the subcarrier that has not been camouflaged as a positive sample;

[0030] The signal samples are generated according to the positive samples, the negative samples, the channel impulse response and the twin communication environment.

[0031] In an exemplary embodiment of the present disclosure, generating the signal sample according to the positive sample, the negative sample, the channel impulse response and the twin communication environment includes:

[0032] Performing convolution processing on the positive sample and the negative sample through the channel impulse response;

[0033] A noise signal is added to the positive sample and / or negative sample after convolution processing according to the twin communication environment, and the positive sample and / or negative sample to which the noise signal is added is determined as the simulated communication signal.

[0034] In an exemplary embodiment of the present disclosure, obtaining antenna configuration information and a specified channel model corresponding to the Internet of Things terminal includes:

[0035] Obtain antenna configuration information corresponding to the Internet of Things terminal;

[0036] Determine communication path information between the Internet of Things terminal and the receiving end, the communication path information includes path loss and / or multipath fading, the path loss includes free space path loss or dual-path loss, and the multipath fading includes Rayleigh fading model and / or Rice fading;

[0037] and / or determining shadow fading information of a log-normally distributed analog signal;

[0038] The designated channel model is determined according to the communication path information and / or the shadow fading information.

[0039] According to a second aspect of an embodiment of the present disclosure, there is provided an identity authentication device for an Internet of Things terminal, comprising:

[0040] The authentication module is configured to use a trained authentication model to authenticate the communication signal of the Internet of Things terminal, wherein the authentication model is configured to be trained based on simulated communication signals, channel impulse responses, and signal samples generated by a twin communication environment, and the twin communication environment is configured to be constructed based on the construction information of the communication environment and the user location information.

[0041] According to a third aspect of the present disclosure, there is provided an electronic device, comprising: a memory; and a processor coupled to the memory, wherein the processor is configured to execute any one of the methods described above based on instructions stored in the memory.

[0042] According to a fourth aspect of the present disclosure, a computer-readable storage medium is provided, on which a program is stored, and when the program is executed by a processor, the identity authentication method of an Internet of Things terminal as described in any one of the above items is implemented.

[0043] According to a fifth aspect of the present disclosure, a computer program product is provided, comprising a computer program, wherein when the computer program is executed by a processor, the computer program implements the identity authentication method for an Internet of Things terminal as described in any one of the above.

[0044] The disclosed embodiment performs identity authentication on the communication signal of the Internet of Things terminal by adopting a trained authentication model, wherein the authentication model is configured to be trained based on simulated communication signals, channel impulse responses, and signal samples generated by a twin communication environment, and the twin communication environment is configured to be constructed according to the construction information of the communication environment and the user location information, thereby reducing the possibility of overfitting of the neural network, improving the accuracy and fault tolerance of the authentication model, enhancing the accuracy of authentication, and improving the identity security and communication reliability of the industrial Internet of Things terminal.

[0045] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The accompanying drawings herein are incorporated into the specification and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the specification are used to explain the principles of the present disclosure. Obviously, the accompanying drawings described below are only some embodiments of the present disclosure, and for ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without creative work.

[0047] Figure 1 A schematic diagram showing an exemplary system architecture of an identity authentication solution for an Internet of Things terminal to which an embodiment of the present invention can be applied;

[0048] Figure 2 is a flow chart of an identity authentication method for an Internet of Things terminal in an exemplary embodiment of the present disclosure;

[0049] Figure 3 is a flow chart of another identity authentication method for an IoT terminal in an exemplary embodiment of the present disclosure;

[0050] Figure 4is a flow chart of another identity authentication method for an IoT terminal in an exemplary embodiment of the present disclosure;

[0051] Figure 5 is a flow chart of another identity authentication method for an IoT terminal in an exemplary embodiment of the present disclosure;

[0052] Figure 6 is a flow chart of another identity authentication method for an IoT terminal in an exemplary embodiment of the present disclosure;

[0053] Figure 7 is a flow chart of another identity authentication method for an IoT terminal in an exemplary embodiment of the present disclosure;

[0054] Figure 8 is a flow chart of another identity authentication method for an IoT terminal in an exemplary embodiment of the present disclosure;

[0055] Fig. 9 It is a workflow diagram of each module under an identity authentication architecture of an Internet of Things terminal in an exemplary embodiment of the present disclosure;

[0056] Fig.10 is a block diagram of an identity authentication device for an Internet of Things terminal in an exemplary embodiment of the present disclosure;

[0057] Fig.11 is a block diagram of an electronic device in an exemplary embodiment of the present disclosure. DETAILED DESCRIPTION

[0058] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as being limited to the examples set forth herein; on the contrary, these embodiments are provided so that the present disclosure will be more comprehensive and complete, and the concepts of the example embodiments are fully conveyed to those skilled in the art. The described features, structures, or characteristics may be combined in one or more embodiments in any suitable manner. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present disclosure. However, those skilled in the art will appreciate that the technical solutions of the present disclosure may be practiced while omitting one or more of the specific details, or other methods, components, devices, steps, etc. may be adopted. In other cases, known technical solutions are not shown or described in detail to avoid obscuring various aspects of the present disclosure.

[0059] In addition, the accompanying drawings are only schematic diagrams of the present disclosure, and the same reference numerals in the drawings represent the same or similar parts, so their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities, which do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor devices and / or microcontroller devices.

[0060] Figure 1 A schematic diagram showing an exemplary system architecture of an identity authentication solution for an Internet of Things terminal to which an embodiment of the present invention can be applied.

[0061] like Figure 1 As shown, the system architecture 100 may include one or more of terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 is used to provide a medium for communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or optical fiber cables, etc.

[0062] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is only for illustration. According to the implementation requirements, there may be any number of terminal devices, networks and servers. For example, the server 105 may be a server cluster composed of multiple servers.

[0063] Users can use terminal devices 101, 102, 103 to interact with server 105 through network 104 to receive or send messages, etc. Terminal devices 101, 102, 103 can be various electronic devices with display screens, including but not limited to smart phones, tablet computers, portable computers, desktop computers, etc.

[0064] In some embodiments, the identity authentication method of the Internet of Things terminal provided in the embodiments of the present invention is generally executed by the server 105. Accordingly, the identity authentication device of the Internet of Things terminal is generally set in the terminal device 103 (or the terminal device 101 or 102). In other embodiments, some terminals may have functions similar to those of the server device to execute the method.

[0065] The exemplary embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.

[0066] Figure 2 It is a flowchart of an identity authentication method for an Internet of Things terminal in an exemplary embodiment of the present disclosure.

[0067] refer to Figure 2 , the identity authentication methods of IoT terminals can include:

[0068] Step S202: Use the trained authentication model to authenticate the communication signal of the Internet of Things terminal. The authentication model is configured to be trained based on simulated communication signals, channel impulse responses, and signal samples generated by a twin communication environment. The twin communication environment is configured to be constructed according to the construction information of the communication environment and the user location information.

[0069] The disclosed embodiment performs identity authentication on the communication signal of the Internet of Things terminal by adopting a trained authentication model, wherein the authentication model is configured to be trained based on simulated communication signals, channel impulse responses, and signal samples generated by a twin communication environment, and the twin communication environment is configured to be constructed according to the construction information of the communication environment and the user location information, thereby reducing the possibility of overfitting of the neural network, improving the accuracy and fault tolerance of the authentication model, enhancing the accuracy of authentication, and improving the identity security and communication reliability of the industrial Internet of Things terminal.

[0070] Below, below combined Figures 3 to 8 Each step of the identity authentication method of the Internet of Things terminal is described in detail.

[0071] In an exemplary embodiment of the present disclosure, Figure 3 As shown, before using the trained authentication model to authenticate the communication signal of the IoT terminal, it also includes:

[0072] Step S302, obtaining the construction information of the communication environment and the user location information corresponding to the IoT terminal;

[0073] Step S304: constructing a twin communication environment of the Internet of Things terminal according to the construction information of the communication environment and the user location information;

[0074] Step S306, determining the channel impulse response according to preset configuration parameters between the Internet of Things terminal and the receiving end;

[0075] Step S308, generating a signal sample according to the simulated communication signal, the channel impulse response and the twin communication environment;

[0076] Step S310: input the signal sample into the authentication model for training until the loss function of the authentication model meets the convergence condition.

[0077] In an exemplary embodiment of the present disclosure, Figure 4 As shown, obtaining the construction information of the communication environment and the user location information corresponding to the IoT terminal includes:

[0078] Step S402, obtaining user location information corresponding to the Internet of Things terminal;

[0079] Step S404, performing channel estimation according to the user location information and the address of the receiving end to determine communication channel parameters under the communication environment, wherein the communication channel parameters include at least one of channel state information, power spectrum density, and power amplifier characteristics;

[0080] Step S406, acquiring communication environment information through a sensor device associated with the IoT terminal;

[0081] Step S408: determining the construction information according to the communication environment information and the communication channel parameters.

[0082] In an exemplary embodiment of the present disclosure, Figure 5 As shown, determining the channel impulse response according to preset configuration parameters between the Internet of Things terminal and the receiving end includes:

[0083] Step S502, obtaining antenna configuration information and a specified channel model corresponding to the IoT terminal;

[0084] Step S504: Generate a channel impulse response between the Internet of Things terminal and the receiving end according to the user location information, the antenna configuration information and the specified channel model.

[0085] In an exemplary embodiment of the present disclosure, Figure 6 As shown, generating a signal sample according to the simulated communication signal, the channel impulse response and the twin communication environment includes:

[0086] Step S602, modulating the transmission signal of the signal sample to be generated;

[0087] Step S604, using an orthogonal frequency division multiplexing algorithm to divide the modulated transmission signal to obtain multiple subcarrier signals;

[0088] Step S606, extracting a plurality of subcarrier signals according to a preset number;

[0089] Step S608, camouflaging the designated subcarrier signal according to the preset attack code strategy to generate a negative sample, and determining the subcarrier that has not been camouflaged as a positive sample;

[0090] Step S610: Generate the signal sample according to the positive sample, the negative sample, the channel impulse response and the twin communication environment.

[0091] In an exemplary embodiment of the present disclosure, Figure 7As shown, generating the signal sample according to the positive sample, the negative sample, the channel impulse response and the twin communication environment includes:

[0092] Step S702, performing convolution processing on the positive sample and the negative sample through the channel impulse response;

[0093] Step S704: adding a noise signal to the positive samples and / or negative samples after convolution processing according to the twin communication environment, and determining the positive samples and / or negative samples to which the noise signal is added as the simulated generated communication signal.

[0094] In an exemplary embodiment of the present disclosure, Figure 8 As shown, obtaining the antenna configuration information and the specified channel model corresponding to the Internet of Things terminal includes:

[0095] Step S802, obtaining antenna configuration information corresponding to the Internet of Things terminal;

[0096] Step S804, determining the communication path information between the IoT terminal and the receiving end, wherein the communication path information includes path loss and / or multipath fading, the path loss includes free space path loss or dual-path loss, and the multipath fading includes Rayleigh fading model and / or Rice fading;

[0097] And / or, step S806, determining shadow fading information of the log-normal distribution analog signal;

[0098] Step S808: determine the designated channel model according to the communication path information and / or the shadow fading information.

[0099] Fig. 9 It is a workflow diagram of each module under an identity authentication architecture of an Internet of Things terminal in an exemplary embodiment of the present disclosure.

[0100] refer to Fig. 9 The identity authentication architecture of the IoT terminal includes a data acquisition module 902, a digital twin module 904, an authentication model training module 906 and an identity authentication module 908. The workflow of each module includes but is not limited to the following contents.

[0101] In an exemplary embodiment of the present disclosure, in the data acquisition module 902, the physical structure of the industrial environment and the position of each user node are obtained through sensors and monitoring equipment, and the receiving end node obtains physical quantities such as channel state information and received signal strength through channel estimation, which specifically includes the following steps:

[0102] (1) Obtain physical structure information of the industrial environment through sensors and monitoring equipment.

[0103] (2) Establish a coordinate system and obtain the position coordinates (x, y, z) of each user node.

[0104] (3) The receiving node receives the wireless signals sent from various industrial terminals.

[0105] (4) The receiving node obtains physical quantities such as the channel state information, power spectrum density, and power amplifier characteristics of the received signal through channel estimation. The end node obtains the channel state information (CSI) of the received signal through channel estimation technology. The channel state information describes the characteristics of the channel such as fading, delay, and noise, and is the basis for modulation, demodulation, equalization, and other physical layer signal processing. Channel estimation is usually implemented through pilot signals, training sequences, or blind estimation methods, which can help the receiving end better compensate for various attenuation and distortion of the signal during transmission and improve the receiving performance.

[0106] In an exemplary embodiment of the present disclosure, the data acquisition module 902 transmits the industrial environment entity construction information and the coordinate information of the user node to the digital twin module 904. The data acquisition module 902 transmits the received physical quantities to the certification model training module 906.

[0107] In an exemplary embodiment of the present disclosure, in the digital twin module 904, the parameters obtained by the data acquisition module 902 are used to construct the digital twin world of the industrial Internet of Things, and the transmission and reception process of the wireless signal is simulated through the parameter information such as the position, antenna, and carrier of each user, so as to obtain the signal samples transmitted by the users at each position received by the receiving node.

[0108] In an exemplary embodiment of the present disclosure, the digital twin module 904 constructs a digital twin world of the industrial Internet of Things using the parameters transmitted by the data acquisition module 902, and simulates the sending and receiving process of wireless signals through parameter information such as the position, antenna, and carrier of each user, specifically including the following steps:

[0109] (1) Establishing the user channel model: that is, establishing the channel model between the user and the receiving end based on the user's location and antenna configuration. Commonly used channel models include:

[0110] (1.1) Path loss model: Calculates the path loss of a signal as it propagates in space. The path loss is usually a function of distance, such as using a free space path loss model or a dual-path loss model.

[0111] (1.2) Multipath fading model: Considering the multipath effects caused by buildings, ground, obstacles, etc., the Rayleigh fading model or the Rice fading model is used to simulate the multipath fading of wireless signals.

[0112] (1.3) Shadow fading: Log-normal distribution is used to simulate the shadow effect of signals in different environments, such as signal attenuation when passing through walls or trees.

[0113] (2) Channel response generation, that is, using user location, antenna information and multipath parameters to generate the channel impulse response between the user and the receiving end.

[0114] In an exemplary embodiment of the present disclosure, the channel impulse response is the key to receiving a signal and is used to characterize the attenuation, delay and phase change of the signal in the time and space domains.

[0115] (3) Signal modulation and transmission, that is, using a suitable modulation method to modulate the user's original information (such as QPSK, 16-QAM, etc.). The modulated signal is affected by factors such as path loss, multipath fading, phase rotation, and noise when transmitted through the channel. In addition, the use of OFDM (Orthogonal Frequency Division Multiplexing) technology can split the signal into multiple subcarriers for transmission, thereby resisting frequency selective fading.

[0116] (4) Received signal generation, i.e., convolving the modulated signal with the generated channel impulse response to simulate the propagation process of the signal from the user to the receiving end. Signals from multiple users are superimposed and noise (such as additive white Gaussian noise, AWGN) is added to generate signal samples received by the receiving end. The signal samples transmitted by users at various locations received by the receiving node are generated. When the number of samples is less than the threshold value ρ, the sample signal is continued to be generated. When the number of samples is greater than the threshold value ρ, the next step is performed to transmit the generated signal samples to the authentication model training module 906.

[0117] In an exemplary embodiment of the present disclosure, in the authentication model training module 906, the real signal samples collected by the data acquisition module 902 and the signal samples generated by the digital twin module 904 are used as samples for authentication model training, a neural network is selected, and the parameters of the authentication model are obtained through back propagation and gradient descent, and transmitted to the identity authentication module.

[0118] In an exemplary embodiment of the present disclosure, the authentication model training module 906 selects a neural network among DNN, CNN, and RNN as the authentication model.

[0119] In an exemplary embodiment of the present disclosure, the real signal samples transmitted by the data acquisition module 902 and the signal samples generated by the digital twin module 904 are used as samples for authentication model training. The parameters of the authentication model are optimized by back propagation and gradient descent. When the value of the loss function is greater than the threshold When the value of the loss function is less than the threshold When , the next step is performed. The parameters of the authentication model are transmitted to the identity authentication module 908.

[0120] In an exemplary embodiment of the present disclosure, in the identity authentication module 908, the optimized authentication model is used to authenticate the signal to be authenticated, and an authentication result is obtained. The receiving node receives a signal unknown to the sending end. The receiving node authenticates the signal to be authenticated using the optimized authentication model and obtains an identity authentication result.

[0121] Corresponding to the above method embodiments, the present disclosure also provides an identity authentication device for an Internet of Things terminal, which can be used to execute the above method embodiments.

[0122] Fig.10 It is a block diagram of an identity authentication device of an Internet of Things terminal in an exemplary embodiment of the present disclosure.

[0123] refer to Fig.10 , the identity authentication device 1000 of the Internet of Things terminal may include:

[0124] The authentication module 1002 is configured to use a trained authentication model to authenticate the communication signal of the Internet of Things terminal, and the authentication model is configured to be trained based on simulated communication signals, channel impulse responses and signal samples generated by a twin communication environment, and the twin communication environment is configured to be constructed according to the construction information of the communication environment and the user location information.

[0125] In an exemplary embodiment of the present disclosure, before using the trained authentication model to authenticate the communication signal of the Internet of Things terminal, the identity authentication device 1000 of the Internet of Things terminal is also used to:

[0126] Obtaining the construction information of the communication environment and user location information corresponding to the IoT terminal;

[0127] Constructing a twin communication environment of the Internet of Things terminal according to the construction information of the communication environment and the user location information;

[0128] Determining the channel impulse response according to preset configuration parameters between the Internet of Things terminal and the receiving end;

[0129] generating a signal sample according to the simulated communication signal, the channel impulse response and the twin communication environment;

[0130] The signal samples are input into the authentication model for training until the loss function of the authentication model meets the convergence condition.

[0131] In an exemplary embodiment of the present disclosure, the identity authentication device 1000 of the Internet of Things terminal is also used for:

[0132] Obtaining user location information corresponding to the Internet of Things terminal;

[0133] Performing channel estimation according to the user location information and the address of the receiving end to determine communication channel parameters under the communication environment, wherein the communication channel parameters include at least one of channel state information, power spectrum density, and power amplifier characteristics;

[0134] Acquiring communication environment information through a sensor device associated with the IoT terminal;

[0135] The configuration information is determined according to the communication environment information and the communication channel parameters.

[0136] In an exemplary embodiment of the present disclosure, the identity authentication device 1000 of the Internet of Things terminal is also used for:

[0137] Obtain antenna configuration information and a specified channel model corresponding to the Internet of Things terminal;

[0138] A channel impulse response between the Internet of Things terminal and the receiving end is generated according to the user location information, the antenna configuration information and the specified channel model.

[0139] In an exemplary embodiment of the present disclosure, the identity authentication device 1000 of the Internet of Things terminal is also used for:

[0140] Modulating a transmission signal of a signal sample to be generated;

[0141] An orthogonal frequency division multiplexing algorithm is used to divide the transmission signal after modulation processing to obtain multiple subcarrier signals;

[0142] Extracting multiple subcarrier signals according to a preset number;

[0143] Performing camouflage processing on the designated subcarrier signal according to a preset attack code strategy to generate a negative sample, and determining the subcarrier that has not been camouflaged as a positive sample;

[0144] The signal samples are generated according to the positive samples, the negative samples, the channel impulse response and the twin communication environment.

[0145] In an exemplary embodiment of the present disclosure, the identity authentication device 1000 of the Internet of Things terminal is also used for:

[0146] Performing convolution processing on the positive sample and the negative sample through the channel impulse response;

[0147] A noise signal is added to the positive sample and / or negative sample after convolution processing according to the twin communication environment, and the positive sample and / or negative sample to which the noise signal is added is determined as the simulated communication signal.

[0148] In an exemplary embodiment of the present disclosure, the identity authentication device 1000 of the Internet of Things terminal is also used for:

[0149] Obtain antenna configuration information corresponding to the Internet of Things terminal;

[0150] Determine communication path information between the Internet of Things terminal and the receiving end, the communication path information includes path loss and / or multipath fading, the path loss includes free space path loss or dual-path loss, and the multipath fading includes Rayleigh fading model and / or Rice fading;

[0151] and / or determining shadow fading information of a log-normally distributed analog signal;

[0152] The designated channel model is determined according to the communication path information and / or the shadow fading information.

[0153] Since the functions of the device 1000 have been described in detail in the corresponding method embodiments, the present disclosure will not elaborate on them here.

[0154] Compared with the prior art, the technical solution defined in the embodiments of the present application has the following main advantages:

[0155] 1. In order to give full play to the advantages of digital twins, the physical structure and terminal nodes of the industrial Internet of Things are digitally represented through digital twins, and the sending and receiving process of wireless signals is simulated, thereby generating a large number of wireless signal samples.

[0156] 2. The samples generated by the digital twin world can assist in the training of the authentication model, avoid overfitting of the neural network, and improve the accuracy and fault tolerance of the model.

[0157] Compared with the prior art, the technical solutions defined in the embodiments of the present application are mainly creative in that:

[0158] 1. Compared with the identity authentication method based on digital twins and blockchain, the sample parameter information used for authentication in this application can be self-configured, and the authentication performance will not decrease with the increase of data volume. It has lower latency and is therefore more practical. The authentication method of this application is a one-way authentication and does not require any computing resources from the sending node, so it is a lightweight authentication method. In addition, this application is based on the physical quantity of the wireless channel and can realize "one-time, one-key" encryption authentication, so it is more secure.

[0159] 2. Compared with the neural network-based identity authentication method, this application introduces digital twins to generate additional training samples, avoiding the problem of overfitting of the neural network during the training process, improving the accuracy of authentication, and ensuring the identity security of the industrial Internet of Things terminal.

[0160] It should be noted that, although several modules or units of the device for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. On the contrary, the features and functions of one module or unit described above can be further divided into multiple modules or units to be embodied.

[0161] In an exemplary embodiment of the present disclosure, an electronic device capable of implementing the above method is also provided.

[0162] It will be appreciated by those skilled in the art that various aspects of the present invention may be implemented as a system, method or program product. Therefore, various aspects of the present invention may be specifically implemented in the following forms, namely: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or a combination of hardware and software, which may be collectively referred to herein as a "circuit", "module" or "system".

[0163] Refer to the following Fig.11 The electronic device 1100 according to this embodiment of the present invention is described. Fig.11 The electronic device 1100 shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0164] like Fig.11 As shown, the electronic device 1100 is in the form of a general computing device. The components of the electronic device 1100 may include but are not limited to: at least one processing unit 1110, at least one storage unit 1120, and a bus 1130 connecting different system components (including the storage unit 1120 and the processing unit 1110).

[0165] The storage unit stores program codes, which can be executed by the processing unit 1110, so that the processing unit 1110 performs the steps according to various exemplary embodiments of the present invention described in the above “Exemplary Method” section of this specification. For example, the processing unit 1110 can perform the method shown in the embodiment of the present disclosure.

[0166] The storage unit 1120 may include a readable medium in the form of a volatile storage unit, such as a random access storage unit (RAM) 11201 and / or a cache storage unit 11202 , and may further include a read-only storage unit (ROM) 11203 .

[0167] The storage unit 1120 may also include a program / utility 11204 having a set (at least one) of program modules 11205, such program modules 11205 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0168] Bus 1130 may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0169] The electronic device 1100 may also communicate with one or more external devices 1140 (e.g., keyboards, pointing devices, Bluetooth devices, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 1100, and / or communicate with any device that enables the electronic device 1100 to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed via an input / output (I / O) interface 1150. Furthermore, the electronic device 1100 may also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via a network adapter 1160. As shown, the network adapter 1160 communicates with other modules of the electronic device 1100 via a bus 1130. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 1100, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0170] Through the description of the above implementation, it is easy for those skilled in the art to understand that the example implementation described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the implementation of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the implementation of the present disclosure.

[0171] In an exemplary embodiment of the present disclosure, a computer-readable storage medium is also provided, on which a program product capable of implementing the above method of the present specification is stored. In some possible implementations, various aspects of the present invention can also be implemented in the form of a program product, which includes a program code, and when the program product is run on a terminal device, the program code is used to enable the terminal device to execute the steps according to various exemplary embodiments of the present invention described in the above "Exemplary Method" section of the present specification.

[0172] The program product for implementing the above method according to an embodiment of the present invention can adopt a portable compact disk read-only memory (CD-ROM) and include program code, and can be run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited thereto, and in this document, a readable storage medium can be any tangible medium containing or storing a program, which can be used by or in combination with an instruction execution system, an apparatus or a device.

[0173] Among them, the readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0174] Computer readable signal media may include data signals propagated in baseband or as part of a carrier wave, in which readable program code is carried. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Readable signal media may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0175] The program code embodied on the readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the foregoing.

[0176] In an exemplary embodiment of the present disclosure, a computer program product is also provided, which can be loaded or stored in any combination of one or more readable media, and can be written in any combination of one or more programming languages ​​to perform the program code of the present invention, including object-oriented programming languages ​​such as Java, C++, etc., and also conventional procedural programming languages ​​such as "C" language or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, as an independent software package, partially on the user computing device and partially on a remote computing device, or completely on a remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (for example, using an Internet service provider to connect through the Internet).

[0177] In addition, the above-mentioned figures are only schematic illustrations of the processes included in the method according to an exemplary embodiment of the present invention, and are not intended to be limiting. It is easy to understand that the processes shown in the above-mentioned figures do not indicate or limit the time sequence of these processes. In addition, it is also easy to understand that these processes can be performed synchronously or asynchronously, for example, in multiple modules.

[0178] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art that are not disclosed in the present disclosure. The specification and examples are to be considered exemplary only, and the true scope and concept of the present disclosure are indicated by the claims.

Claims

1. An identity authentication method for an Internet of Things terminal, characterized in that: include: The trained authentication model is used to authenticate the communication signal of the Internet of Things terminal. The authentication model is configured to be trained based on simulated communication signals, channel impulse responses and signal samples generated by a twin communication environment. The twin communication environment is configured to be constructed according to the construction information of the communication environment and the user location information.

2. The identity authentication method of the Internet of Things terminal according to claim 1, characterized in that: Before using the trained authentication model to authenticate the communication signal of the IoT terminal, it also includes: Obtaining the construction information of the communication environment and user location information corresponding to the IoT terminal; Constructing a twin communication environment of the Internet of Things terminal according to the construction information of the communication environment and the user location information; Determining the channel impulse response according to preset configuration parameters between the IoT terminal and the receiving end; generating a signal sample according to the simulated communication signal, the channel impulse response and the twin communication environment; The signal samples are input into the authentication model for training until the loss function of the authentication model meets the convergence condition.

3. The identity authentication method of the Internet of Things terminal according to claim 2, characterized in that: Obtaining the construction information of the communication environment and user location information corresponding to the IoT terminal includes: Obtaining user location information corresponding to the Internet of Things terminal; Performing channel estimation according to the user location information and the address of the receiving end to determine communication channel parameters under the communication environment, wherein the communication channel parameters include at least one of channel state information, power spectrum density, and power amplifier characteristics; Acquiring communication environment information through a sensor device associated with the IoT terminal; The configuration information is determined according to the communication environment information and the communication channel parameters.

4. The identity authentication method of the Internet of Things terminal according to claim 2, characterized in that: Determining the channel impulse response according to preset configuration parameters between the Internet of Things terminal and the receiving end includes: Obtain antenna configuration information and a specified channel model corresponding to the IoT terminal; A channel impulse response between the Internet of Things terminal and the receiving end is generated according to the user location information, the antenna configuration information and the specified channel model.

5. The identity authentication method of the Internet of Things terminal according to claim 2, characterized in that: Generating a signal sample according to the simulated communication signal, the channel impulse response and the twin communication environment includes: Modulating a transmission signal of a signal sample to be generated; An orthogonal frequency division multiplexing algorithm is used to divide the transmission signal after modulation processing to obtain multiple subcarrier signals; Extracting multiple subcarrier signals according to a preset number; Performing camouflage processing on the designated subcarrier signal according to a preset attack code strategy to generate a negative sample, and determining the subcarrier that has not been camouflaged as a positive sample; The signal samples are generated according to the positive samples, the negative samples, the channel impulse response and the twin communication environment.

6. The identity authentication method of the Internet of Things terminal according to claim 5, characterized in that: Generating the signal sample according to the positive sample, the negative sample, the channel impulse response and the twin communication environment includes: Performing convolution processing on the positive sample and the negative sample through the channel impulse response; A noise signal is added to the positive sample and / or negative sample after convolution processing according to the twin communication environment, and the positive sample and / or negative sample to which the noise signal is added is determined as the simulated communication signal.

7. The identity authentication method of the Internet of Things terminal according to claim 4, characterized in that: Obtaining antenna configuration information and a specified channel model corresponding to the IoT terminal includes: Obtain antenna configuration information corresponding to the Internet of Things terminal; Determine communication path information between the Internet of Things terminal and the receiving end, the communication path information includes path loss and / or multipath fading, the path loss includes free space path loss or dual-path loss, and the multipath fading includes Rayleigh fading model and / or Rice fading; and / or determining shadow fading information of a log-normally distributed analog signal; The designated channel model is determined according to the communication path information and / or the shadow fading information.

8. An identity authentication device for an Internet of Things terminal, characterized in that: include: The authentication module is configured to use a trained authentication model to authenticate the communication signal of the Internet of Things terminal, wherein the authentication model is configured to be trained based on simulated communication signals, channel impulse responses, and signal samples generated by a twin communication environment, and the twin communication environment is configured to be constructed based on construction information of the communication environment and user location information.

9. An electronic device, characterized in that: include: Memory; as well as A processor coupled to the memory, the processor being configured to execute the identity authentication method for an Internet of Things terminal according to any one of claims 1 to 7 based on instructions stored in the memory.

10. A computer-readable storage medium having a program stored thereon, wherein when the program is executed by a processor, the identity authentication method for an Internet of Things terminal according to any one of claims 1 to 7 is implemented.

11. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the identity authentication method of the Internet of Things terminal as described in any one of claims 1 to 7 is implemented.

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