Device identity generation method and apparatus, and computer device

Abstract

The application discloses a device identity generation method and device and a computer device. The method comprises the following steps: obtaining multi-dimensional attribute information of a target device, and performing feature mapping on the multi-dimensional attribute information to obtain an original feature vector, wherein the multi-dimensional attribute information at least comprises hardware information, software configuration information and network configuration information of the target device; generating noise of the target device according to the multi-dimensional attribute information, and adding the noise of the target device into the original feature vector to obtain a feature vector after adding noise; and generating an identity of the target device based on the feature vector after adding noise. The application solves the technical problem that the device identity is invalid in the related art.

Description

Technical Field

[0001] The present application relates to the technical field of device identification generation, and in particular to a method, apparatus and computer device for generating a device identity. Background Art

[0002] With the rapid development of the Internet of Things (IoT) and smart devices, communication and interaction between devices are becoming increasingly complex. Accurately and securely identifying and verifying the identity of each device has become a critical issue. Traditional device authentication methods rely on static hardware or software features, such as MAC addresses or device serial numbers. However, these static attributes are vulnerable to physical damage, software updates, or malicious attacks, resulting in unstable or insecure authentication.

[0003] Furthermore, as the number of devices increases and their functionality becomes more complex, relying solely on static attributes for authentication can no longer meet the demands of modern communication environments. For example, when a device is in different operating states or environments, its communication behavior and characteristics may change, making authentication methods based on static attributes ineffective in certain situations. Summary of the Invention

[0004] The embodiments of the present application provide a method, apparatus, and computer device for generating a device identity, so as to at least solve the technical problem in the related art that device identities are prone to failure.

[0005] According to one aspect of an embodiment of the present application, a method for generating a device identity is provided, including: obtaining multi-dimensional attribute information of a target device, and performing feature mapping on the multi-dimensional attribute information to obtain an original feature vector, wherein the multi-dimensional attribute information includes at least: hardware information, software configuration information, and network configuration information of the target device; generating noise of the target device based on the multi-dimensional attribute information, and adding the noise of the target device to the original feature vector to obtain a feature vector after adding noise; and generating an identity of the target device based on the feature vector after adding noise.

[0006] Optionally, the multi-dimensional attribute information is feature mapped to obtain the original feature vector, including: obtaining the hardware information, the hardware information including the processor model, memory capacity, storage capacity, and IO port configuration information of the target device; obtaining the software configuration information, the software configuration information including the operating system version, the installed application list and runtime library; obtaining the network configuration information, the network configuration information including the MAC address of the target device, the IP address of the target device, the list of network devices connected to the target device, the communication protocol type and its version; converting the multi-dimensional attribute information of the target device into a data set in a target format, and using a nonlinear mapping function to feature map the data set in the target format to obtain the original feature vector.

[0007] Optionally, the noise of the target device is generated according to the multi-dimensional attribute information, and the noise of the target device is added to the original feature vector to obtain the feature vector after adding the noise, including: determining the distribution mode of the noise vector according to the distribution characteristics of the original feature vector; generating the noise vector with the same dimension as the original feature vector according to the distribution mode of the noise vector, and the distribution mode of the noise vector includes: Gaussian distribution and Laplace distribution; performing weighted fusion on the noise vector and the original feature vector to obtain the feature vector after adding the noise, wherein the weight of the weighted fusion is determined according to the change of the multi-dimensional attribute information of the target device after the fusion of the noise.

[0008] Optionally, determining the distribution mode of the noise vector according to the distribution characteristics of the original eigenvector includes: obtaining statistical parameters of the original eigenvector, the statistical parameters including at least a mean and a variance; adjusting the original eigenvector to obtain a mean of 0 and a variance of 1 for the adjusted original eigenvector; performing a skewness test on the adjusted original eigenvector to obtain a skewness test result; when the skewness test result indicates that the adjusted original eigenvector is normally distributed, determining that the distribution mode of the noise vector is a Gaussian distribution; when the skewness test result indicates that the adjusted original eigenvector is positively skewed or negatively skewed, determining that the distribution mode of the noise vector is a Laplace distribution.

[0009] Optionally, generating the identity of the target device based on the feature vector after adding noise includes: obtaining operating status data of the target device, the operating status data including processor usage, memory usage, battery power, and network connection status; determining a state weight based on the operating status data, the state weight being used to characterize the degree of influence of each operating state on the feature vector after adding noise; adjusting the feature vector after adding noise based on the state weight to obtain a target feature vector; and generating the identity of the target device based on the target feature vector.

[0010] Optionally, determining the state weight according to the operating status data includes: normalizing the operating status data to obtain the normalized operating status data; obtaining a first weight and a second weight of the normalized operating status data, wherein the first weight is used to represent the weight of the maximum value of the operating status data, and the second weight is used to represent the weight of the minimum value of the operating status data; determining the state weight of the operating status data based on the normalized operating status data, the first weight and the second weight.

[0011] Optionally, generating the identity of the target device based on the target feature vector includes: converting the target feature vector into a continuous byte stream; encrypting the continuous byte stream to obtain a ciphertext; and determining the ciphertext as the identity of the target device.

[0012] According to another aspect of an embodiment of the present application, a device identity generation device is also provided, including: an acquisition module for acquiring multi-dimensional attribute information of a target device, and performing feature mapping on the multi-dimensional attribute information to obtain an original feature vector, wherein the multi-dimensional attribute information includes at least: hardware information, software configuration information and network configuration information of the target device; a fusion module for generating noise of the target device based on the multi-dimensional attribute information, and adding the noise of the target device to the original feature vector to obtain a feature vector after adding noise; a generation module for generating an identity of the target device based on the feature vector after adding noise.

[0013] According to another aspect of the embodiments of the present application, a computer device is provided, including: a memory and a processor, wherein the memory is used to store program instructions; the processor is connected to the memory and is used to execute the above-mentioned device identity generation method.

[0014] According to another aspect of the embodiments of the present application, a non-volatile storage medium is provided, which includes a stored computer program, wherein the device where the non-volatile storage medium is located executes the above-mentioned device identity generation method by running the computer program.

[0015] According to another aspect of the embodiments of the present application, a computer program product is provided, including computer instructions, which implement the above-mentioned device identity generation method when executed by a processor.

[0016] In an embodiment of the present application, multi-dimensional attribute information of a target device is obtained, and feature mapping of the multi-dimensional attribute information is performed to obtain an original feature vector, wherein the multi-dimensional attribute information includes at least: hardware information, software configuration information and network configuration information of the target device; noise of the target device is generated according to the multi-dimensional attribute information, and the noise of the target device is added to the original feature vector to obtain a feature vector after adding noise; an identity of the target device is generated based on the feature vector after adding noise, thereby achieving the purpose of adding noise to the attribute information of the target device and thereby increasing the stability of the original feature vector, thereby achieving the technical effect of improving the stability of the target device identification, and thereby solving the technical problem of easy failure of the device identity identification in the related technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0018] Figure 1 This is a hardware structure block diagram of a computer terminal for implementing a method for generating a device identity according to an embodiment of the present application;

[0019] Figure 2 is a flow chart of a method for generating a device identity according to an embodiment of the present application;

[0020] Figure 3 This is a structural diagram of a device identity generation apparatus according to an embodiment of the present application. DETAILED DESCRIPTION

[0021] In order to enable those skilled in the art to better understand the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of this application.

[0022] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in a sequence other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0023] The information collected in the embodiments of the present application is information and data authorized by the account or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of the relevant data comply with the relevant laws, regulations and standards of the relevant regions, take necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation entrances for the account to choose to authorize or reject the automated decision-making results; if the account chooses to reject, it enters the expert decision-making process.

[0024] In order to solve the problems existing in the related art, the embodiment of the present application provides a method for generating a device identity. The method can be run on Figure 1 In the computer terminal shown, the computer terminal is explained below.

[0025] The device identity generation method embodiment provided in the embodiment of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Figure 1 The hardware structure block diagram of a computer terminal for implementing a method for generating a device identity is shown. Figure 1 As shown, the computer terminal 10 may include one or more (illustrated by 102a, 102b, ..., 102n in the figure) processors (the processor may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory 104 for storing data, and a transmission module 106 for communication functions connected via a wired and / or wireless network. In addition, it may also include: a display, a keyboard, a cursor control device, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of the I / O interface), a network interface, and a BUS bus. It will be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 1 More or fewer components than shown, or with Figure 1 Different configurations shown.

[0026] It should be noted that the one or more processors and / or other data processing circuits described above may generally be referred to herein as "data processing circuitry." The data processing circuitry may be embodied in whole or in part as software, hardware, firmware, or any other combination thereof. Furthermore, the data processing circuitry may be a single, independent processing module, or may be incorporated in whole or in part into any of the other components of the computer terminal 10. As described in the embodiments of the present application, the data processing circuitry serves as a processor control (e.g., selection of a variable resistor terminal path connected to an interface).

[0027] The memory 104 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the device identity generation method in the embodiment of the present application. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory 104, that is, implementing the above-mentioned device identity generation method. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include a memory remotely located relative to the processor, and these remote memories may be connected to the computer terminal 10 via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0028] The transmission module 106 is configured to receive or transmit data via a network. A specific example of the aforementioned network may include a wireless network provided by the communications provider of the computer terminal 10. In one embodiment, the transmission module 106 includes a network interface controller (NIC), which can be connected to other network devices via a base station to enable communication with the Internet. In another embodiment, the transmission module 106 may be a radio frequency (RF) module, which is configured to communicate with the Internet wirelessly.

[0029] The display may be, for example, a touch screen liquid crystal display (LCD), which enables the account to interact with the account interface of the computer terminal 10 .

[0030] It should be noted that, in some optional embodiments, the above Figure 1 The computer terminal shown may include hardware elements (including circuits), software elements (including computer code stored on a computer-readable medium), or a combination of hardware elements and software elements. Figure 1This is merely one example of a particular embodiment and is intended to illustrate the types of components that may be present in the computer terminal described above.

[0031] In the above-mentioned operating environment, an embodiment of the present application provides an embodiment of a method for generating a device identity. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0032] Figure 2 This is a flow chart of a method for generating a device identity according to an embodiment of the present application. Figure 2 As shown, the method includes the following steps:

[0033] Step S202: Acquire multi-dimensional attribute information of the target device and perform feature mapping on the multi-dimensional attribute information to obtain an original feature vector, wherein the multi-dimensional attribute information includes at least hardware information, software configuration information, and network configuration information of the target device;

[0034] Step S204: generating noise of the target device according to the multi-dimensional attribute information, and adding the noise of the target device to the original feature vector to obtain a feature vector after adding noise;

[0035] Step S206: Generate an identity of the target device based on the feature vector after adding noise.

[0036] Through the above steps S202 to S206, the multi-dimensional attribute information of the target device is obtained, and the original feature vector is obtained by feature mapping the multi-dimensional attribute information, wherein the multi-dimensional attribute information includes at least: the hardware information, software configuration information and network configuration information of the target device; the noise of the target device is generated according to the multi-dimensional attribute information, and the noise of the target device is added to the original feature vector to obtain the feature vector after adding noise; the identity of the target device is generated based on the feature vector after adding noise, thereby achieving the purpose of adding noise to the attribute information of the target device and thereby increasing the stability of the original feature vector, thereby achieving the technical effect of improving the stability of the target device identification and solving the technical problem of the easy failure of the device identity in the related art. The following is a detailed description.

[0037] In step S202 of the above-mentioned device identity generation method, the multi-dimensional attribute information is feature mapped to obtain the original feature vector, including: obtaining the hardware information, the hardware information including the processor model, memory capacity, storage capacity, and IO port configuration information of the target device; obtaining the software configuration information, the software configuration information including the operating system version, the installed application list and the runtime library; obtaining the network configuration information, the network configuration information including the MAC address of the target device, the IP address of the target device, the list of network devices to which the target device is connected, the communication protocol type and its version; converting the multi-dimensional attribute information of the target device into a data set in a target format, and using a nonlinear mapping function to feature map the data set in the target format to obtain the original feature vector.

[0038] It can be understood that the original feature vector is processed by adding noise to increase the robustness of the original feature vector, so that the consistency of the vector is still guaranteed when the device's properties change slightly. The noise is dynamically generated based on the target device's own property information, and the noise generated by each device is unique.

[0039] Specifically, hardware information is extracted from the hardware management module of the target device, network configuration information is extracted from the network management module or communication interface of the target device, and software configuration information is extracted from the operating system or application layer of the target device.

[0040] It should be noted that the target format can be a digital format or a binary encoding format.

[0041] It can be understood that there are many nonlinear mapping functions. An optional method in the embodiment of the present application is: normalize the data set in a unified format to ensure that the value of each attribute is in the same scale or range, providing a balanced data basis for nonlinear mapping; use radial basis functions to map the original data space to a high-dimensional feature space to ensure that the data is separable in the high-dimensional space; in the high-dimensional feature space, apply principal component analysis technology to select the principal component with the largest variance to obtain the reduced eigenvector (original eigenvector).

[0042] In some embodiments of the present application, the noise of the target device is generated according to the multi-dimensional attribute information, and the noise of the target device is added to the original feature vector to obtain the feature vector after adding the noise, including: determining the distribution mode of the noise vector according to the distribution characteristics of the original feature vector; generating the noise vector with the same dimension as the original feature vector according to the distribution mode of the noise vector, and the distribution mode of the noise vector includes: Gaussian distribution and Laplace distribution; performing weighted fusion on the noise vector and the original feature vector to obtain the feature vector after adding the noise, wherein the weight of the weighted fusion is determined according to the change of the multi-dimensional attribute information of the target device after the fusion of the noise.

[0043] Specifically, according to the distribution characteristics of the original eigenvector, a noise distribution model is selected, wherein the noise distribution model includes a Gaussian distribution or a Laplace distribution; a noise vector with the same dimension as the original eigenvector is generated, wherein each element of the noise vector is independently generated based on the determined noise distribution model; the generated noise vector is weightedly fused with the original eigenvector to obtain a eigenvector with enhanced robustness, wherein the weight of the weighted fusion is determined according to the rate of change of the device attributes to ensure that the fused eigenvector remains consistent with the original eigenvector when the device attributes change slightly; the fused eigenvector is standardized to be within a predefined range for subsequent processing and application.

[0044] In some embodiments of the present application, the distribution mode of the noise vector is determined according to the distribution characteristics of the original eigenvector, including: obtaining statistical parameters of the original eigenvector, the statistical parameters including at least mean and variance; adjusting the original eigenvector to obtain a mean of 0 and a variance of 1 for the adjusted original eigenvector; performing a skewness test on the adjusted original eigenvector to obtain a skewness test result; when the skewness test result indicates that the adjusted original eigenvector is normally distributed, determining that the distribution mode of the noise vector is Gaussian distribution; when the skewness test result indicates that the adjusted original eigenvector is positively skewed or negatively skewed, determining that the distribution mode of the noise vector is Laplace distribution.

[0045] Specifically, the statistical parameters of the original eigenvector, including mean, variance and skewness, are calculated; the original eigenvector is adjusted according to the mean and variance so that its mean is 0 and its variance is 1, thereby obtaining a standardized eigenvector; a skewness test is performed on the standardized eigenvector to determine the skewness of its distribution. Based on the result of the skewness test: if the original eigenvector is close to a normal distribution, a Gaussian distribution is selected as the noise distribution model; if the original eigenvector shows positive skewness or negative skewness, a Laplace distribution is selected as the noise distribution model; the parameters of the determined noise distribution model provide a basis for subsequent noise generation.

[0046] The specific steps of generating the identity of the target device based on the feature vector after adding noise are as follows: obtaining the operating status data of the target device, the operating status data including processor usage, memory usage, battery power, and network connection status; determining a state weight based on the operating status data, the state weight being used to characterize the degree of influence of each operating state on the feature vector after adding noise; adjusting the feature vector after adding noise based on the state weight to obtain a target feature vector; and generating the identity of the target device based on the target feature vector.

[0047] Specifically, real-time operating status data is collected from the monitoring or management module of the target device, and the data includes processor usage, memory usage, battery power, and network connection status; based on the collected real-time operating status data, a state weight vector is calculated, where each weight value represents the degree of influence of the corresponding state data on the feature vector; the state weight vector is multiplied element by element by the feature vector after adding noise to obtain a weight-adjusted feature vector; the weight-adjusted feature vector is normalized to ensure that it is within a predefined range for subsequent processing and application.

[0048] In some embodiments of the application, determining the state weight based on the operating status data includes: normalizing the operating status data to obtain the normalized operating status data; obtaining a first weight and a second weight of the normalized operating status data, wherein the first weight is used to represent the weight of the maximum value of the operating status data, and the second weight is used to represent the weight of the minimum value of the operating status data; determining the state weight of the operating status data based on the normalized operating status data, the first weight, and the second weight.

[0049] Specifically, the running state data S={s1,s2,…,s n} as an example, where s i Represents the i-th state data such as CPU usage and memory usage, and performs normalization processing so that each state data is in the range of [0,1].

[0050] For the normalized running state data S′, calculate its corresponding state weight vector:

[0051] W={w1,w2,…,w n}, where weight w i The calculation formula is:

[0052] w i =α×s′ i +β×(1-s′ i );

[0053] Here, α represents the first weight, and β represents the second weight.

[0054] The calculated state weight vector W is normalized again to be within a predefined range so as to facilitate multiplication with the feature vector after adding noise.

[0055] Furthermore, the element-by-element multiplication operation specifically includes:

[0056] Let V={v1,v2,…,v n} is the original feature vector after adding noise, where each element v i Represents the i-th feature after noise processing:

[0057] Let W={w1,w2,…,w n} is the state weight vector, where each weight value w i Represents the degree of influence of the corresponding state data on the feature vector;

[0058] The weighted feature vector V′ is calculated by element-by-element multiplication operation. The specific calculation method is: V′={v′1,v′2,…,v′ n};

[0059] Among them, the calculation method of each element is: v′ i =v i ×w i ;

[0060] Each feature v i According to the corresponding state weight value w i Adjust to get the weight-adjusted feature v′ i , all weighted features are combined into the target feature vector V′={v′1,v′2,…,v′ n}.

[0061] In some embodiments of the present application, generating the identity of the target device based on the target feature vector includes: converting the target feature vector into a continuous byte stream; encrypting the continuous byte stream to obtain a ciphertext; and determining the ciphertext as the identity of the target device.

[0062] The proposed device identity generation method combines the device's self-describing features with real-time operating status data to generate a weighted feature vector. First, by adding noise to the original feature vector, the robustness of the feature vector to slight changes in the device's attributes is effectively improved. Subsequently, the feature vector is further optimized based on the device's real-time operating status, ensuring that different devices can still be effectively distinguished under different operating states, thereby significantly improving the accuracy of device identification.

[0063] Furthermore, a block encryption algorithm is used to convert the weighted feature vectors into a block-encrypted identity, providing the device with a unique, fixed-length identifier. This design not only ensures that the original feature vector information cannot be easily recovered from the block-encrypted identity, but also ensures that legitimate decryption processes can restore the original information when access is required. This bidirectional nature may provide greater flexibility in some application scenarios while still enhancing the security of the device identity. Furthermore, the use of block encryption not only protects the device's self-describing features and operating status information, making it less vulnerable to exploitation by malicious entities, but also provides an additional layer of protection, helping to maintain the security of the entire system.

[0064] The device identity generation method provided in this application takes into account multiple aspects such as the real-time operating status of the device and the robustness of the feature vector. This makes this method not only suitable for device identification in a fixed environment, but also adaptable to the authentication needs of the device in different environments and states. In addition, by flexibly selecting different block encryption algorithms and parameters, the device authentication process can be customized and optimized according to the needs of actual applications.

[0065] Figure 3 is a structural diagram of a device identity generating apparatus according to an embodiment of the present application, such as Figure 3 As shown, the device includes:

[0066] An acquisition module 30 is configured to acquire multi-dimensional attribute information of a target device and perform feature mapping on the multi-dimensional attribute information to obtain an original feature vector, wherein the multi-dimensional attribute information includes at least hardware information, software configuration information, and network configuration information of the target device;

[0067] A fusion module 32 is configured to generate noise of the target device based on the multi-dimensional attribute information, and add the noise of the target device to the original feature vector to obtain a feature vector after adding noise;

[0068] The generating module 34 is configured to generate an identity of the target device based on the feature vector after adding noise.

[0069] Through the above-mentioned device identity identification generation device, it is achieved by obtaining multi-dimensional attribute information of the target device and performing feature mapping on the multi-dimensional attribute information to obtain the original feature vector, wherein the multi-dimensional attribute information at least includes: hardware information, software configuration information and network configuration information of the target device; generating noise of the target device according to the multi-dimensional attribute information, and adding the noise of the target device to the original feature vector to obtain the feature vector after adding noise; generating the identity of the target device based on the feature vector after adding noise, achieving the purpose of adding noise to the attribute information of the target device and thereby increasing the stability of the original feature vector, thereby realizing the technical effect of improving the stability of the target device identification, and thus solving the technical problem of the easy failure of the device identity identification in the related technology.

[0070] In the above-mentioned device identity generation device, the acquisition module 30 includes: a mapping submodule, which is used to perform feature mapping on the multi-dimensional attribute information to obtain the original feature vector, including: obtaining the hardware information, the hardware information includes the processor model, memory capacity, storage capacity, and IO port configuration information of the target device; obtaining the software configuration information, the software configuration information includes the operating system version, the installed application list and the runtime library; obtaining the network configuration information, the network configuration information includes the MAC address of the target device, the IP address of the target device, the list of network devices to which the target device is connected, the communication protocol type and its version; converting the multi-dimensional attribute information of the target device into a data set in a target format, and using a nonlinear mapping function to perform feature mapping on the data set in the target format to obtain the original feature vector.

[0071] The fusion module 32 includes: a fusion submodule, which is used to generate the noise of the target device according to the multi-dimensional attribute information, and add the noise of the target device to the original feature vector to obtain the feature vector after adding the noise, including: determining the distribution mode of the noise vector according to the distribution characteristics of the original feature vector; generating the noise vector with the same dimension as the original feature vector according to the distribution mode of the noise vector, and the distribution mode of the noise vector includes: Gaussian distribution and Laplace distribution; performing weighted fusion on the noise vector and the original feature vector to obtain the feature vector after adding the noise, wherein the weight of the weighted fusion is determined according to the change of the multi-dimensional attribute information of the target device after the fusion of the noise.

[0072] The fusion submodule includes: a determination unit, which is used to determine the distribution mode of the noise vector according to the distribution characteristics of the original feature vector, including: obtaining statistical parameters of the original feature vector, the statistical parameters including at least mean and variance; adjusting the original feature vector to obtain a mean of 0 and a variance of 1 for the adjusted original feature vector; performing a skewness test on the adjusted original feature vector to obtain a skewness test result; when the skewness test result indicates that the adjusted original feature vector is normally distributed, determining that the distribution mode of the noise vector is Gaussian distribution; when the skewness test result indicates that the adjusted original feature vector is positively skewed or negatively skewed, determining that the distribution mode of the noise vector is Laplace distribution.

[0073] The generation module 34 includes: a generation submodule, which is used to generate the identity of the target device based on the feature vector after adding noise, including: obtaining the operating status data of the target device, the operating status data including processor usage, memory usage, battery power, and network connection status; determining a state weight based on the operating status data, the state weight being used to characterize the degree of influence of each operating state on the feature vector after adding noise; adjusting the feature vector after adding noise based on the state weight to obtain a target feature vector; and generating the identity of the target device based on the target feature vector.

[0074] The generation submodule includes: a weight unit, which is used to determine the state weight according to the operating status data, including: normalizing the operating status data to obtain the normalized operating status data; obtaining the first weight and the second weight of the normalized operating status data, wherein the first weight is used to represent the weight of the maximum value of the operating status data, and the second weight is used to represent the weight of the minimum value of the operating status data; determining the state weight of the operating status data based on the normalized operating status data, the first weight and the second weight.

[0075] The weight unit includes: a generating subunit for generating the identity of the target device based on the target feature vector, including: converting the target feature vector into a continuous byte stream; encrypting the continuous byte stream to obtain a ciphertext; and determining the ciphertext as the identity of the target device.

[0076] It should be noted that Figure 3 The device identification generating device shown is used to perform Figure 2 The device identity generating method shown in the figure, therefore the relevant explanations in the above device identity generating method are also applicable to the device identity generating device, and will not be repeated here.

[0077] An embodiment of the present application also provides a computer device, including: a memory and a processor, wherein the memory is used to store program instructions; the processor is connected to the memory and is used to execute the above-mentioned device identity generation method.

[0078] An embodiment of the present application further provides a non-volatile storage medium, which includes a stored computer program, wherein the device where the non-volatile storage medium is located executes the above-mentioned device identity generation method by running the computer program.

[0079] An embodiment of the present application also provides a computer program product, including a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the device identity generation method in the present application are implemented.

[0080] An embodiment of the present application also provides a computer program product, including computer instructions, which, when executed by a processor, implement the steps of the device identity generation method in the present application.

[0081] An embodiment of the present application also provides a computer program, which, when executed by a processor, implements the steps of the device identity generation method in the present application.

[0082] The serial numbers of the above embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0083] In the above embodiments of the present application, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0084] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only exemplary. For example, the division of the units can be a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0085] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0086] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0087] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for enabling a computer device (which can be a personal computer, a server or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk.

[0088] The above is only a preferred embodiment of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for generating a device identity, characterized in that: include: Acquire multi-dimensional attribute information of a target device, and perform feature mapping on the multi-dimensional attribute information to obtain an original feature vector, wherein the multi-dimensional attribute information includes at least: hardware information, software configuration information, and network configuration information of the target device; generating noise of the target device according to the multi-dimensional attribute information, and adding the noise of the target device to the original feature vector to obtain a feature vector after adding noise; generating an identity of the target device based on the feature vector after adding noise; Generating the noise of the target device according to the multi-dimensional attribute information, and adding the noise of the target device to the original feature vector to obtain the feature vector after adding the noise, including: Determining a distribution mode of the noise vector according to the distribution characteristics of the original eigenvector; generating a noise vector having the same dimension as the original feature vector according to a distribution mode of the noise vector, wherein the distribution mode of the noise vector includes: Gaussian distribution and Laplace distribution; Performing weighted fusion on the noise vector and the original feature vector to obtain the feature vector after adding noise, wherein the weight of the weighted fusion is determined according to the change of the multi-dimensional attribute information of the target device after the noise is fused; Determining the distribution mode of the noise vector according to the distribution characteristics of the original feature vector includes: Obtaining statistical parameters of the original eigenvector, wherein the statistical parameters include at least a mean and a variance; Adjust the original eigenvector to obtain an adjusted original eigenvector with a mean of 0 and a variance of 1; Performing a skewness test on the adjusted original eigenvector to obtain a skewness test result; When the skewness test result indicates that the adjusted original eigenvector is normally distributed, determining that the distribution mode of the noise vector is Gaussian distribution; When the skewness test result indicates that the adjusted original eigenvector is positively skewed or negatively skewed, it is determined that the distribution mode of the noise vector is Laplace distribution.

2. The method according to claim 1, characterized in that Performing feature mapping on the multi-dimensional attribute information to obtain an original feature vector includes: Obtaining the hardware information, including the processor model, memory capacity, storage capacity, and IO port configuration information of the target device; Obtaining the software configuration information, wherein the software configuration information includes an operating system version, a list of installed applications, and a runtime library; Acquire the network configuration information, wherein the network configuration information includes the MAC address of the target device, the IP address of the target device, a list of network devices to which the target device is connected, and a communication protocol type and version thereof; The multi-dimensional attribute information of the target device is converted into a data set in a target format, and a nonlinear mapping function is used to perform feature mapping on the data set in the target format to obtain the original feature vector.

3. The method according to claim 1, characterized in that Generating the identity of the target device based on the feature vector after adding noise includes: Obtaining operating status data of the target device, wherein the operating status data includes processor usage, memory usage, battery power, and network connection status; Determining a state weight according to the operating state data, wherein the state weight is used to characterize the degree of influence of each operating state on the feature vector after adding noise; Adjusting the noise-added feature vector based on the state weight to obtain a target feature vector; An identity of the target device is generated based on the target feature vector.

4. The method according to claim 3, characterized in that Determining a state weight according to the operating state data includes: Normalizing the operating status data to obtain normalized operating status data; Obtaining a first weight and a second weight of the normalized running status data, wherein the first weight is used to represent the weight of the maximum value of the running status data, and the second weight is used to represent the weight of the minimum value of the running status data; The operating status data is determined based on the normalized operating status data, the first weight, and the second weight.

5. The method according to claim 3, characterized in that Generating an identity of the target device based on the target feature vector includes: Converting the target feature vector into a continuous byte stream; Encrypting the continuous byte stream to obtain ciphertext; The ciphertext is determined as the identity of the target device.

6. A device identity generation device, characterized in that: include: an acquisition module, configured to acquire multi-dimensional attribute information of a target device and perform feature mapping on the multi-dimensional attribute information to obtain an original feature vector, wherein the multi-dimensional attribute information includes at least hardware information, software configuration information, and network configuration information of the target device; a fusion module, configured to generate noise of the target device according to the multi-dimensional attribute information, and add the noise of the target device to the original feature vector to obtain a feature vector after adding noise; A generating module, configured to generate an identity of the target device based on the feature vector after adding noise; Generating the noise of the target device according to the multi-dimensional attribute information, and adding the noise of the target device to the original feature vector to obtain the feature vector after adding the noise, including: Determining a distribution mode of the noise vector according to the distribution characteristics of the original eigenvector; generating a noise vector having the same dimension as the original feature vector according to a distribution mode of the noise vector, wherein the distribution mode of the noise vector includes: Gaussian distribution and Laplace distribution; Performing weighted fusion on the noise vector and the original feature vector to obtain the feature vector after adding noise, wherein the weight of the weighted fusion is determined according to the change of the multi-dimensional attribute information of the target device after the noise is fused; Determining the distribution mode of the noise vector according to the distribution characteristics of the original feature vector includes: Obtaining statistical parameters of the original eigenvector, wherein the statistical parameters include at least a mean and a variance; Adjust the original eigenvector to obtain an adjusted original eigenvector with a mean of 0 and a variance of 1; Performing a skewness test on the adjusted original eigenvector to obtain a skewness test result; When the skewness test result indicates that the adjusted original eigenvector is normally distributed, determining that the distribution mode of the noise vector is Gaussian distribution; When the skewness test result indicates that the adjusted original eigenvector is positively skewed or negatively skewed, it is determined that the distribution mode of the noise vector is Laplace distribution.

7. A computer device, characterized in that: include: A memory and a processor, wherein the memory is used to store program instructions; The processor is connected to the memory and is used to execute the device identity generation method described in any one of claims 1 to 5.

8. A computer program product comprising computer instructions, characterized in that When the computer instructions are executed by a processor, the device identity generating method according to any one of claims 1 to 5 is implemented.

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