Identity authentication method and device, equipment, storage medium and computer program product

Abstract

The invention discloses an identity authentication method and device, equipment, a storage medium and a computer program product, and relates to the technical field of safety certification, and the method comprises the steps: obtaining a real-time behavior feature value of a user using distributed equipment and a real-time space feature value of the distributed equipment in real time, constructing a real-time behavior feature vector according to the real-time behavior feature value, and carrying out the real-time space feature value of the distributed equipment; constructing a real-time spatial feature vector according to the real-time spatial feature value; the similarity between a real-time behavior feature vector and an initial behavior feature vector and the feature distance between a real-time spatial feature vector and an initial spatial feature vector are calculated according to feature matching, and the initial behavior feature vector and the initial spatial feature vector are obtained when the distributed device accesses the distributed network for the first time; and if the similarity is greater than or equal to a preset similarity threshold and the feature distance is less than or equal to a preset distance threshold, the identity authentication of the user is passed. The invention aims to solve the technical problem of low reliability of user identity authentication.

Description

Technical Field

[0001] The present application relates to the field of security authentication technology, and in particular to an identity authentication method, an identity authentication apparatus, an identity authentication device, a storage medium, and a computer program product. Background Art

[0002] Traditional static user identity authentication methods can only authenticate the identity of a distributed device when it first connects to a distributed network. When the user's behavioral characteristics of using distributed devices change, such as sudden changes in user input methods and operation rhythms, and when the spatial characteristics of distributed devices change, for example, in some scenarios, the movement of distributed devices is limited to a certain range, and the distributed device is used outside the range; this dynamic change traditional static user identity authentication cannot identify the current characteristics of distributed devices and users using distributed devices in real time, and abnormal user authentication will appear. The distributed network introduces security threats and it is difficult to reliably authenticate user identities. Summary of the invention

[0003] The main purpose of this application is to provide an identity authentication method, an identity authentication device, an identity authentication equipment, a storage medium and a computer program product, aiming to solve the technical problem of low reliability of user identity authentication.

[0004] To achieve the above purpose, the present application proposes an identity authentication method, the method comprising:

[0005] Acquire real-time behavior feature values ​​of users using distributed devices and real-time spatial feature values ​​of the distributed devices, wherein a real-time behavior feature vector is constructed according to the real-time behavior feature values, and a real-time spatial feature vector is constructed according to the real-time spatial feature values;

[0006] Calculating the similarity between the real-time behavior feature vector and the initial behavior feature vector, and the feature distance between the real-time space feature vector and the initial space feature vector according to feature matching, wherein the initial behavior feature vector and the initial space feature vector are obtained when the distributed device first accesses the distributed network;

[0007] If the similarity is greater than or equal to a preset similarity threshold and the feature distance is less than or equal to a preset distance threshold, the user's identity authentication is successful.

[0008] In one embodiment, the step of acquiring the real-time behavior feature value of the user using the distributed device and the real-time spatial feature value of the distributed device in real time includes:

[0009] Collecting real-time behavior feature data of users operating distributed devices in real time, and converting the real-time behavior feature data into real-time behavior feature values;

[0010] Real-time spatial feature data of distributed devices are collected in real time, and the real-time spatial feature data are converted into real-time spatial feature values.

[0011] In one embodiment, the step of respectively calculating the similarity between the real-time behavior feature vector and the initial behavior feature vector and the feature distance between the real-time space feature vector and the initial space feature vector according to feature matching comprises:

[0012] The similarity is obtained by calculating the cosine similarity between the real-time behavior feature vector and the initial behavior feature vector;

[0013] The feature distance is obtained by calculating the Euclidean distance between the real-time space feature vector and the initial space feature vector.

[0014] In one embodiment, the identity authentication method further includes:

[0015] Acquire historical data, wherein the historical data includes a behavior feature vector of a user in a historical period, a spatial feature vector of a distributed device in a historical period, and a user authentication result obtained according to the behavior feature vector of the user in the historical period and the spatial feature vector of the distributed device in the historical period;

[0016] Analyzing the historical data through a preset neural network model to obtain behavioral features and spatial features that have a probability of influencing the user authentication result exceeding a preset probability threshold;

[0017] Identity authentication is performed based on the weighted behavioral features and the spatial features.

[0018] In one embodiment, the identity authentication method further includes:

[0019] When the distributed device first accesses the distributed network, obtaining a device identifier of the distributed device and an identity identifier of a user using the distributed device according to an access request of the distributed device to access the distributed network;

[0020] Bind the device identifier and the identity identifier, the user and the distributed device to obtain an association relationship between the user and the distributed device;

[0021] The number of distributed devices used by the user is queried according to the association relationship, and if the number exceeds a preset number threshold, the identity of the user is verified.

[0022] In one embodiment, the identity authentication method includes:

[0023] If the user's identity authentication fails, the user whose identity authentication fails is determined to be an abnormal user;

[0024] Terminate the abnormal user's access to the distributed device.

[0025] In addition, to achieve the above purpose, the present application also proposes an identity authentication device, the identity authentication device comprising:

[0026] An acquisition module, used for acquiring in real time a real-time behavior feature vector of a user using a distributed device and a real-time spatial feature vector of the distributed device;

[0027] A matching module, used to calculate the similarity between the real-time behavior feature vector and the initial behavior feature vector, and the feature distance between the real-time space feature vector and the initial space feature vector according to feature matching, wherein the initial behavior feature vector and the initial space feature vector are obtained when the distributed device first accesses the distributed network;

[0028] The authentication module is used to pass the user's identity authentication if the similarity is greater than or equal to a preset similarity threshold and the feature distance is less than or equal to a preset distance threshold.

[0029] In addition, to achieve the above-mentioned purpose, the present application also proposes an identity authentication device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the identity authentication method described above.

[0030] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the identity authentication method described above are implemented.

[0031] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the steps of the identity authentication method described above are implemented.

[0032] Compared with the traditional static identity authentication method, authentication is only performed when the distributed device first accesses the distributed network, which is difficult to cope with the dynamic changes in the spatial characteristics of the distributed device and the behavioral characteristics of the user using the distributed device, resulting in low reliability of authentication; the present application continuously obtains the user's behavioral feature values ​​and the spatial feature values ​​of the distributed device, and constructs a real-time feature vector based on the real-time feature value, and then matches them with the initial feature vector when the distributed network is first accessed. If the behavioral feature vector and the spatial feature vector are both matched successfully, the user identity authentication is passed; continuously monitoring the user's behavioral characteristics and the spatial characteristics of the distributed device, and performing identity authentication through dynamic data, can solve the technical problem of low reliability caused by the existing technology relying on static information for identity authentication. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0034] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0035] Figure 1 A flowchart of the first embodiment of the identity authentication method of this application is provided;

[0036] Figure 2 A flowchart of the second embodiment of the identity authentication method of this application is provided;

[0037] Figure 3 A brief flowchart of the third embodiment of the identity authentication method of this application is provided;

[0038] Figure 4 This is a schematic diagram of the module structure of the identity authentication device for this application;

[0039] Figure 5 This is a schematic diagram of the device structure of the hardware operating environment involved in the identity authentication method of this application. DETAILED DESCRIPTION

[0040] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0041] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0042] It should be noted that the execution subject of this embodiment may be an identity authentication device, or a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or an electronic device, a processor, etc. capable of realizing the above functions. The following takes the identity authentication device as an example to illustrate this embodiment and the following embodiments.

[0043] Based on this, the present application embodiment provides an identity authentication method, referring to Figure 1 , Figure 1 This is a flowchart of the first embodiment of the identity authentication method of this application.

[0044] In this embodiment, the identity authentication method includes steps S10 to S30:

[0045] Step S10, obtaining real-time behavior feature values ​​of users using distributed devices and real-time spatial feature values ​​of distributed devices in real time, wherein a real-time behavior feature vector is constructed according to the real-time behavior feature values, and a real-time spatial feature vector is constructed according to the real-time spatial feature values;

[0046] It should be noted that distributed devices refer to a group of computing devices connected through a network. These devices can be computers, servers, sensors, smart devices, etc.; real-time behavior characteristics refer to the user's operating habits, interaction patterns, etc., and real-time behavior feature values ​​refer to the quantified values ​​of real-time behavior characteristics, such as the operating habit feature value is 10, and the interaction pattern feature value is 20; real-time behavior feature vector refers to the set of quantified values ​​of the user's behavior pattern or habit data at a certain point in time; real-time behavior characteristics refer to the position, movement trajectory, angle, etc. of the physical space where the distributed devices are located, and the real-time behavior feature vector refers to the set of quantified values ​​of the distributed devices at a certain point in time.

[0047] By tracking the user's immediate operations through real-time behavioral feature values ​​and monitoring the changes in the device's environment through real-time spatial feature values, it is possible to perform identity authentication and monitor abnormal situations based on user behavior and device status to enhance security.

[0048] In one embodiment, step S10 includes steps A10 to A20:

[0049] Step A10, collecting real-time behavior feature data of users operating distributed devices in real time, and converting the real-time behavior feature data into real-time behavior feature values;

[0050] It should be noted that the real-time collection of behavioral feature data of users and devices includes but is not limited to clicks, slides, text input, video viewing and other behaviors; the real-time behavioral feature data is converted into real-time behavioral feature values. This process involves data cleaning, preprocessing and feature extraction, and the purpose is to convert the behavioral data into a format that the computer can understand and process.

[0051] Step A20: collecting real-time spatial feature data of distributed devices in real time, and converting the real-time spatial feature data into real-time spatial feature values.

[0052] It should be noted that real-time collection of spatial feature data on distributed devices and conversion of the spatial data into numerical features to describe the spatial attributes of the distributed devices is conducive to feature matching.

[0053] In this embodiment, user operation and environmental data are collected in real time and converted into feature values, providing instant data for the identity authentication device, capturing and analyzing dynamic changes in user behavior and changes in the location of distributed devices, thereby being able to quickly respond to user abnormal conditions and improve the computing efficiency of the identity authentication device.

[0054] Step S20, respectively calculating the similarity between the real-time behavior feature vector and the initial behavior feature vector, and the feature distance between the real-time space feature vector and the initial space feature vector according to feature matching, wherein the initial behavior feature vector and the initial space feature vector are obtained when the distributed device first accesses the distributed network;

[0055] It should be noted that the initial behavior feature vector is a data set of the user's initial behavior pattern recorded when the distributed device first accesses the network, such as the first interaction mode, operating habits, etc.; the initial spatial feature vector is a data set of the physical environment characteristics of the device when it first accesses the network, including but not limited to location coordinates, movement trajectory, etc.; compare the similarity of the "real-time behavior feature vector" and the "initial behavior feature vector" to determine whether the user behavior has changed significantly, calculate the distance between the "real-time spatial feature vector" and the "initial spatial feature vector" to determine whether the device location or environment has changed significantly. By calculating the similarity and feature distance, abnormal user behavior can be identified and the security of devices in the distributed network can be maintained.

[0056] In one embodiment, step S20 includes steps B10 to B20:

[0057] Step B10, obtaining similarity by calculating the cosine similarity between the real-time behavior feature vector and the initial behavior feature vector;

[0058] It should be noted that the real-time behavior feature vector Initial space eigenvector The similarity is calculated using the following formula:

[0059]

[0060] By calculating the cosine similarity between the real-time behavior feature vector and the initial behavior feature vector, the similarity is obtained, which is used to evaluate the consistency between the current behavior pattern and the historical behavior pattern.

[0061] Step B20, obtaining the feature distance by calculating the Euclidean distance between the real-time spatial feature vector and the initial spatial feature vector.

[0062] It should be noted that the real-time spatial feature vector Initial space eigenvector The feature distance is calculated using the following formula:

[0063]

[0064] By calculating the Euclidean distance between the real-time spatial feature vector and the initial spatial feature vector, the feature distance is obtained, that is, the difference between the two vectors is quantified. According to the quantified result, it is judged that the geographical location and trajectory of the distributed device have not deviated abnormally.

[0065] In this embodiment, by calculating the cosine similarity of the real-time behavior feature vector and the initial behavior feature vector, the similarity is obtained, the changing trend of user behavior is captured in real time, and the real-time behavior that deviates significantly from the historical behavior pattern is identified, which is conducive to timely discovering potential security threats such as account theft and system intrusion, and taking timely security measures; by comparing the Euclidean distance between the real-time spatial feature vector and the initial spatial feature vector, the dynamic changes can be tracked and analyzed, and timely responses can be made to authentication anomalies.

[0066] Step S30: If the similarity is greater than or equal to the preset similarity threshold and the feature distance is less than or equal to the preset distance threshold, the user's identity authentication is passed.

[0067] It should be noted that the higher the similarity, the more similar the two are; the smaller the feature distance, the more overlapping the spatial positions are.

[0068] represents the similarity, where represents the real-time behavior feature vector, represents the initial behavior feature vector;

[0069] represents the feature distance, where represents the real-time spatial feature vector, represents the initial space eigenvector;

[0070] when and When the user's identity authentication is passed, sim threshold Indicates the preset similarity threshold, dist threshold Indicates the preset distance threshold.

[0071] This embodiment provides an identity authentication method, which continuously obtains the user's behavioral feature values ​​and the spatial feature values ​​of the distributed devices, and constructs a real-time feature vector based on the real-time feature values, and then matches them with the initial feature vectors when accessing the distributed network. If the behavioral feature vector and the spatial feature vector are both matched successfully, the user identity authentication is successful; continuously monitoring the user's behavioral characteristics and the distributed device's spatial characteristics, and performing identity authentication through dynamic data can solve the technical problem of low reliability caused by the existing technology relying on static information for identity authentication.

[0072] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the first embodiment can refer to the above introduction, and will not be repeated in the following. Figure 2 The identity authentication method further includes steps D10 to D30:

[0073] Step D10, obtaining historical data, wherein the historical data includes a behavior feature vector of a user in a historical period, a spatial feature vector of a distributed device in a historical period, and a user authentication result obtained according to the behavior feature vector of the user in the historical period and the spatial feature vector of the distributed device in the historical period;

[0074] It should be noted that historical data is obtained to analyze user behavior patterns and device status changes, providing a basis for selecting behavioral features and spatial features with greater influence.

[0075] Step D20, analyzing the historical data through a preset neural network model to obtain behavioral features and spatial features that have a probability of affecting the user authentication result exceeding a preset probability threshold;

[0076] It should be noted that the historical data is analyzed by a preset neural network model, wherein the training process of the preset neural network is trained using existing common means, such as data preprocessing, feature value extraction, convergence, etc. Exemplarily, the preset probability threshold can be set to 80%, and when a certain behavior feature is greater than or equal to 80% through the neural network model, the behavior feature is identified as an important feature affecting authentication.

[0077] Step D30, performing identity authentication based on the weighted behavioral features and spatial features.

[0078] It should be noted that behavioral features and spatial features with greater influence are given greater weights. For example, the original weight of clicking the screen is 10%. According to the analysis of the preset neural network model, clicking the screen is a behavioral feature with greater influence when performing identity authentication, so the original weight of 10% is increased to 30%.

[0079] In this embodiment, by using a preset neural network model to deeply study historical data, it is possible to extract features that have a significant impact on user authentication results. When the probability of a feature's impact exceeds a set probability threshold, these features are weighted. The greater the weight, the greater the influence of the feature in decision-making. Dynamically adjusting feature weights, identifying and excluding low-impact features, not only improves the accuracy and efficiency of identity authentication, but also enables more secure identity authentication.

[0080] In another embodiment, the identity authentication method further includes steps E10 to E30:

[0081] Step E10, when the distributed device first accesses the distributed network, according to the access request of the distributed device to access the distributed network, obtaining the device identifier of the distributed device and the identity identifier of the user using the distributed device;

[0082] It should be noted that a distributed network is a network system composed of numerous distributed devices; the device identifier is a string that uniquely identifies each distributed device, and is used to accurately distinguish and locate each distributed device in the distributed network; the user identity identifier is a string used to uniquely identify the user who uses the distributed device, and is used for user authentication, authority management, etc.

[0083] Step E20, binding the device identifier and the identity identifier, the user and the distributed device, to obtain an association relationship between the user and the distributed device;

[0084] It should be noted that association is the process of establishing a relationship between distributed devices and users. By pairing device identifiers with identity identifiers, it is possible to track which devices a user is using and the interaction history between the user and distributed devices.

[0085] Step E30, querying the number of distributed devices used by the user according to the association relationship, and if the number exceeds a preset number threshold, verifying the identity of the user.

[0086] It should be noted that the association relationship is the association relationship between the user and the device. By querying the user's identity identifier, the number of distributed devices used by the user can be obtained. For example, user A used device A, device B, device C, and device D. The set usage quantity threshold is 3. At this time, user A used 4 distributed devices, which exceeds the set quantity threshold. In this case, the user's identity needs to be verified to ensure whether the user uses the distributed devices abnormally.

[0087] In this embodiment, when a distributed device accesses the network for the first time, its device identifier and the identity identifier of the user using the device are recorded. Next, the two identifiers are linked to establish an association between the user and the device, thereby achieving efficient management of the distributed devices. The number of distributed devices used by the user is monitored through association, and if it is greater than a preset number, the user identity is verified to ensure the security of the network and maintain the security of the distributed network.

[0088] Based on the first embodiment of the present application, in the third embodiment of the present application, the same or similar contents as those in the first embodiment can refer to the above introduction, and will not be repeated later. Figure 3 The identity authentication method further includes steps T10 to T20:

[0089] Step T10, if the user's identity authentication fails, determining the user whose identity authentication fails is an abnormal user;

[0090] It should be noted that identity authentication failure refers to the user's verification information failing to meet preset thresholds such as similarity and feature distance; abnormal users refer to users whose identity authentication fails or whose behavior patterns deviate significantly from the normal range, including malicious attempts, stolen devices, etc.

[0091] Step T20, terminating the abnormal user's permission to access the distributed device.

[0092] It should be noted that since the user is identified as an abnormal user, the abnormal user's access rights need to be terminated. This is to prevent the abnormal user from continuing to access or operate distributed devices and their resources and prevent data from being stolen or destroyed.

[0093] In this embodiment, by confirming users with identity authentication abnormalities as abnormal users and terminating the abnormal users' access to distributed devices, it is possible to respond to security threats in a timely manner, maintain the stability and security of the network environment, effectively prevent data leakage, and improve security.

[0094] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the identity authentication method of the present application. More simple transformations based on this technical concept are all within the scope of protection of the present application.

[0095] This application also provides an identity authentication device, please refer to Figure 4 , the identity authentication device comprises:

[0096] An acquisition module 10 is used to acquire in real time the real-time behavior feature value of the user using the distributed device and the real-time space feature value of the distributed device, wherein a real-time behavior feature vector is constructed according to the real-time behavior feature value, and a real-time space feature vector is constructed according to the real-time space feature value;

[0097] A matching module 20, configured to calculate the similarity between the real-time behavior feature vector and the initial behavior feature vector, and the feature distance between the real-time space feature vector and the initial space feature vector according to feature matching, wherein the initial behavior feature vector and the initial space feature vector are obtained when the distributed device first accesses the distributed network;

[0098] The authentication module 30 is configured to pass the user identity authentication if the similarity is greater than or equal to a preset similarity threshold and the feature distance is less than or equal to a preset distance threshold.

[0099] Optionally, the acquisition module 10 is further used to collect real-time behavior feature data of users operating distributed devices in real time, and convert the real-time behavior feature data into real-time behavior feature values;

[0100] Real-time spatial feature data of distributed devices are collected in real time, and the real-time spatial feature data are converted into real-time spatial feature values.

[0101] Optionally, the matching module 20 is further configured to obtain similarity by calculating cosine similarity between the real-time behavior feature vector and the initial behavior feature vector;

[0102] The feature distance is obtained by calculating the Euclidean distance between the real-time space feature vector and the initial space feature vector.

[0103] The identity authentication device comprises: acquiring historical data, wherein the historical data comprises a behavior feature vector of a user in a historical period, a spatial feature vector of a distributed device in a historical period, and a user authentication result obtained according to the behavior feature vector of the user in the historical period and the spatial feature vector of the distributed device in the historical period;

[0104] Analyzing the historical data through a preset neural network model to obtain behavioral features and spatial features that have a probability of influencing the user authentication result exceeding a preset probability threshold;

[0105] Identity authentication is performed based on the weighted behavioral features and the spatial features.

[0106] The identity authentication device comprises: when the distributed device first accesses the distributed network, according to the access request of the distributed device to access the distributed network, obtaining the device identifier of the distributed device and the identity identifier of the user using the distributed device;

[0107] Bind the device identifier and the identity identifier, the user and the distributed device to obtain an association relationship between the user and the distributed device;

[0108] The number of distributed devices used by the user is queried according to the association relationship, and if the number exceeds a preset number threshold, the identity of the user is verified.

[0109] The identity authentication device comprises: if the identity authentication of the user fails, determining the user whose identity authentication fails is an abnormal user;

[0110] Terminate the abnormal user's access to the distributed device.

[0111] The identity authentication device provided by the present application adopts the identity authentication method in the above embodiment, which can solve the technical problem of low reliability of user identity authentication. Compared with the prior art, the beneficial effects of the identity authentication device provided by the present application are the same as the beneficial effects of the identity authentication method provided by the above embodiment, and the other technical features in the identity authentication device are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0112] The present application provides an identity authentication device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the identity authentication method in the first embodiment described above.

[0113] Reference below Figure 5 , which shows a schematic diagram of the structure of an identity authentication device suitable for implementing the embodiment of the present application. The identity authentication device in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 5 The identity authentication device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0114] like Figure 5As shown, the identity authentication device may include a processing device 1001 (such as a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM: Random Access Memory) 1004. In RAM1004, various programs and data required for the operation of the identity authentication device are also stored. The processing device 1001, ROM1002 and RAM1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the identity authentication device to communicate with other devices wirelessly or wired to exchange data. Although the figure shows an identity authentication device with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented or provided instead.

[0115] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0116] The identity authentication device provided by the present application adopts the identity authentication method in the above embodiment, which can solve the technical problem of low reliability of user identity authentication. Compared with the prior art, the beneficial effects of the identity authentication device provided by the present application are the same as the beneficial effects of the identity authentication method provided by the above embodiment, and the other technical features in the identity authentication device are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0117] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0118] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0119] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, wherein the computer-readable program instructions are used to execute the identity authentication method in the above-mentioned embodiment.

[0120] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM: Random Access Memory), a read-only memory (ROM: Read Only Memory), an erasable programmable read-only memory (EPROM: Erasable Programmable Read Only Memory or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM: CD-Read Only Memory), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency: Radio Frequency), etc., or any suitable combination of the above.

[0121] The computer-readable storage medium may be included in the identity authentication device; or may exist independently without being assembled into the identity authentication device.

[0122] The computer-readable storage medium carries one or more programs. When the one or more programs are executed by the identity authentication device, the identity authentication device: obtains in real time the real-time behavior feature values ​​of the user using the distributed device and the real-time spatial feature values ​​of the distributed device, wherein a real-time behavior feature vector is constructed according to the real-time behavior feature values, and a real-time spatial feature vector is constructed according to the real-time spatial feature values; calculates the similarity between the real-time behavior feature vector and the initial behavior feature vector, and the feature distance between the real-time spatial feature vector and the initial spatial feature vector according to feature matching, wherein the initial behavior feature vector and the initial spatial feature vector are obtained when the distributed device first accesses the distributed network; if the similarity is greater than or equal to a preset similarity threshold and the feature distance is less than or equal to a preset distance threshold, the user's identity authentication is passed.

[0123] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0124] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0125] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0126] The readable storage medium provided by the present application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned identity authentication method, and can solve the technical problem of low reliability of user identity authentication. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided by the present application are the same as the beneficial effects of the identity authentication method provided by the above-mentioned embodiment, and will not be repeated here.

[0127] The present application also provides a computer program product, including a computer program, which implements the steps of the above-mentioned identity authentication method when executed by a processor.

[0128] The computer program product provided by this application can solve the technical problem of low reliability of user identity authentication. Compared with the prior art, the beneficial effects of the computer program product provided by this application are the same as the beneficial effects of the identity authentication method provided by the above embodiment, which will not be repeated here.

[0129] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. An identity authentication method, characterized in that: The identity authentication method comprises: Acquire real-time behavior feature values ​​of users using distributed devices and real-time spatial feature values ​​of the distributed devices, wherein a real-time behavior feature vector is constructed according to the real-time behavior feature values, and a real-time spatial feature vector is constructed according to the real-time spatial feature values; Calculating the similarity between the real-time behavior feature vector and the initial behavior feature vector, and the feature distance between the real-time space feature vector and the initial space feature vector according to feature matching, wherein the initial behavior feature vector and the initial space feature vector are obtained when the distributed device first accesses the distributed network; If the similarity is greater than or equal to a preset similarity threshold and the feature distance is less than or equal to a preset distance threshold, the user's identity authentication is successful.

2. The identity authentication method according to claim 1, characterized in that: The step of acquiring the real-time behavior feature value of the user using the distributed device and the real-time spatial feature value of the distributed device in real time comprises: Collecting real-time behavior feature data of users operating distributed devices in real time, and converting the real-time behavior feature data into real-time behavior feature values; Real-time spatial feature data of distributed devices are collected in real time, and the real-time spatial feature data are converted into real-time spatial feature values.

3. The identity authentication method according to claim 1, characterized in that: The step of respectively calculating the similarity between the real-time behavior feature vector and the initial behavior feature vector and the feature distance between the real-time space feature vector and the initial space feature vector according to feature matching comprises: The similarity is obtained by calculating the cosine similarity between the real-time behavior feature vector and the initial behavior feature vector; The feature distance is obtained by calculating the Euclidean distance between the real-time space feature vector and the initial space feature vector.

4. The identity authentication method according to claim 1, characterized in that: The identity authentication method further comprises: Acquire historical data, wherein the historical data includes a behavior feature vector of a user in a historical period, a spatial feature vector of a distributed device in a historical period, and a user authentication result obtained according to the behavior feature vector of the user in the historical period and the spatial feature vector of the distributed device in the historical period; Analyzing the historical data through a preset neural network model to obtain behavioral features and spatial features that have a probability of influencing the user authentication result exceeding a preset probability threshold; Identity authentication is performed based on the weighted behavioral features and the spatial features.

5. The identity authentication method according to claim 1, characterized in that: The identity authentication method further comprises: When the distributed device first accesses the distributed network, obtaining a device identifier of the distributed device and an identity identifier of a user using the distributed device according to an access request of the distributed device to access the distributed network; Bind the device identifier and the identity identifier, the user and the distributed device to obtain an association relationship between the user and the distributed device; The number of distributed devices used by the user is queried according to the association relationship, and if the number exceeds a preset number threshold, the identity of the user is verified.

6. The identity authentication method according to claim 1, characterized in that: The identity authentication method comprises: If the user's identity authentication fails, the user whose identity authentication fails is determined to be an abnormal user; Terminate the abnormal user's access to the distributed device.

7. An identity authentication device, characterized in that: The identity authentication device comprises: An acquisition module, used for acquiring in real time a real-time behavior feature vector of a user using a distributed device and a real-time spatial feature vector of the distributed device; A matching module, used to calculate the similarity between the real-time behavior feature vector and the initial behavior feature vector, and the feature distance between the real-time space feature vector and the initial space feature vector according to feature matching, wherein the initial behavior feature vector and the initial space feature vector are obtained when the distributed device first accesses the distributed network; The authentication module is used to pass the user's identity authentication if the similarity is greater than or equal to a preset similarity threshold and the feature distance is less than or equal to a preset distance threshold.

8. An identity authentication device, characterized in that: The device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the identity authentication method according to any one of claims 1 to 6.

9. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the identity authentication method according to any one of claims 1 to 6 are implemented.

10. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the steps of the identity authentication method according to any one of claims 1 to 6 are implemented.

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