Data verification method, device, computer storage medium, and electronic device

Abstract

The present application discloses a data verification method, device, computer storage medium, and electronic device. Relating to the field of financial technology or other related fields, the method comprises: receiving encrypted behavior data fed back by an application, decrypting the encrypted behavior data to obtain target behavior data, wherein the encrypted behavior data refers to the encrypted behavior data of the user using the application; inputting the target behavior data into a target verification model, and outputting a behavior verification result, wherein the target verification model is used to verify the user's behavior data; determining the risk level according to the behavior verification result, and determining the verification method using the risk level, sending verification information to the user according to the verification method, wherein the verification information is used to characterize the verification method information when the user uses the application. Through this application, the problems of low verification efficiency and low verification accuracy in user identity authentication in related technologies are solved.

Description

Technical Field

[0001] The present application relates to the field of financial technology or other related fields, and specifically, to a data verification method, device, computer storage medium, and electronic device. Background Art

[0002] With the rapid development of technology, the trend toward digitalization and onlineization of financial services is becoming increasingly evident. However, when users conduct relevant financial transactions, identity authentication is often required. However, related technologies often use relatively simple methods for verifying identity, such as using a single password or fingerprint recognition. This approach poses security risks because if a password is cracked or fingerprint information is stolen, the account could be illegally accessed. Furthermore, the security systems of these technologies may be subject to the risk of hacker attacks or data leaks. They also lack real-time and dynamic features. They typically only perform identity authentication at user login, but lack effective monitoring and response mechanisms during user use. This results in an inability to promptly detect and prevent illegal access to user accounts. Inadequate system security could lead to the theft or misuse of users' biometric information, preventing users from providing a more secure, convenient, and intelligent financial services experience.

[0003] In addition, although the above-mentioned biometric recognition technologies such as fingerprint recognition and facial recognition are relatively mature, there is still the possibility of recognition errors, resulting in low accuracy. For example, facial recognition may be affected by factors such as lighting, angle, and makeup, resulting in recognition failure or misidentification; different financial institutions may adopt different identity authentication methods or systems for their financial services, which may cause users to need to perform multiple identity authentications when using different services, reducing the user experience; and with the continuous development of technology, new identity authentication technologies continue to emerge, and financial institutions need to update and upgrade their identity authentication systems in a timely manner to adapt to new security needs and user needs. However, system updates and upgrades may bring certain technical challenges and cost investments.

[0004] Regarding the problems of low verification efficiency and low verification accuracy when performing user identity authentication in related technologies, no effective solution has been proposed so far. Summary of the Invention

[0005] The main purpose of this application is to provide a data verification method, device, computer storage medium and electronic device to solve the problems of low verification efficiency and low verification accuracy when performing user identity authentication in related technologies.

[0006] To achieve the above objectives, according to one aspect of the present application, a data verification method is provided. The method comprises: receiving encrypted behavior data fed back by an application, decrypting the encrypted behavior data to obtain target behavior data, wherein the encrypted behavior data refers to encrypted behavior data of a user using the application; inputting the target behavior data into a target verification model and outputting a behavior verification result, wherein the target verification model is used to verify the user's behavior data; determining a risk level based on the behavior verification result, using the risk level to determine a verification method, and sending verification information to the user based on the verification method, wherein the verification information is used to represent the verification method information when the user uses the application.

[0007] Furthermore, the target verification model includes a first feature extraction model, a second feature extraction model and a feature verification model. The target behavior data is input into the target verification model, and the output behavior verification result includes: standardizing the target behavior data to obtain processed behavior data; inputting the processed behavior data into the first feature extraction model, and extracting the target behavior features of the processed behavior data by the first feature extraction model to obtain initial behavior features; inputting the processed behavior data into the second feature extraction model, and processing the processed behavior data by the second feature extraction model to obtain behavior pattern features, wherein the behavior pattern features are used to characterize the behavior state of the user operating the application; identifying the behavior pattern features, and when the behavior pattern features characterize that the user meets the pattern requirements, inputting the initial behavior features and the processed behavior data into the feature verification model to obtain the behavior verification result; when the behavior pattern features characterize that the user does not meet the pattern requirements, stopping the step of outputting the behavior verification result.

[0008] Furthermore, the processed behavior data is processed by the second feature extraction model to obtain behavior pattern features, including: extracting the behavior pattern of the processed behavior data by the second feature extraction model to obtain a target behavior pattern, wherein the target behavior pattern is used to characterize the behavior state of the user operating the application; obtaining the historical behavior pattern, matching the target behavior pattern and the historical behavior pattern through the second feature extraction model to obtain a matching result, and determining the behavior pattern features based on the matching result, wherein the historical behavior pattern is used to characterize the behavior state of the user operating the application in a historical time period.

[0009] Furthermore, determining the risk level based on the behavior verification result includes: obtaining an identity authentication address from an identity authentication request, wherein the identity authentication request is a request sent by the user to verify identity information; obtaining the user's device information, and determining an initial risk level based on the device information, the behavior verification result and the identity authentication address, wherein the device information includes device address information and device status; and determining the risk level based on the initial risk level.

[0010] Furthermore, determining the initial risk level based on the device information, behavior verification results and identity authentication address includes: obtaining the user's historical device information in a historical time period, extracting the device address information from the device information, and extracting the historical device address information from the historical device information; when the behavior verification result indicates that the user's operating behavior complies with the operating rules, obtaining a heat map, and determining whether the heat map includes the identity authentication address, wherein the heat map is constructed from the historical device address information; when the heat map includes the identity authentication address, determining the initial risk level as the first risk level, and when the heat map does not include the identity authentication address, calculating the distance between the device address information and the center point of the heat map to obtain the address distance; calculating the product of the address distance and the preset weight coefficient to obtain the address risk value, and determining the initial risk level based on the address risk value and the device status in the device information; when the behavior verification result indicates that the user's operating behavior does not comply with the operating rules, determining the initial risk level as the third risk level.

[0011] Furthermore, determining the initial risk level based on the address risk value and the device status in the device information includes: judging whether the address risk value is greater than a preset threshold value, and if the address risk value is greater than the preset threshold value, judging whether the device status is a safe state; if the device status is a safe state, determining the initial risk level as a second risk level; if the device status is not a safe state, determining the initial risk level as a third risk level; if the address risk value is less than or equal to the preset threshold value, determining the initial risk level as a first risk level, wherein the first risk level is lower than the second risk level, and the second risk level is lower than the third risk level.

[0012] Furthermore, after sending verification information to the user according to the verification method, the method also includes: receiving the verification result fed back by the user, and allowing the user to perform transaction operations when the verification result indicates that the verification is passed; when the verification result indicates that the verification fails, locking the user's operation process and issuing a prompt message, wherein the prompt message is used to remind the user to perform verification again.

[0013] To achieve the above-mentioned purpose, according to another aspect of the present application, a data verification device is provided. The device includes: a first receiving unit, configured to receive encrypted behavior data fed back by an application, decrypt the encrypted behavior data to obtain target behavior data, wherein the encrypted behavior data refers to encrypted behavior data of a user using the application; an input unit, configured to input the target behavior data into a target verification model and output a behavior verification result, wherein the target verification model is used to verify the user's behavior data; a determination unit, configured to determine a risk level based on the behavior verification result, determine a verification method based on the risk level, and send verification information to the user based on the verification method, wherein the verification information is used to represent verification method information when the user uses the application.

[0014] According to another aspect of an embodiment of the present invention, a computer storage medium is provided. The computer storage medium is used to store a program. When the program is running, the device where the computer storage medium is located is controlled to execute a data verification method.

[0015] According to another aspect of an embodiment of the present invention, an electronic device is provided, comprising one or more processors and a memory; the memory stores computer-readable instructions, and the processor is used to execute the computer-readable instructions, wherein a data verification method is executed when the computer-readable instructions are executed.

[0016] According to another aspect of an embodiment of the present invention, a computer program product is provided, including a computer program. When the computer program is executed by a processor, a data verification method is performed.

[0017] Through this application, the following steps are adopted: receiving encrypted behavior data fed back by the application, decrypting the encrypted behavior data to obtain target behavior data, wherein the encrypted behavior data refers to the encrypted behavior data of the user using the application; inputting the target behavior data into a target verification model, and outputting the behavior verification result, wherein the target verification model is used to verify the user's behavior data; determining the risk level according to the behavior verification result, and using the risk level to determine the verification method, sending verification information to the user according to the verification method, wherein the verification information is used to characterize the verification method information when the user uses the application, thereby solving the problems of low verification efficiency and low verification accuracy when authenticating the user in the related technology, by obtaining the target behavior data, inputting the target behavior data into the target verification model, outputting the behavior verification result, and determining the risk level and verification method according to the behavior verification result, thereby achieving the effect of improving the efficiency and accuracy of user identity authentication. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The accompanying drawings, which constitute part of this application, are intended to provide a further understanding of this application. The exemplary embodiments and descriptions of this application are intended to explain this application and do not constitute an improper limitation on this application. In the accompanying drawings:

[0019] Figure 1 is a flow chart of a data verification method provided in accordance with an embodiment of the present application;

[0020] Figure 2 is a schematic diagram of a method for determining a behavior verification result according to an embodiment of the present application;

[0021] Figure 3 is a schematic diagram of a data verification device provided according to an embodiment of the present application;

[0022] Figure 4 is a schematic diagram of an electronic device provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0023] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments in this application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0024] 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.

[0025] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes 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.

[0026] It should be noted that the relevant information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for display and analysis, etc.) involved in this disclosure are all information and data authorized by the user or fully authorized by all parties. For example, an interface is set up between this system and the relevant user or organization. Before obtaining relevant information, it is necessary to send an acquisition request to the aforementioned user or organization through the interface, and obtain the relevant information after receiving the consent information fed back by the aforementioned user or organization.

[0027] It should be noted that the collected information used in this application is information and data authorized by the user 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 customs, and provide corresponding operation entrances for users to choose to authorize or refuse use.

[0028] The present invention will be described below in conjunction with preferred implementation steps. Figure 1 is a flow chart of a data verification method according to an embodiment of the present application. Figure 1 As shown, the method includes the following steps:

[0029] Step S101: receiving encrypted behavior data fed back by an application, decrypting the encrypted behavior data to obtain target behavior data, wherein the encrypted behavior data refers to the encrypted behavior data of the user using the application.

[0030] Specifically, when a user uses a financial institution's application to perform operations or attempts to access sensitive functions, in order to protect the user's property safety, the user's identity needs to be verified, and the user verification can be performed based on the user's behavioral data and location information.

[0031] When collecting behavioral data, the application can collect user behavioral data through the phone's built-in touch sensor and motion sensor (accelerometer and gyroscope). To ensure that even if the data is intercepted during transmission, it cannot be read by unauthorized third parties, the data can be encrypted using an encryption algorithm and securely transmitted to the server via the HTTPS (Hyper Text Transfer Protocol Secure) protocol to prevent the behavioral data from being intercepted during transmission. After receiving the encrypted behavioral data, the server uses a pre-set key to decrypt the encrypted behavioral data and obtain the target behavioral data, which can be used for subsequent user behavior analysis and verification.

[0032] It should be noted that the target behavior data may include behavioral data recorded by the sensor from five aspects when the user enters a password or slides: touch pressure data, that is, the force when the user presses the screen; sliding speed data, that is, the speed when the user slides his fingertips to enter the password; sliding trajectory data, that is, the path of the user's finger moving on the screen; click interval data, that is, the time interval between consecutive clicks when the user enters the password, and dwell time data, that is, the length of time the user's finger stays on a specific key.

[0033] Step S102: input the target behavior data into a target verification model and output a behavior verification result, wherein the target verification model is used to verify the user's behavior data.

[0034] Specifically, after obtaining the target behavior data, the target behavior data is feature extracted and verified using a target verification model, thereby obtaining a behavior verification result for characterizing the user identity type, that is, a behavior verification result for determining whether the user is a safe user. The target verification model can be a model based on models such as support vector machines.

[0035] It should be noted that before using the target verification model to process behavioral data, the model needs to be trained. During the training process, the model parameters of the above model can be optimized using cross-validation to ensure that the model has good generalization ability.

[0036] Step S103, determining the risk level based on the behavior verification result, and using the risk level to determine the verification method, and sending verification information to the user based on the verification method, wherein the verification information is used to represent the verification method information when the user uses the application.

[0037] Specifically, after obtaining the behavior verification results output by the model, the risk level of the user behavior can be evaluated by analyzing the behavior verification results, that is, unusual behavior or location changes can be identified based on the behavior verification results. If abnormal behavior is detected, the user's account can be locked and the user can be notified via SMS or email, requiring identity authentication through other means. For example, if a user attempts to log in at an abnormal time or place, the system may increase their risk level, where the risk level can include the lowest level: the first target risk level, the medium level: the second target risk level, and the highest level: the third target risk level.

[0038] Once the system determines the user's risk level, it can select the most appropriate verification method based on this level and send the corresponding verification information to the user. The verification information can not only be used to confirm the user's identity, but also convey the user's current security status. For example, if the user receives a text message verification code, the user will need to further verify their identity to ensure the security of the transaction. For another example, if the risk level is the first target risk level (such as the user is at home), the verification method at this time may only require the entry of a password; if the risk level is the second target risk level (such as the user is in a public place), the verification method at this time may require the entry of a password and fingerprint verification or facial recognition.

[0039] The data verification method provided in the embodiment of the present application receives encrypted behavior data fed back by an application, decrypts the encrypted behavior data, and obtains target behavior data, wherein the encrypted behavior data refers to the encrypted behavior data of the user using the application; inputs the target behavior data into a target verification model, and outputs a behavior verification result, wherein the target verification model is used to verify the user's behavior data; determines the risk level according to the behavior verification result, and uses the risk level to determine the verification method, and sends verification information to the user according to the verification method, wherein the verification information is used to characterize the verification method information when the user uses the application, solves the problem of low verification efficiency and low verification accuracy when authenticating the user in the related art, obtains the target behavior data, inputs the target behavior data into the target verification model, outputs the behavior verification result, and determines the risk level and verification method according to the behavior verification result, thereby achieving the effect of improving the efficiency and accuracy of user identity authentication.

[0040] Figure 2 is a schematic diagram of a method for determining a behavior verification result according to an embodiment of the present application, such as Figure 2 As shown, optionally, in the data verification method provided in the embodiment of the present application, the target verification model includes a first feature extraction model, a second feature extraction model and a feature verification model, the target behavior data is input into the target verification model, and the output behavior verification result includes:

[0041] Step S201 : normalize the target behavior data to obtain processed behavior data.

[0042] Specifically, when using the target verification model to verify the target behavior data, first of all, in order to eliminate the noise and inconsistency in the data and make the data format unified for subsequent processing, it is necessary to standardize the original target behavior data to obtain the processed behavior data.

[0043] Step S202: input the processed behavior data into a first feature extraction model, and extract the target behavior features of the processed behavior data by the first feature extraction model to obtain initial behavior features.

[0044] Step S203: input the processed behavior data into the second feature extraction model, and the second feature extraction model processes the processed behavior data to obtain behavior pattern features, wherein the behavior pattern features are used to characterize the behavior state of the user operating the application.

[0045] Furthermore, in order to improve the performance of the model, before the processed behavioral data is input into the feature verification model composed of the support vector machine algorithm, the processed behavioral data can first be collaboratively processed using a deep learning model, that is, the processed behavioral data will be input into two different feature extraction models, wherein the first feature extraction model can be a convolutional neural network model for extracting high-level features from user behavior data, and the key features in the data are automatically extracted based on the multiple convolutional layers in the model, such as the change pattern of touch pressure, the fluctuation of sliding speed, etc. These features constitute the initial behavioral features of the behavioral analysis; and the second feature extraction model can be a long short-term memory network model for analyzing the user's behavioral sequence data, identifying and extracting behavioral pattern features from the processed behavioral data that can reflect the user's specific behavioral patterns or behavioral habits when using the application.

[0046] Step S204, identifying the behavioral pattern characteristics. When the behavioral pattern characteristics indicate that the user meets the pattern requirements, the initial behavioral characteristics and the processed behavioral data are input into the feature verification model to obtain the behavior verification results. When the behavioral pattern characteristics indicate that the user does not meet the pattern requirements, the step of outputting the behavior verification results is stopped.

[0047] Furthermore, after extracting the behavioral pattern features, they need to be identified and analyzed. If the behavioral pattern features indicate that the user's operation meets the preset pattern requirements, the initial behavioral features and processed behavioral data can be input into the feature verification model for further analysis, and the final behavioral verification results can be output; on the contrary, if the behavioral pattern features do not match the preset pattern, it indicates that the user's behavior is abnormal or has potential risks, and the risk level is directly determined to be the highest third risk level, and verification information is sent to the user based on the third risk level.

[0048] This embodiment processes the behavior verification results by utilizing a target verification model composed of multiple algorithms, which can effectively identify and analyze the behavior patterns of users when using applications, and timely discover and prevent potential security threats. At the same time, the model has high flexibility and scalability and can adapt to different application scenarios and user needs.

[0049] In order to analyze the user's behavior pattern, optionally, in the data verification method provided in the embodiment of the present application, the processed behavior data is processed by the second feature extraction model to obtain behavior pattern features, including: extracting the behavior pattern of the processed behavior data by the second feature extraction model to obtain a target behavior pattern, wherein the target behavior pattern is used to characterize the behavior state of the user operating the application; obtaining a historical behavior pattern, matching the target behavior pattern and the historical behavior pattern through the second feature extraction model to obtain a matching result, and determining the behavior pattern features based on the matching result, wherein the historical behavior pattern is used to characterize the behavior state of the user operating the application in a historical time period.

[0050] Specifically, the second feature extraction model can be based on a long short-term memory network algorithm. The long short-term memory network algorithm can process sequential data and analyze each user operation to establish a dynamic representation of their behavior patterns. When the second feature extraction model is used to process the processed behavior data, it can first extract the target behavior pattern that represents the user's behavior state in operating the application.

[0051] To further analyze the user's behavior pattern, the user's historical behavior pattern representing the operation of the application within a historical time period can be obtained, and the target behavior pattern can be matched with the historical behavior pattern. Based on the matching results, it is determined whether the user's current target behavior pattern is consistent with its historical behavior pattern, or whether there is a significant deviation, and then the behavior pattern characteristics are obtained. If the target behavior pattern matches the historical behavior pattern, it indicates that the user's behavior needs to be securely verified and subsequent operations are allowed. This embodiment extracts and analyzes the target behavior pattern and the historical behavior pattern through the second feature extraction model, which can more comprehensively understand the user's operation behavior and lay the foundation for subsequent identity verification.

[0052] Optionally, in the data verification method provided in the embodiment of the present application, determining the risk level based on the behavior verification result includes: obtaining an identity authentication address from an identity authentication request, wherein the identity authentication request is a request sent by the user to verify identity information; obtaining the user's device information, and determining the initial risk level based on the device information, the behavior verification result and the identity authentication address, wherein the device information includes device address information and device status; and determining the risk level based on the initial risk level.

[0053] Specifically, when determining the risk level, the user's location information is also used to generate an authentication address for verification. This authentication address can refer to the address of the location of the user's mobile phone number. For example, when the user is in a secure environment such as home or office, a simple password verification is sufficient. However, when the user is outside of their location, a more stringent verification method, such as a password and fingerprint verification, may be required. At this point, the authentication address can be obtained from the authentication request. The device information, behavior verification results, and authentication address are then evaluated using an adaptive risk assessment engine or risk model. This information is then comprehensively processed to adjust the complexity of the authentication process to match the current risk level, i.e., to determine an initial risk level. Finally, the initial risk level is used to determine a final risk level. Device information can include various contextual information, such as location information (i.e., device address information), time information (e.g., the time when the user transaction occurred), and device status (e.g., whether malware is present). For example, the initial risk level includes three levels: a first risk level, a second risk level, and a third risk level. The final risk level is also generated based on the initial risk level: a first target risk level, a second target risk level, and a third target risk level. For example, if a user uses an application at night or in an infrequently visited location, the initial risk level will increase. The risk model can be a decision tree model, a Bayesian network model, or other statistical model. By obtaining the authentication address, this embodiment can more effectively identify and prevent potential security threats, protecting user data and system resources.

[0054] In order to improve the accuracy of identity authentication, optionally, in the data verification method provided in the embodiment of the present application, determining the initial risk level based on the device information, behavior verification results and identity authentication address includes: obtaining the user's historical device information in a historical time period, extracting the device address information from the device information, and extracting the historical device address information from the historical device information; when the behavior verification result indicates that the user's operating behavior complies with the operating rules, obtaining a heat map, and determining whether the heat map includes the identity authentication address, wherein the heat map is constructed from the historical device address information; when the heat map includes the identity authentication address, determining the initial risk level as the first risk level, and when the heat map does not include the identity authentication address, calculating the distance between the device address information and the center point of the heat map to obtain the address distance; calculating the product of the address distance and the preset weight coefficient to obtain the address risk value, and determining the initial risk level based on the address risk value and the device status in the device information; when the behavior verification result indicates that the user's operating behavior does not comply with the operating rules, determining the initial risk level as the third risk level.

[0055] Specifically, when determining the initial risk level based on device information, behavior verification results and identity authentication address, first extract the device address information representing the IP address and other information of the current device from the device information of the mobile device used by the user, obtain historical device information through the device log, and extract historical device address information from the historical device information.

[0056] Furthermore, it is necessary to determine whether the behavior verification results represent the user's operational behavior in compliance with the operational rules. On the one hand, when the user's operational behavior complies with the operational rules, the collected historical device address information can be used to construct a heat map through data visualization. For example, the historical device address information corresponding to each transaction record can be used to count the number of transactions corresponding to each address, with the city as the smallest statistical unit, and generate a heat map on a virtual map. The heat map can use color changes to represent the activity level or risk level of different addresses. When the heat map includes the identity authentication address, it indicates that the user is in the mobile phone card's home area, which means that the user is relatively safe. In this case, the initial risk level can be directly determined as the first risk level. If the heat map does not include the identity authentication address, it indicates that the user is not in a regular location. In this case, it is necessary to calculate the distance between the device address information and the center point of the heat map (usually a high-activity or high-risk area). Then, based on a preset weight coefficient, the address distance is calculated to obtain the address risk value. The initial risk level is determined based on the address risk value and the device status in the device information.

[0057] On the other hand, if the behavior verification result indicates that the user's operation does not comply with the operation rules, the initial risk level will be directly determined as the third risk level, where the third risk level has the highest risk coefficient and the first risk level has the lowest risk coefficient. This embodiment uses device information, historical device information, and the identity authentication address to determine the initial risk level, which can improve the accuracy of identity authentication while also enhancing the security and user experience of the user account.

[0058] The initial risk level may include multiple types. Optionally, in the data verification method provided in the embodiment of the present application, determining the initial risk level based on the address risk value and the device status in the device information includes: judging whether the address risk value is greater than a preset threshold, and if the address risk value is greater than the preset threshold, judging whether the device status is a safe state; if the device status is a safe state, determining the initial risk level as the second risk level; if the device status is not a safe state, determining the initial risk level as the third risk level; if the address risk value is less than or equal to the preset threshold, determining the initial risk level as the first risk level, wherein the first risk level is lower than the second risk level, and the second risk level is lower than the third risk level.

[0059] Specifically, when determining the initial risk level, it is first necessary to set a preset threshold and compare the calculated address risk value with the preset threshold. If the address risk value is greater than the preset threshold, it indicates that the user's address is at a higher level of risk, and it is necessary to determine whether the device status of the hardware device used by the user is safe. If the device status is displayed as safe, the initial risk level can be determined as the second risk level. For example, the verification method corresponding to the second risk level is facial recognition verification and fingerprint verification. Conversely, if the device status is not safe, the initial risk level needs to be determined as the third risk level. For example, the verification method corresponding to the third risk level is facial recognition verification, fingerprint verification, and voiceprint verification.

[0060] If the address risk value is less than or equal to the preset threshold, this indicates a low risk level and the user is considered relatively safe. The initial risk level is determined as Level 1, which can be verified, for example, through fingerprint or password authentication. This embodiment utilizes the address risk value and device status to determine the initial risk level, facilitating rapid identification and response to potential security threats and providing support for subsequent verification.

[0061] Optionally, in the data verification method provided in the embodiment of the present application, after sending verification information to the user according to the verification method, the method also includes: receiving the verification result fed back by the user, and allowing the user to perform transaction operations when the verification result indicates that the verification is passed; when the verification result indicates that the verification fails, locking the user's operation process and issuing a prompt message, wherein the prompt message is used to remind the user to verify again.

[0062] Specifically, after the user performs the corresponding verification operation based on the verification information, the server can receive the verification result fed back by the user and judge the verification result. If the verification result shows that the user's identity authentication is passed, the user's operation authority can be opened to allow the user to perform transaction operations. At this time, the user can freely perform operations such as transferring money, purchasing goods or services, and ensure the smooth progress and security of these operations.

[0063] Conversely, if the verification result indicates that the user's identity verification has failed, measures are required to protect the user's account security and transaction environment. First, the user's operation process can be locked to prevent any unauthorized transactions. A prompt message is then sent to the user to inform them of the reason for the verification failure and to prompt them to re-authenticate. This embodiment, by promptly identifying the verification result, ensures that users can quickly and conveniently complete identity verification and receive timely assistance if they encounter any problems. It also ensures transaction security, improves the user experience, and enhances user trust and satisfaction.

[0064] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0065] The present application also provides a data verification device. It should be noted that the data verification device of the present application can be used to execute the data verification method provided in the present application. The data verification device provided in the present application is introduced below.

[0066] Figure 3 Schematic diagram of a data verification device according to an embodiment of the present application. Figure 3 As shown, the device includes: a first receiving unit 30, an input unit 31, and a determining unit 32.

[0067] The first receiving unit 30 is configured to receive the encrypted behavior data fed back by the application, decrypt the encrypted behavior data, and obtain target behavior data, wherein the encrypted behavior data refers to the encrypted behavior data of the user using the application;

[0068] An input unit 31 is used to input target behavior data into a target verification model and output a behavior verification result, wherein the target verification model is used to verify the user's behavior data;

[0069] The determination unit 32 is used to determine the risk level according to the behavior verification result, and use the risk level to determine the verification method, and send verification information to the user according to the verification method, wherein the verification information is used to represent the verification method information when the user uses the application.

[0070] The data verification device provided in the embodiment of the present application receives the encrypted behavior data fed back by the application through the first receiving unit 30, decrypts the encrypted behavior data, and obtains target behavior data, wherein the encrypted behavior data refers to the encrypted behavior data of the user using the application; the input unit 31 inputs the target behavior data into the target verification model, and outputs the behavior verification result, wherein the target verification model is used to verify the user's behavior data; the determination unit 32 determines the risk level according to the behavior verification result, and uses the risk level to determine the verification method, and sends verification information to the user according to the verification method, wherein the verification information is used to characterize the verification method information when the user uses the application, which solves the problems of low verification efficiency and low verification accuracy when authenticating the user in the related technology, by obtaining the target behavior data, inputting the target behavior data into the target verification model, outputting the behavior verification result, and determining the risk level and verification method according to the behavior verification result, thereby achieving the effect of improving the efficiency and accuracy of user identity authentication.

[0071] Optionally, in the data verification device provided in the embodiment of the present application, the input unit 31 includes: a processing module, used to standardize the target behavior data to obtain processed behavior data; a first input module, used to input the processed behavior data into a first feature extraction model, and the first feature extraction model extracts the target behavior features of the processed behavior data to obtain initial behavior features; a second input module, used to input the processed behavior data into a second feature extraction model, and the second feature extraction model processes the processed behavior data to obtain behavior pattern features, wherein the behavior pattern features are used to characterize the behavior state of the user operating the application; an identification module, used to identify the behavior pattern features, and when the behavior pattern features characterize that the user meets the pattern requirements, the initial behavior features and the processed behavior data are input into the feature verification model to obtain a behavior verification result, and when the behavior pattern features characterize that the user does not meet the pattern requirements, the step of outputting the behavior verification result is stopped.

[0072] Optionally, in the data verification device provided in the embodiment of the present application, the input unit 31 includes: an extraction module, which is used to extract the behavior pattern of the processed behavior data by the second feature extraction model to obtain a target behavior pattern, wherein the target behavior pattern is used to characterize the behavior state of the user operating the application; a first acquisition module, which is used to obtain the historical behavior pattern, match the target behavior pattern and the historical behavior pattern through the second feature extraction model to obtain a matching result, and determine the behavior pattern characteristics according to the matching result, wherein the historical behavior pattern is used to characterize the behavior state of the user operating the application in a historical time period.

[0073] Optionally, in the data verification device provided in the embodiment of the present application, the determination unit 32 includes: a second acquisition module, used to obtain an identity authentication address from an identity authentication request, wherein the identity authentication request is a request sent by the user to verify identity information; a third acquisition module, used to obtain the user's device information, and determine the initial risk level based on the device information, behavior verification results and identity authentication address, wherein the device information includes device address information and device status; a first determination module, used to determine the risk level based on the initial risk level.

[0074] Optionally, in the data verification device provided in the embodiment of the present application, the determination unit 32 includes: a fourth acquisition module, used to obtain historical device information of the user in a historical time period, extract device address information from the device information, and extract historical device address information from the historical device information; a fifth acquisition module, used to obtain a heat map when the behavior verification result indicates that the user's operating behavior complies with the operating rules, and determine whether the heat map includes the identity authentication address, wherein the heat map is constructed from the historical device address information; a second determination module, used to determine the initial risk level as the first risk level when the heat map includes the identity authentication address, and calculate the distance between the device address information and the center point of the heat map when the heat map does not include the identity authentication address to obtain the address distance; a calculation module, used to calculate the product of the address distance and a preset weight coefficient to obtain an address risk value, and determine the initial risk level according to the address risk value and the device status in the device information; a third determination module, used to determine the initial risk level as the third risk level when the behavior verification result indicates that the user's operating behavior does not comply with the operating rules.

[0075] Optionally, in the data verification device provided in the embodiment of the present application, the first receiving unit 30 includes: a judgment module, used to judge whether the address risk value is greater than a preset threshold, and when the address risk value is greater than the preset threshold, judge whether the device status is a safe state; a fourth determination module, used to determine the initial risk level as the second risk level when the device status is a safe state; a fifth determination module, used to determine the initial risk level as the third risk level when the device status is not a safe state; and a sixth determination module, used to determine the initial risk level as the first risk level when the address risk value is less than or equal to the preset threshold, wherein the first risk level is lower than the second risk level, and the second risk level is lower than the third risk level.

[0076] Optionally, in the data verification device provided in the embodiment of the present application, the device also includes: a second receiving unit, used to receive the verification result fed back by the user after sending the verification information to the user according to the verification method, and allowing the user to perform transaction operations when the verification result indicates that the verification is passed; a locking unit, used to lock the user's operation process and issue a prompt message when the verification result indicates that the verification fails, wherein the prompt message is used to remind the user to perform verification again.

[0077] The data verification device includes a processor and a memory. The first receiving unit 30, input unit 31, determination unit 32, etc. are all stored in the memory as program units, and the processor executes the program units stored in the memory to implement corresponding functions.

[0078] The processor includes a kernel, which retrieves the corresponding program unit from the memory. One or more kernels can be configured, and kernel parameters can be adjusted to address the low efficiency and accuracy of user authentication in related technologies.

[0079] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0080] An embodiment of the present invention provides a computer storage medium, which is used to store a program. When the program is running, the device where the computer storage medium is located is controlled to execute a data verification method.

[0081] Figure 4 is a schematic diagram of an electronic device provided according to an embodiment of the present application, such as Figure 4 As shown, an embodiment of the present invention provides an electronic device. The electronic device 40 includes a processor, a memory, and a program stored in the memory and executable on the processor. The processor is configured to execute computer-readable instructions. When the computer-readable instructions are executed, a data verification method is executed. The device herein may be a server, a PC, a PAD, a mobile phone, or the like.

[0082] The present application also provides a computer program product, including a computer program, which implements the steps of a data verification method in each embodiment of the present application when the computer program is executed by a processor.

[0083] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0084] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0085] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0086] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0087] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0088] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0089] Computer-readable media includes permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. The information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media (transitory media), such as modulated data signals and carrier waves.

[0090] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0091] The above are merely embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should all be included within the scope of the claims of the present application.

Claims

1. A data verification method, characterized in that: include: Receive encrypted behavior data fed back by the application, decrypt the encrypted behavior data to obtain target behavior data, wherein the encrypted behavior data refers to the encrypted behavior data of the user using the application; Inputting the target behavior data into a target verification model and outputting a behavior verification result, wherein the target verification model is used to verify the user's behavior data; Determining a risk level based on the behavior verification result, determining a verification method using the risk level, and sending verification information to the user based on the verification method, wherein the verification information is used to represent verification method information when the user uses the application; Wherein, the target verification model includes a first feature extraction model, a second feature extraction model and a feature verification model, and the target behavior data is input into the target verification model, and outputting the behavior verification result includes: standardizing the target behavior data to obtain processed behavior data; inputting the processed behavior data into the first feature extraction model, and extracting the target behavior features of the processed behavior data by the first feature extraction model to obtain initial behavior features; inputting the processed behavior data into the second feature extraction model, and processing the processed behavior data by the second feature extraction model to obtain behavior pattern features, wherein the behavior pattern features are used to characterize the behavior state of the user operating the application; identifying the behavior pattern features, and when the behavior pattern features characterize that the user meets the pattern requirements, inputting the initial behavior features and the processed behavior data into the feature verification model to obtain the behavior verification result, and when the behavior pattern features characterize that the user does not meet the pattern requirements, stopping the step of outputting the behavior verification result; Determining the initial risk level based on the device information, the behavior verification result and the identity authentication address includes: obtaining historical device information of the user in a historical time period, extracting device address information from the device information, and extracting historical device address information from the historical device information; when the behavior verification result indicates that the user's operation behavior complies with the operation rules, obtaining a heat map, and determining whether the heat map includes the identity authentication address, wherein the heat map is constructed from the historical device address information; when the heat map includes the identity authentication address, determining the initial risk level as the first risk level, and when the heat map does not include the identity authentication address, calculating the distance between the device address information and the center point of the heat map to obtain the address distance; calculating the product of the address distance and a preset weight coefficient to obtain an address risk value, and determining the initial risk level based on the address risk value and the device status in the device information; when the behavior verification result indicates that the user's operation behavior does not comply with the operation rules, determining the initial risk level as the third risk level; Determining the initial risk level based on the address risk value and the device status in the device information includes: judging whether the address risk value is greater than a preset threshold value, and if the address risk value is greater than the preset threshold value, judging whether the device status is a safe state; if the device status is the safe state, determining the initial risk level as the second risk level; if the device status is not the safe state, determining the initial risk level as the third risk level; if the address risk value is less than or equal to the preset threshold value, determining the initial risk level as the first risk level, wherein the first risk level is lower than the second risk level, and the second risk level is lower than the third risk level.

2. The method according to claim 1, characterized in that The second feature extraction model processes the processed behavior data to obtain behavior pattern features including: extracting a behavior pattern of the processed behavior data using the second feature extraction model to obtain a target behavior pattern, wherein the target behavior pattern is used to represent a behavior state of the user operating the application; Obtain a historical behavior pattern, match the target behavior pattern with the historical behavior pattern through the second feature extraction model to obtain a matching result, and determine the behavior pattern feature based on the matching result, wherein the historical behavior pattern is used to characterize the behavioral state of the user operating the application in a historical time period.

3. The method according to claim 1, characterized in that Determining the risk level based on the behavior verification results includes: Obtaining an identity authentication address from an identity authentication request, wherein the identity authentication request is a request sent by the user for verifying identity information; Obtaining device information of the user, and determining an initial risk level based on the device information, the behavior verification result, and the identity authentication address, wherein the device information includes device address information and device status; The risk level is determined based on the initial risk level.

4. The method according to claim 1, wherein After sending verification information to the user according to the verification method, the method further includes: receiving a verification result fed back by the user, and allowing the user to perform a transaction operation if the verification result indicates that the verification is passed; In the case where the verification result indicates that the verification has failed, the operation process of the user is locked and a prompt message is issued, wherein the prompt message is used to remind the user to perform verification again.

5. A data verification device, characterized in that: include: a first receiving unit, configured to receive encrypted behavior data fed back by the application, and decrypt the encrypted behavior data to obtain target behavior data, wherein the encrypted behavior data refers to encrypted behavior data of the user using the application; an input unit, configured to input the target behavior data into a target verification model and output a behavior verification result, wherein the target verification model is used to verify the user's behavior data; a determination unit, configured to determine a risk level based on the behavior verification result, determine a verification method based on the risk level, and send verification information to the user based on the verification method, wherein the verification information is used to represent verification method information when the user uses the application; Wherein, the input unit includes: a processing module for standardizing the target behavior data to obtain processed behavior data; a first input module for inputting the processed behavior data into a first feature extraction model, and extracting the target behavior features of the processed behavior data by the first feature extraction model to obtain initial behavior features; a second input module for inputting the processed behavior data into a second feature extraction model, and processing the processed behavior data by the second feature extraction model to obtain behavior pattern features, wherein the behavior pattern features are used to characterize the behavior state of the user operating the application; an identification module for identifying the behavior pattern features, and when the behavior pattern features characterize that the user meets the pattern requirements, inputting the initial behavior features and the processed behavior data into a feature verification model to obtain the behavior verification result, and when the behavior pattern features characterize that the user does not meet the pattern requirements, stopping the step of outputting the behavior verification result; The determination unit includes: a fourth acquisition module for acquiring historical device information of the user in a historical time period, extracting device address information from the device information, and extracting historical device address information from the historical device information; a fifth acquisition module for acquiring a heat map and determining whether the heat map includes an identity authentication address if the behavior verification result indicates that the user's operation behavior complies with the operation rules, wherein the heat map is constructed from the historical device address information; a second determination module for determining an initial risk level as a first risk level if the heat map includes the identity authentication address, and calculating a distance between the device address information and a center point of the heat map to obtain an address distance if the heat map does not include the identity authentication address; a calculation module for calculating the product of the address distance and a preset weight coefficient to obtain an address risk value, and determining the initial risk level based on the address risk value and the device status in the device information; a third determination module for determining the initial risk level as a third risk level if the behavior verification result indicates that the user's operation behavior does not comply with the operation rules; The first receiving unit includes: a judgment module, used to judge whether the address risk value is greater than a preset threshold, and if the address risk value is greater than the preset threshold, judge whether the device status is a safe state; a fourth determination module, used to determine the initial risk level as the second risk level if the device status is the safe state; a fifth determination module, used to determine the initial risk level as the third risk level if the device status is not the safe state; and a sixth determination module, used to determine the initial risk level as the first risk level if the address risk value is less than or equal to the preset threshold, wherein the first risk level is lower than the second risk level, and the second risk level is lower than the third risk level.

6. A computer storage medium, characterized in that The computer storage medium is used to store a program, wherein when the program is run, the device where the computer storage medium is located is controlled to execute the data verification method according to any one of claims 1 to 4.

7. An electronic device, characterized in that: The invention comprises one or more processors and a memory, wherein the memory is used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the data verification method described in any one of claims 1 to 4.

Images