Dynamic multi-factor verification method, system and equipment based on artificial intelligence and medium

Abstract

The invention discloses a dynamic multi-factor verification method, system and device based on artificial intelligence and a storage medium. The method comprises the steps of obtaining operation behavior data and environment data of a target user; inputting the operation behavior data and the environment data into a preset risk assessment model to obtain a risk assessment result of the target user; and inputting a risk assessment result into a preset multi-factor verification strategy generation model based on an artificial intelligence algorithm, and generating a dynamic multi-factor verification strategy. Compared with the prior art, the method has the following effects that the real-time operation behavior and environmental data of the target user are analyzed and evaluated by constructing the user operation behavior risk evaluation model, so that the risk evaluation result is obtained, and finally, the dynamic multi-factor verification strategy is generated according to the risk evaluation result, so that the user operation behavior risk evaluation method and device can be used for verifying the user operation behavior. Compared with a traditional static mode, the verification method is more intelligent and flexible, the user experience is improved, and meanwhile the safety of the information data is improved.

Description

Technical Field

[0001] The present invention relates to the field of cloud computing technology, and in particular to an artificial intelligence-based multi-factor authentication method, system, device and medium. Background Art

[0002] With the continuous development of the Internet and artificial intelligence, networking and informatization have gradually become popular in people's lives. Networking and informatization have brought convenience to people's lives in many fields such as mobile payment, online education, smart home, etc. However, at the same time, with the surge in data volume, the increase in system complexity and the emergence of intelligent network attacks, network and information security have also ushered in unprecedented challenges.

[0003] At present, in order to improve data security, most network platforms use multi-factor authentication technology for information security verification. However, traditional multi-factor authentication methods rely on static factors (such as passwords, fingerprints, SMS verification codes, etc.), which have certain security vulnerabilities when facing complex attack scenarios (such as phishing attacks and hosting attacks). In addition, static authentication methods lack flexibility and cannot dynamically adjust authentication strategies according to actual conditions, resulting in a decline in user experience. Summary of the invention

[0004] The purpose of the present invention is to provide a dynamic multi-factor authentication method, system, device and medium based on artificial intelligence to solve the problems of security vulnerabilities and lack of flexibility in static multi-factor authentication methods in related technologies.

[0005] In order to achieve the above object, the present invention provides the following technical solutions:

[0006] First aspect: The present invention provides a dynamic multi-factor verification method based on artificial intelligence, including: obtaining the operation behavior data and environmental data of the target user; inputting the operation behavior data and environmental data into a preset risk assessment model to obtain the risk assessment result of the target user; inputting the risk assessment result into a preset multi-factor verification strategy generation model based on an artificial intelligence algorithm to generate a dynamic multi-factor verification strategy.

[0007] Preferably, the method further includes: guiding the target user to perform verification according to the dynamic multi-factor verification strategy; if the target user performs dynamic multi-factor verification correctly, allowing the target user to perform the next operation; if the target user performs dynamic multi-factor verification with an error, prompting the target user that the verification is wrong.

[0008] Preferably, the risk assessment model adopts the following calculation formula:

[0009] R=α1*B+α2*E+α3*D+α4*H+α5*C

[0010] Among them, R is the final comprehensive risk score, B is the user behavior characteristic risk factor, E is the environmental risk factor, D is the device security risk factor, H is the historical interaction behavior risk factor, C is the contextual risk factor, and α1, α2, α3, α4, α5 are the weight coefficients of each risk factor.

[0011] Preferably, the verification strategy generation model adopts the following calculation formula:

[0012]

[0013] Among them, R is the result of risk assessment, and T1 and T2 are preset risk thresholds.

[0014] Preferably, the multi-factor authentication includes: password authentication, SMS authentication, security authentication and biometric authentication, wherein the biometric authentication includes: fingerprint authentication, face authentication and iris authentication.

[0015] Preferably, the method further comprises: dynamically adjusting the multi-factor authentication strategy of the target user based on the historical operation behavior data and historical environment data of the target user.

[0016] In the second aspect, the present invention also provides a dynamic multi-factor verification system based on artificial intelligence, including: an acquisition module for acquiring the operation behavior data and environmental data of the target user; an evaluation module for inputting the operation behavior data and environmental data into a preset risk assessment model to obtain the risk assessment result of the target user; a generation module for inputting the risk assessment result into a preset multi-factor verification strategy generation model based on an artificial intelligence algorithm to generate a dynamic multi-factor verification strategy.

[0017] Preferably, the system also includes: a verification module, used to guide the target user to perform verification according to the dynamic multi-factor verification strategy; if the target user is correct in all aspects when performing the dynamic multi-factor verification, the target user is allowed to perform the next operation; if the target user makes an error when performing the dynamic multi-factor verification, the target user is prompted with a verification error.

[0018] A third aspect: The present invention also provides a computer electronic device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of any one of the above-mentioned dynamic multi-factor authentication methods based on artificial intelligence are implemented.

[0019] A fourth aspect: The present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of any one of the above-mentioned dynamic multi-factor authentication methods based on artificial intelligence are implemented.

[0020] The present invention provides a dynamic multi-factor verification method, system, device and storage medium based on artificial intelligence. The method obtains the operation behavior data and environmental data of the target user; inputs the operation behavior data and environmental data into a preset risk assessment model to obtain the result of the risk assessment of the target user; inputs the result of the risk assessment into a preset multi-factor verification strategy generation model based on an artificial intelligence algorithm to generate a dynamic multi-factor verification strategy. Compared with the related art, it has the following effects: the present invention constructs a user operation behavior risk assessment model to analyze and evaluate the real-time operation behavior and environmental data of the target user, thereby obtaining the result of the risk assessment, and finally generates a dynamic multi-factor verification strategy based on the result of the risk assessment. Compared with the traditional static method, the verification method of the present invention is more intelligent and flexible, which improves the user experience while also improving the security of information data. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is a flowchart of a dynamic multi-factor authentication method based on artificial intelligence according to an embodiment of the present invention;

[0022] Figure 2 It is a structural diagram of a dynamic multi-factor authentication system based on artificial intelligence according to an embodiment of the present invention;

[0023] Figure 3 It is a structural schematic diagram of a computer electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0024] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0025] It should be noted that when an element is referred to as being "fixed to" another element, it may be directly on the other element or there may be a central element. When an element is considered to be "connected to" another element, it may be directly connected to the other element or there may be a central element at the same time. In contrast, when an element is referred to as being "directly on" another element, there is no intermediate element. The terms "vertical", "horizontal", "left", "right" and similar expressions used herein are for illustrative purposes only.

[0026] In the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0027] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0028] The terms used in one or more embodiments of the present invention are only for the purpose of describing specific embodiments, and are not intended to limit one or more embodiments of the present invention. The singular forms "a", "said" and "the" used in one or more embodiments of the present invention are also intended to include plural forms, unless the context clearly indicates other meanings.

[0029] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein in the specification of the template are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more of the associated listed items.

[0030] It should be understood that, although the terms first, second, etc. may be used to describe various information in one or more embodiments of the present invention, these information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of one or more embodiments of the present invention, the first may also be referred to as the second, and similarly, the second may also be referred to as the first. Depending on the context, the word "if" as used herein may be interpreted as "when..." or "when...".

[0031] At present, the existing multi-factor authentication methods have the following problems: 1. Static authentication factors cannot effectively deal with complex security threats and are difficult to dynamically adapt to risk environments. 2. MFA authentication provides a poor user experience, often interrupting user operations due to too frequent authentication. 3. The scalability of existing technologies in multi-scenario applications is limited, and intelligent adjustments cannot be made based on user behavior. The above problems have brought great inconvenience to users when using various platform systems, resulting in a poor user experience.

[0032] The technical solution of the present invention and how the technical solution of the present invention solves the above-mentioned technical problems are described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes are not repeated in some embodiments. The embodiments of the present invention will be described below in conjunction with the accompanying drawings.

[0033] See also Figure 1 The present invention provides a dynamic multi-factor authentication method based on artificial intelligence, which is applied to a dynamic multi-factor authentication system. The specific application scenarios may include various platform login scenarios, payment scenarios, and security device connection scenarios. The method includes at least the following steps:

[0034] S10. Obtaining the target user's operation behavior data and environment data.

[0035] Specifically, in this example, it is first necessary to obtain the user's operation behavior data and environmental data. The operation behavior data refers to the actions that the user is performing. For example, the user behavior operation can be any of a login operation, a registration operation, an operation to modify user information, a user order operation, or a user coupon collection operation. Of course, the embodiments of the present invention only use the operation behavior data for the above-mentioned operations as an example for explanation, but it does not mean that the embodiments of the present invention are limited to this.

[0036] Environmental data refers to environmental factors related to the user. For example, the environmental data may be geographic location data, device information data, network status and other environmental data. Similarly, the embodiments of the present invention are only described using the environmental data as the above-mentioned examples, but do not mean that the embodiments of the present invention are limited to this.

[0037] S20: Input the operation behavior data and environment data into a preset risk assessment model to obtain a risk assessment result of the target user.

[0038] It should be noted that the risk assessment model is a tool for assessing various potential risks that may occur, and has a wide range of applications in different fields. The risk assessment model in this example is a risk assessment model trained based on a big data platform.

[0039] Exemplarily, the risk assessment model may include a risk identification module and a risk assessment module. The risk identification module is used to identify the types of risks that may occur. Exemplarily, the risk types may include user identity authentication risks and network security risks, etc., while the risk assessment module is used to obtain risk identification results based on the type of risk. It can be understood that the result of risk identification can be a corresponding numerical value or a probability value.

[0040] In a specific embodiment, the risk assessment model uses the following calculation formula:

[0041] R=α1*B+α2*E+α3*D+α4*H+α5*C

[0042] Among them, R is the final comprehensive risk score, which is used to determine the strength of multi-factor authentication to be used; B is the risk factor of user operation behavior characteristics, such as abnormal login behavior, deviation from operation habits, etc.; E is the environmental risk factor, such as whether the login device is a commonly used device, whether the login location is abnormal, network conditions, etc.; D is the device security risk factor, such as whether the device has security vulnerabilities, whether the device has passed trusted verification, etc.; H is the historical interaction behavior risk factor, such as the frequency and regularity of users' past logins, whether there have been security accidents, etc.; C is the contextual risk factor, such as the sensitivity of the current operation, the high or low transaction amount or operation type, etc.; α1, α2, α3, α4, α5 are the weight coefficients of each risk factor, which reflect the degree of influence of each risk factor on the total risk score. The weight coefficient can be adjusted according to different scenarios through model training or historical data.

[0043] S30: Input the risk assessment result into a preset artificial intelligence-based multi-factor authentication strategy generation model to generate a dynamic multi-factor authentication strategy.

[0044] Specifically, in this embodiment, after obtaining the risk assessment result, the assessment result is input into the multi-factor authentication strategy generation model based on artificial intelligence to generate a dynamic multi-factor authentication strategy.

[0045] Exemplarily, the multi-factor authentication includes: password authentication, SMS authentication, security authentication and biometric authentication, wherein the biometric authentication includes: fingerprint authentication, face authentication and iris authentication.

[0046] In a specific embodiment, the verification strategy generation model adopts the following calculation formula:

[0047]

[0048] Among them, R is the result of risk assessment, and T1 and T2 are preset risk thresholds.

[0049] Exemplarily, for the login scenario, when the result of the risk assessment is in the low-risk area, a single-factor verification login can be used, such as password login and verification code login, etc., and when the result of the risk assessment is in the medium-risk area, a two-factor verification login is required, such as password plus verification code login, etc., and when the result of the risk assessment is in the high-risk area, a multi-factor verification login is required, such as password + verification code + face verification login, etc. It should be noted that the embodiments of the present invention only take the above-mentioned cases as examples for illustration, but it does not mean that the embodiments of the present invention are limited to this.

[0050] The present invention provides a dynamic multi-factor verification method based on artificial intelligence, which obtains the target user's operation behavior data and environmental data; inputs the operation behavior data and environmental data into a preset risk assessment model to obtain the result of the target user's risk assessment; inputs the result of the risk assessment into a preset multi-factor verification strategy generation model based on an artificial intelligence algorithm to generate a dynamic multi-factor verification strategy. Compared with the related art, the present invention has the following effects: the present invention constructs a user operation behavior risk assessment model to analyze and evaluate the target user's real-time operation behavior and environmental data, thereby obtaining the result of the risk assessment, and finally generates a dynamic multi-factor verification strategy based on the result of the risk assessment. Compared with the traditional static method, the verification method of the present invention is more intelligent and flexible, which improves the user experience while also improving the security of information data.

[0051] In some optional embodiments, the method further includes: S40, guiding the target user to perform verification according to the dynamic multi-factor verification strategy; if the target user performs dynamic multi-factor verification correctly, allowing the target user to perform the next operation; if the target user performs dynamic multi-factor verification with an error, prompting the target user that the verification is wrong.

[0052] It is understandable that when a dynamic multi-factor authentication policy is generated, the system will automatically guide the user to complete the verification. If the user's verification is correct, the user is allowed to proceed to the next step. If an error occurs during the user's verification process, the user will be prompted that the verification is wrong.

[0053] For example, in a multi-factor authentication scenario of password + verification code + face verification, the authentication is successful only when all three verifications are correct. If any one of the factors of password + verification code + face verification fails, the user will be prompted with a verification error.

[0054] In some optional embodiments, the method further includes: dynamically adjusting the multi-factor authentication policy of the target user based on the historical operation behavior data and historical environment data of the target user.

[0055] Specifically, in this embodiment, an adaptive learning model is also introduced. By inputting the historical operation behavior data and historical environment data of the target user into the adaptive learning model for training, the adaptive learning model can adjust the user's verification strategy according to the user's operation habits.

[0056] For example, in a payment scenario, when a user uses a new device to make payments, in order to protect the safety of the user's property, a multi-factor verification login is generally required. However, as the user continues to use the device to make payments, if the user is required to perform multiple verifications each time, it will seriously affect the user's experience. Therefore, in this implementation, the adaptive learning model will determine that the device is a new device of the user based on the user's historical operation behavior data and historical environment data, thereby reducing the number of verifications and improving the user experience.

[0057] See also Figure 2 The present invention also provides a dynamic multi-factor authentication system 200 based on artificial intelligence, comprising:

[0058] The acquisition module 201 is used to acquire the operation behavior data and environment data of the target user;

[0059] An evaluation module 202, used to input the operation behavior data and environmental data into a preset risk evaluation model to obtain a risk evaluation result of the target user;

[0060] The generation module 203 is used to input the risk assessment result into a preset multi-factor authentication strategy generation model based on an artificial intelligence algorithm to generate a dynamic multi-factor authentication strategy.

[0061] In some optional embodiments, the system also includes: a verification module 204, which is used to guide the target user to perform verification according to the dynamic multi-factor verification strategy; if the target user is correct in all aspects when performing the dynamic multi-factor verification, the target user is allowed to perform the next step; if the target user makes an error when performing the dynamic multi-factor verification, the target user is prompted with a verification error.

[0062] See also Figure 3 An embodiment of the present invention further provides a computer electronic device 400, comprising a memory 403 and a processor 402, wherein the memory 403 stores a computer program, and when the processor executes the computer program, the steps of any one of the above-mentioned dynamic multi-factor authentication methods based on artificial intelligence are implemented.

[0063] Specifically, the electronic device 400 includes: a transceiver 401, a bus interface and a processor 402, wherein the processor 402 is used to obtain the operation behavior data and environmental data of the target user; input the operation behavior data and environmental data into a preset risk assessment model to obtain the result of the risk assessment of the target user; input the result of the risk assessment into a preset multi-factor verification strategy generation model based on an artificial intelligence algorithm to generate a dynamic multi-factor verification strategy.

[0064] In the embodiment of the present invention, the electronic device 400 further includes a memory 403. Figure 3 In the embodiment, the bus architecture may include any number of interconnected buses and bridges, specifically linking various circuits of one or more processors represented by processor 402 and memory represented by memory 403. The bus architecture may also link various other circuits such as peripherals, voltage regulators, and power management circuits, which are well known in the art and are therefore not further described herein. The bus interface provides an interface. The transceiver 401 may be a plurality of components, i.e., including a transmitter and a receiver, providing a unit for communicating with various other devices on a transmission medium. The processor 402 is responsible for managing the bus architecture and general processing, and the memory 403 may store data used by the processor 402 when performing operations.

[0065] An embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of any one of the above-mentioned dynamic multi-factor authentication methods based on artificial intelligence are implemented.

[0066] In this embodiment, the computer readable storage medium may be a non-volatile storage medium or a volatile storage medium. For example, the computer storage medium may include but is not limited to: a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

[0067] In all examples shown and described herein, any specific values ​​should be interpreted as merely exemplary and not as limiting, and thus other examples of the exemplary embodiments may have different values.

[0068] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0069] In several embodiments provided in the present application, it should be understood that the disclosed devices and methods can also be implemented in other ways. The device embodiments described above are merely schematic. For example, the flowcharts and structure diagrams in the accompanying drawings show the possible architecture, functions and operations of the devices, methods and computer program products according to multiple embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of a code, and the module, a program segment or a part of a code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in an alternative implementation, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes 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 box in the structure diagram and / or the flow diagram, and the combination of boxes in the structure diagram and / or the flow diagram, can be implemented with a dedicated hardware-based system that performs a specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions.

[0070] In addition, the functional modules or units in the various embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.

[0071] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for enabling a terminal device (which can be a smart phone, a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application.

[0072] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the 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.

Claims

1. A dynamic multi-factor authentication method based on artificial intelligence, characterized in that: include: Obtain the target user's operation behavior data and environment data; Inputting the operation behavior data and environmental data into a preset risk assessment model to obtain a risk assessment result of the target user; The risk assessment results are input into a preset multi-factor authentication strategy generation model based on an artificial intelligence algorithm to generate a dynamic multi-factor authentication strategy.

2. The dynamic multi-factor authentication method according to claim 1, characterized in that: The method further comprises: According to the dynamic multi-factor authentication strategy, guiding the target user to perform authentication; If the target user is correct in all aspects of dynamic multi-factor authentication, the target user is allowed to proceed to the next step; If the target user makes an error when performing dynamic multi-factor authentication, the target user is prompted that the authentication is wrong.

3. The dynamic multi-factor authentication method according to claim 1, characterized in that: The risk assessment model uses the following calculation formula: R=α1*B+α2*E+α3*D+α4*H+α5*C Among them, R is the final comprehensive risk score, B is the user behavior characteristic risk factor, E is the environmental risk factor, D is the device security risk factor, H is the historical interaction behavior risk factor, C is the contextual risk factor, and α1, α2, α3, α4, α5 are the weight coefficients of each risk factor.

4. The dynamic multi-factor authentication method according to claim 1, characterized in that: The verification strategy generation model adopts the following calculation formula: Among them, R is the result of risk assessment, and T1 and T2 are preset risk thresholds.

5. The dynamic multi-factor authentication method according to claim 1, characterized in that: The multi-factor authentication includes: password authentication, SMS authentication, security authentication and biometric authentication, wherein the biometric authentication includes: fingerprint authentication, face authentication and iris authentication.

6. The dynamic multi-factor authentication method according to claim 1, characterized in that: The method further comprises: Based on the historical operation behavior data and historical environment data of the target user, the multi-factor authentication strategy of the target user is dynamically adjusted.

7. A dynamic multi-factor authentication system based on artificial intelligence, characterized in that: include: An acquisition module is used to acquire the target user's operation behavior data and environment data; An evaluation module, used to input the operation behavior data and environmental data into a preset risk evaluation model to obtain a risk evaluation result of the target user; The generation module is used to input the results of the risk assessment into a preset multi-factor authentication strategy generation model based on an artificial intelligence algorithm to generate a dynamic multi-factor authentication strategy.

8. The dynamic multi-factor authentication system according to claim 7, characterized in that: The system further comprises: The verification module is used to guide the target user to perform verification according to the dynamic multi-factor verification strategy; if the target user performs the dynamic multi-factor verification correctly, the target user is allowed to perform the next operation; if the target user performs the dynamic multi-factor verification incorrectly, the target user is prompted that the verification is incorrect.

9. A computer electronic device, characterized in that: The invention comprises a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the dynamic multi-factor authentication method based on artificial intelligence described in any one of claims 1 to 6 when executing the computer program.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the dynamic multi-factor authentication method based on artificial intelligence described in any one of claims 1 to 6 are implemented.

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