Internet of Things authentication method and system

Abstract

The invention discloses an Internet of Things authentication method and system, and relates to the technical field of Internet of Things. The system comprises a quantum machine learning behavior analysis module, a multi-factor authentication module, a block chain and intelligent contract module, an edge computing authentication processing module, a self-adaptive authentication strategy and risk assessment module, a privacy protection and data desensitization module and a cross-platform interoperation module. According to the invention, the security, efficiency and flexibility of the Internet of Things authentication system are significantly improved, so that the system can efficiently identify abnormal behaviors of equipment, effectively prevent potential security threats, significantly reduce authentication delay, and improve the response speed and user experience of the system. Therefore, the problems existing in the aspects of safety, authentication efficiency and expandability of a traditional authentication method are solved, and a safer, more efficient and more extensible authentication mechanism is provided for the Internet of Things equipment.

Description

Technical Field

[0001] The present invention relates to the technical field of Internet of Things, and in particular to an Internet of Things authentication method and system. Background Art

[0002] With the rapid development of IoT technology, the number of IoT devices has exploded, covering multiple fields such as smart homes, industrial automation, smart cities, and smart transportation. The popularity of IoT devices has brought unprecedented convenience and efficiency improvements, but it has also posed severe challenges to security authentication mechanisms. Traditional security authentication methods, such as password-based authentication and hardware-based authentication, have problems such as insufficient security, low authentication efficiency, and poor scalability when facing large-scale IoT devices.

[0003] First, the security of traditional authentication methods is seriously threatened. IoT devices are usually deployed in open and complex environments and are vulnerable to various forms of threats such as physical attacks and network attacks. Attackers can bypass traditional authentication mechanisms and gain illegal access and control over IoT devices by cracking passwords and counterfeiting hardware.

[0004] Secondly, the traditional authentication method has low authentication efficiency. As the number of IoT devices increases, traditional authentication methods consume a lot of computing and communication resources, resulting in a slow authentication process that cannot meet the requirements of real-time and efficiency. Especially in large-scale IoT scenarios, the authentication bottleneck problem is particularly prominent.

[0005] In order to solve the above problems, in summary, the present invention proposes an Internet of Things authentication method and system, which aims to solve the problems of traditional authentication methods in terms of security, authentication efficiency and scalability, and provide a safer, more efficient and more scalable authentication mechanism for Internet of Things devices. Summary of the invention

[0006] 1. Technical issues to be resolved

[0007] In view of the shortcomings of the prior art, the present invention provides an Internet of Things authentication method and system, which significantly improves the security, efficiency and flexibility of the Internet of Things authentication system, enables the system to efficiently identify abnormal device behavior, effectively prevent potential security threats, significantly reduces authentication delays, and improves the system's response speed and user experience. Thus, the problems in the background technology are solved.

[0008] (II) Technical solution

[0009] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0010] An Internet of Things authentication method comprises the steps of:

[0011] a. Use quantum machine learning algorithms to analyze the behavior data of IoT devices, extract features and train anomaly detection models. The behavior data include but are not limited to device operation logs, communication patterns, energy consumption data, etc.;

[0012] b. Embed a physical unclonable function in IoT devices, use the physical characteristics of the device to generate a unique and non-copyable device fingerprint, and combine it with quantum key distribution technology to securely generate and transmit keys between devices, forming a dual security authentication mechanism combining the physical layer and the quantum layer;

[0013] c. Combine device fingerprints, quantum keys, user behavior patterns, and device physical location information to perform multi-factor authentication, where user behavior patterns include user operating habits, access frequency, etc.;

[0014] d. Record detailed information of each authentication transaction on the blockchain, including but not limited to authentication timestamp, device ID, user ID, authentication result, smart contract execution status, and physical layer security feature information, to ensure the transparency and immutability of the authentication process;

[0015] e. Use edge computing nodes and fog computing technology to delegate some authentication tasks to the edge of the network to reduce data transmission delays and improve authentication efficiency;

[0016] f. Dynamically adjust authentication strategies and risk assessment models based on device type, user behavior patterns, network environment, physical layer security features, and historical authentication records, and use machine learning algorithms to continuously optimize risk assessment models;

[0017] g. During the authentication process, differential privacy, homomorphic encryption and quantum encryption technologies are used to protect user privacy and data security, ensuring the security of sensitive data during transmission and storage;

[0018] h. Provide cross-platform interoperability support, design system interfaces and protocols, ensure that the authentication system can be seamlessly integrated with other IoT platforms and services, and achieve cross-platform authentication information sharing and interoperability.

[0019] Preferably, in the above-mentioned IoT authentication method, the training process of the quantum machine learning algorithm includes:

[0020] Collect behavioral data of IoT devices and perform preprocessing, including data cleaning and feature extraction;

[0021] Build a quantum machine learning model and set model parameters;

[0022] Take advantage of the parallelism and speed of quantum computing devices to train models and optimize model parameters;

[0023] Validate and test the trained model to ensure it can accurately identify abnormal behavior.

[0024] Preferably, in the above-mentioned IoT authentication method, the generation process of the physical unclonable function PUF includes:

[0025] Designing PUF circuits using the physical properties of the device;

[0026] Stimulate the PUF circuit to generate a unique device fingerprint;

[0027] Combine the device fingerprint with the key generated by quantum key distribution technology to form composite authentication information.

[0028] Preferably, in the above-mentioned IoT authentication method, the information recorded in the blockchain also includes:

[0029] Device physical location information, used to verify whether the physical location of the device matches the authentication request;

[0030] Device status information, such as whether the device is online and in a safe state;

[0031] The execution log of the smart contract is used to record the execution process and results of the smart contract.

[0032] An Internet of Things authentication system, the system comprising:

[0033] Quantum machine learning behavior analysis module, which is used to analyze the behavior data of IoT devices and train anomaly detection models;

[0034] Physical unclonable function PUF and quantum key distribution module, used to generate device fingerprint and quantum key, forming a dual security authentication mechanism;

[0035] Multi-factor authentication module, which combines device fingerprint, quantum key, user behavior pattern and device physical location information for authentication;

[0036] Blockchain and smart contract modules, used to record and authenticate transaction information and execute smart contracts;

[0037] The edge computing authentication processing module uses edge computing nodes and fog computing technology to perform some authentication tasks;

[0038] Adaptive authentication strategy and risk assessment module, dynamically adjusts authentication strategy and risk assessment model according to factors such as device type and user behavior pattern;

[0039] Privacy protection and data desensitization module, which uses differential privacy, homomorphic encryption and quantum encryption technologies to protect user privacy and data security;

[0040] Cross-platform interoperability modules to enable seamless integration with other IoT platforms and services.

[0041] Preferably, in the above-mentioned Internet of Things authentication system, a distributed ledger module is also included to realize distributed storage and verification of authentication records to ensure the integrity and security of authentication records.

[0042] Preferably, in the above-mentioned IoT authentication system, the adaptive authentication strategy and risk assessment module further includes:

[0043] Real-time risk assessment submodule, used to monitor device behavior in real time and assess potential security risks;

[0044] The authentication strategy optimization submodule dynamically adjusts the authentication strategy and authentication level according to the risk assessment results;

[0045] The machine learning algorithm optimization submodule uses machine learning algorithms to continuously optimize the risk assessment model.

[0046] Preferably, the above-mentioned IoT authentication system also includes a user interface module for displaying authentication results, blockchain record query function, authentication policy configuration options and cross-platform integration status, providing a user-friendly interactive interface.

[0047] Preferably, the above-mentioned IoT authentication system further includes a developer interface module, which provides API and SDK to facilitate developers to integrate the authentication system into their IoT applications to achieve the scalability and flexibility of the system.

[0048] Preferably, in the above-mentioned IoT authentication system, the IoT authentication system further includes a security audit module for conducting a security audit on the authentication process to ensure the compliance and security of the authentication process. The security audit module can record key events, abnormal behaviors, and system operation status during the authentication process for analysis and review by security managers.

[0049] (III) Beneficial effects

[0050] The present invention provides an Internet of Things authentication method and system, which has the following beneficial effects:

[0051] Significantly improve security: By introducing quantum machine learning algorithms for behavioral data analysis, it is possible to more accurately identify abnormal behaviors of IoT devices and effectively prevent potential security threats.

[0052] By utilizing the physical unclonable function PUF and quantum key distribution technology, a dual security authentication mechanism combining the physical layer and the quantum layer is formed to ensure the uniqueness and non-replicability of the device identity, while realizing the secure generation and transmission of keys, greatly improving the security of the authentication system.

[0053] Enhanced authentication efficiency and flexibility: Combining edge computing nodes and fog computing technology, some authentication tasks are delegated to the edge of the network for execution, significantly reducing data transmission delays and improving authentication efficiency. Provide cross-platform interoperability support to ensure that the authentication system can be seamlessly integrated with other IoT platforms and services, achieve cross-platform authentication information sharing and interoperability, and enhance the flexibility and scalability of the system.

[0054] Optimize user experience and privacy protection: Dynamically adjust authentication strategies and risk assessment models based on factors such as device type and user behavior patterns, making the authentication process more intelligent and personalized, and improving user experience. Use differential privacy, homomorphic encryption, and quantum encryption technologies to protect user privacy and data security, ensure the security of sensitive data during transmission and storage, and enhance user trust in the system.

[0055] Improve the transparency and credibility of authentication: Record the detailed information of each authentication transaction on the blockchain, including authentication timestamp, device ID, user ID, etc., to ensure the transparency and immutability of the authentication process and improve the credibility of the authentication results. Provide a security audit module to conduct a security audit of the authentication process to ensure the compliance and security of the authentication process, further enhancing the credibility of the system.

[0056] In summary, the present invention provides an Internet of Things authentication method and system, which significantly improves the security, efficiency, flexibility and user experience of the authentication system by introducing multiple technologies, while enhancing the transparency and credibility of the authentication process, providing a strong guarantee for the healthy development of the Internet of Things industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 It is a structural framework diagram of the present invention. DETAILED DESCRIPTION

[0058] 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 only 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.

[0059] See also Figure 1 , an Internet of Things authentication method, comprising the steps of:

[0060] a. Use quantum machine learning algorithms (including but not limited to quantum support vector machines, quantum neural networks, quantum convolutional neural networks, etc.) to analyze the behavioral data of IoT devices, extract features and train anomaly detection models. The behavioral data includes but is not limited to device operation logs, communication patterns, energy consumption data, etc.;

[0061] b. Embed the physical unclonable function PUF in IoT devices, use the physical characteristics of the device (such as manufacturing process differences, material characteristics, etc.) to generate a unique and non-replicable device fingerprint, and combine it with quantum key distribution (QKD) technology to securely generate and transmit keys between devices, forming a dual security authentication mechanism combining the physical layer and the quantum layer;

[0062] c. Combine device fingerprints, quantum keys, user behavior patterns, and device physical location information to perform multi-factor authentication, where user behavior patterns include user operating habits, access frequency, etc.;

[0063] d. Record detailed information of each authentication transaction on the blockchain, including but not limited to authentication timestamp, device ID, user ID, authentication result, smart contract execution status, and physical layer security feature information, to ensure the transparency and immutability of the authentication process;

[0064] e. Use edge computing nodes and fog computing technology to delegate some authentication tasks (such as preliminary identity authentication, behavior pattern matching, etc.) to the edge of the network to reduce data transmission delays and improve authentication efficiency;

[0065] f. Dynamically adjust authentication strategies and risk assessment models based on device type, user behavior patterns, network environment, physical layer security features, and historical authentication records, and use machine learning algorithms (including quantum machine learning algorithms) to continuously optimize risk assessment models;

[0066] g. During the authentication process, differential privacy, homomorphic encryption and quantum encryption technologies (such as quantum key encapsulation mechanism, quantum secure direct communication, etc.) are used to protect user privacy and data security, and ensure the security of sensitive data during transmission and storage;

[0067] h. Provide cross-platform interoperability support, design system interfaces and protocols, ensure that the authentication system can be seamlessly integrated with other IoT platforms and services, and achieve cross-platform authentication information sharing and interoperability.

[0068] To further optimize the above technical solutions, the training process of the quantum machine learning algorithm includes:

[0069] Collect behavioral data of IoT devices and perform preprocessing, including data cleaning and feature extraction;

[0070] Build a quantum machine learning model and set model parameters;

[0071] Take advantage of the parallelism and speed of quantum computing devices to train models and optimize model parameters;

[0072] Validate and test the trained model to ensure it can accurately identify abnormal behavior.

[0073] In order to further optimize the above technical solution, the generation process of the physical unclonable function PUF includes:

[0074] Design PUF circuits using the physical characteristics of the device (e.g. manufacturing process differences, material properties, etc.);

[0075] Stimulate the PUF circuit to generate a unique device fingerprint;

[0076] Combine the device fingerprint with the key generated by quantum key distribution (QKD) technology to form composite authentication information.

[0077] In order to further optimize the above technical solutions, the information recorded in the blockchain also includes:

[0078] Device physical location information, used to verify whether the physical location of the device matches the authentication request;

[0079] Device status information, such as whether the device is online and in a safe state;

[0080] The execution log of the smart contract is used to record the execution process and results of the smart contract.

[0081] An Internet of Things authentication system, the system comprising:

[0082] Quantum machine learning behavior analysis module, which is used to analyze the behavior data of IoT devices and train anomaly detection models;

[0083] Physical unclonable function PUF and quantum key distribution module, used to generate device fingerprint and quantum key, forming a dual security authentication mechanism;

[0084] Multi-factor authentication module, which combines device fingerprint, quantum key, user behavior pattern and device physical location information for authentication;

[0085] Blockchain and smart contract modules, used to record and authenticate transaction information and execute smart contracts;

[0086] The edge computing authentication processing module uses edge computing nodes and fog computing technology to perform some authentication tasks;

[0087] Adaptive authentication strategy and risk assessment module, dynamically adjusts authentication strategy and risk assessment model according to factors such as device type and user behavior pattern;

[0088] Privacy protection and data desensitization module, which uses differential privacy, homomorphic encryption and quantum encryption technologies to protect user privacy and data security;

[0089] Cross-platform interoperability modules to enable seamless integration with other IoT platforms and services.

[0090] In order to further optimize the above technical solution, a distributed ledger module is also included to realize distributed storage and verification of authentication records to ensure the integrity and security of authentication records.

[0091] In order to further optimize the above technical solutions, the adaptive authentication strategy and risk assessment module also includes:

[0092] Real-time risk assessment submodule, used to monitor device behavior in real time and assess potential security risks;

[0093] The authentication strategy optimization submodule dynamically adjusts the authentication strategy and authentication level according to the risk assessment results;

[0094] The machine learning algorithm optimization submodule uses machine learning algorithms (including quantum machine learning algorithms) to continuously optimize the risk assessment model.

[0095] In order to further optimize the above technical solution, a user interface module is also included to display authentication results, blockchain record query functions, authentication policy configuration options and cross-platform integration status, providing a user-friendly interactive interface.

[0096] In order to further optimize the above technical solutions, a developer interface module is also included, which provides API and SDK to facilitate developers to integrate the authentication system into their IoT applications and achieve the scalability and flexibility of the system.

[0097] In order to further optimize the above technical solutions, the IoT authentication system also includes a security audit module, which is used to conduct security audits on the authentication process to ensure the compliance and security of the authentication process. The security audit module can record key events, abnormal behaviors, and system operation status during the authentication process for security managers to analyze and review.

[0098] Technical principle description:

[0099] Quantum Machine Learning Algorithms:

[0100] Principle: Quantum machine learning is a cutting-edge field that integrates quantum mechanics and machine learning. It uses the advantages of quantum computing to improve and optimize traditional machine learning algorithms. In IoT authentication, quantum machine learning algorithms can efficiently process and analyze the massive data generated by IoT devices and accurately identify the normal and abnormal behaviors of devices.

[0101] Physically Unclonable Function (PUF):

[0102] Principle: PUF is a technology that uses physical randomness to create a unique ID key. Based on the random characteristics of hardware, each device has a unique fingerprint or ID that is difficult to copy or clone.

[0103] Quantum Key Distribution (QKD):

[0104] Principle: QKD uses the principles of quantum entanglement and quantum measurement to achieve secure key distribution. When distributing keys between communicating parties, even if an attacker eavesdrops, the key information cannot be obtained, ensuring the security of key distribution.

[0105] Blockchain Technology:

[0106] Principle: Blockchain is a distributed ledger technology that ensures the security and immutability of data through encryption algorithms and consensus mechanisms. On the blockchain, each transaction is recorded and forms a block, which are connected into a chain in chronological order.

[0107] Edge computing and fog computing:

[0108] Principle: Edge computing is a technology that deploys data processing and computing capabilities at the edge of the network, that is, near IoT devices or terminals. Fog computing is a more extensive distributed computing model that includes a variety of computing resources including edge computing.

[0109] Cross-platform interoperability:

[0110] Principle: Cross-platform interoperability refers to the ability of systems, applications and devices on different platforms to communicate and collaborate with each other. This is usually achieved by developing unified standards and protocols.

[0111] Privacy protection technology:

[0112] Principle: Privacy protection technologies include differential privacy, homomorphic encryption, quantum encryption and other methods, aiming to protect user privacy and data security.

[0113] Application: In IoT authentication, privacy protection technology can be used to protect the security of users' sensitive information during transmission and storage. For example, differential privacy technology can be used to analyze data while protecting user privacy; homomorphic encryption allows data to be calculated without decryption; and quantum encryption uses the principles of quantum mechanics to achieve secure data transmission and storage.

[0114] In summary, the IoT authentication method and system integrates multiple cutting-edge technologies such as quantum machine learning, physically unclonable functions, quantum key distribution, blockchain, edge computing and fog computing, cross-platform interoperability and privacy protection, and realizes intelligent authentication of IoT devices.

[0115] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website site, a computer, a server or a data center by a wired (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or a data center containing one or more available media sets.

[0116] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0117] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0118] If the functions are implemented in the form of software functional units 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 can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks or optical disks.

[0119] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0120] 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. An Internet of Things authentication method, characterized in that: Includes steps: a. Use quantum machine learning algorithms to analyze the behavioral data of IoT devices, extract features, and train anomaly detection models. The behavioral data includes device operation logs, communication patterns, and energy consumption data. b. Embed the physical unclonable function PUF in the IoT device, use the physical characteristics of the device to generate a unique and non-copyable device fingerprint, and combine it with quantum key distribution technology to securely generate and transmit keys between devices, forming a dual security authentication mechanism combining the physical layer and the quantum layer; c. Combine device fingerprints, quantum keys, user behavior patterns, and device physical location information to perform multi-factor authentication, where user behavior patterns include user operating habits and access frequency; d. Record the detailed information of each authentication transaction on the blockchain, including authentication timestamp, device ID, user ID, authentication result, smart contract execution status, and physical layer security feature information, making the authentication process transparent and tamper-proof; e. Use edge computing nodes and fog computing technology to delegate some authentication tasks to the edge of the network to reduce data transmission delays; f. Dynamically adjust authentication strategies and risk assessment models based on device type, user behavior patterns, network environment, physical layer security features, and historical authentication records, and use machine learning algorithms to continuously optimize risk assessment models; g. During the authentication process, differential privacy, homomorphic encryption and quantum encryption technologies are used to protect user privacy and data security, ensuring the security of sensitive data during transmission and storage; h. Provide cross-platform interoperability support, design system interfaces and protocols, ensure that the authentication system can be seamlessly integrated with other IoT platforms and services, and enable cross-platform authentication information sharing and interoperability.

2. The Internet of Things authentication method according to claim 1, characterized in that: The training process of the quantum machine learning algorithm includes: Collect behavioral data of IoT devices and perform preprocessing, including data cleaning and feature extraction; Build a quantum machine learning model and set model parameters; Take advantage of the parallelism and speed of quantum computing devices to train models and optimize model parameters; Validate and test the trained model to ensure it can accurately identify abnormal behavior.

3. The Internet of Things authentication method according to claim 1, characterized in that: The generation process of the physical unclonable function PUF includes: Designing PUF circuits using the physical properties of the device; Stimulate the PUF circuit to generate a unique device fingerprint; Combine the device fingerprint with the key generated by quantum key distribution technology to form composite authentication information.

4. The Internet of Things authentication method according to claim 1, characterized in that: The information recorded in the blockchain also includes: Device physical location information, used to verify whether the physical location of the device matches the authentication request; Device status information, including whether it is online and in a safe state; The execution log of the smart contract is used to record the execution process and results of the smart contract.

5. An Internet of Things authentication system, implementing the Internet of Things authentication method according to any one of claims 1 to 4, characterized in that: The system includes: Quantum machine learning behavior analysis module, which is used to analyze the behavior data of IoT devices and train anomaly detection models; Physical unclonable function PUF and quantum key distribution module, used to generate device fingerprint and quantum key, forming a dual security authentication mechanism; Multi-factor authentication module, which combines device fingerprint, quantum key, user behavior pattern and device physical location information for authentication; Blockchain and smart contract modules, used to record and authenticate transaction information and execute smart contracts; The edge computing authentication processing module uses edge computing nodes and fog computing technology to perform some authentication tasks; Adaptive authentication strategy and risk assessment module, dynamically adjusts authentication strategy and risk assessment model according to device type and user behavior pattern factors; Privacy protection and data desensitization module, which uses differential privacy, homomorphic encryption and quantum encryption technologies to protect user privacy and data security; Cross-platform interoperability modules to enable seamless integration with other IoT platforms and services.

6. The Internet of Things authentication system according to claim 5, characterized in that: It also includes a distributed ledger module to achieve distributed storage and verification of authentication records to ensure the integrity and security of authentication records.

7. The Internet of Things authentication system according to claim 6, characterized in that: The adaptive authentication strategy and risk assessment module also includes: Real-time risk assessment submodule, used to monitor device behavior in real time and assess potential security risks; The authentication strategy optimization submodule dynamically adjusts the authentication strategy and authentication level according to the risk assessment results; The machine learning algorithm optimization submodule uses machine learning algorithms to continuously optimize the risk assessment model.

8. The Internet of Things authentication system according to claim 5, characterized in that: It also includes a user interface module for displaying authentication results, blockchain record query functions, authentication policy configuration options, and cross-platform integration status, providing a user-friendly interactive interface.

9. The Internet of Things authentication system according to claim 5, characterized in that: It also includes a developer interface module that provides APIs and SDKs to facilitate developers to integrate the authentication system into their IoT applications and achieve system scalability and flexibility.

10. The Internet of Things authentication system according to claim 5, characterized in that: The system also includes a security audit module, which is used to conduct security audits on the authentication process to ensure the compliance and security of the authentication process. The security audit module can record key events, abnormal behaviors and system operation status information in the authentication process for analysis and review by security managers.

Images