A blockchain-based Internet of Things network security authentication method, device and equipment

Abstract

This application discloses a blockchain-based IoT network security authentication method, apparatus, and device, relating to the field of IoT security technology. The method includes receiving a registration request from an IoT device, establishing a blockchain-based distributed ledger, and recording the device registration information in the ledger distributed across the entire network; when a new device access request is received, verifying the access request according to the blockchain consensus algorithm to obtain a consensus algorithm verification result; determining whether the access request is recognized by the blockchain network based on the consensus algorithm verification result; if recognized, granting the device network access permissions; deploying an automated audit trail algorithm in the target IoT network, which continuously monitors the network communication and device behavior of the target IoT network, marking and triggering warnings for activities that deviate from a predefined pattern; this application solves the problem of insufficient security in traditional IoT authentication mechanisms and improves the stability and reliability of IoT systems.

Description

Technical Field

[0001] The present application relates to the field of Internet of Things security technology, and in particular to a blockchain-based Internet of Things networking security authentication method, device and equipment. Background Art

[0002] Currently, the Internet of Things technology is developing rapidly, and a large number of smart devices are connected to the network. However, the traditional centralized authentication mechanism is vulnerable to attacks and lacks security. In addition, the existing Internet of Things network security authentication usually relies on pre-set keys or certificates to achieve device identity authentication. Once the pre-set keys or certificates are leaked, the security of the entire Internet of Things network will be threatened. The centralized certification agency becomes a single point of failure, affecting the stability and reliability of the system, and therefore needs to be improved. Summary of the invention

[0003] In order to solve the problem of insufficient security of traditional IoT authentication mechanisms and improve the stability and reliability of IoT systems, the present application provides a blockchain-based IoT network security authentication method, device and equipment.

[0004] In the first aspect, the invention objective of the present application is achieved by adopting the following technical solutions:

[0005] A blockchain-based Internet of Things networking security authentication method, comprising:

[0006] Receive a registration request from an IoT device, the registration request including at least a unique device identifier, a public key, and an initial access timestamp, and determine device registration information of a target IoT network;

[0007] Establish a distributed ledger based on blockchain, record the device registration information in the ledger distributed throughout the network, so that all blockchain nodes hold the latest copy of the ledger;

[0008] When receiving an access request from a new device, the access request is verified according to the consensus algorithm of the blockchain to obtain the consensus algorithm verification result;

[0009] Based on the consensus algorithm verification result, determine whether the access request is approved by a plurality of associated blockchain nodes exceeding a preset number threshold in the blockchain network; if approved, grant the device corresponding network access rights;

[0010] The target IoT network deploys an automated audit tracking algorithm, which continuously monitors the network communications and device behaviors of the target IoT network, marks activities that deviate from predefined patterns and automatically triggers alarms; for activities marked as potential threats, an in-depth investigation procedure is initiated until the threat is resolved.

[0011] By adopting the above technical solutions, the distributed ledger of the blockchain records and stores the latest copy of the ledger to ensure the integrity and immutability of the information; the blockchain consensus algorithm authentication content includes the validity of the digital signature and the freshness of the timestamp; the automated audit tracking algorithm can mark abnormal or potentially threatening activities and data flows, including potential threat activities, abnormal activities, potential threat data flows, threat data flows, and abnormal data flows; multi-level security strategies, including but not limited to data encryption, security certificate updates, and firewall rule deployment, enhance the system's ability to resist external attacks; in-depth investigation procedures involve suspending device access rights, forcing re-authentication, or implementing advanced data analysis; this application improves The fault tolerance of the system is improved. Even if some nodes fail, the entire system can still operate normally. Through blockchain technology, the latest ledger information can be quickly synchronized to ensure the consistency of all nodes, thereby improving the reliability and stability of the system. At the same time, the automated audit tracking algorithm continuously monitors the network to provide new information and device behavior, and can promptly discover abnormal activities and abnormal data flows in the target IoT network. For activities that deviate from the predefined pattern, it automatically marks and triggers alarms, prompting the security team to take prompt action. In addition, the formula algorithm can automatically verify the access request of new devices, reducing manual intervention and simplifying the device access process. This application significantly enhances the security, reliability and transparency of the system, improves the efficiency of anomaly detection and response, and enhances user experience.

[0012] In a preferred example of the present application: the method further includes:

[0013] According to the historical connection records of each IoT device in the target IoT network, the network behavior data of multiple IoT devices are identified and collected to obtain the communication mode information of each IoT device; the communication mode information covers multiple communication phase intervals corresponding to different security levels; the IoT device is associated with a security identifier;

[0014] Based on the security identifier, retrieve the historical security authentication data set of each IoT device, perform training optimization for each security level in the preset security authentication model, and obtain an optimized security authentication model;

[0015] Based on the historical connection records of IoT devices, analyze and record the security association between IoT devices and determine the security association coefficient of all IoT devices;

[0016] In the optimized security authentication model, the security association coefficients of all IoT devices and the security policy adjustment rules corresponding to the historical security authentication data set are combined to determine the minimum security threshold of each IoT device in each communication phase interval and generate the best security authentication strategy;

[0017] According to the optimal security authentication strategy, security authentication is performed on each IoT device in the target IoT network.

[0018] By adopting the above technical solution, the network behavior data of IoT devices are identified and collected, the communication mode information of each device is obtained, and it is divided into multiple security levels, realizing multi-level security authentication, so as to provide differentiated security protection measures according to the security requirements of different devices. The optimized security authentication model is conducive to improving the accuracy and reliability of security authentication. By combining the security correlation between devices and the historical security authentication data set, the security strategy is dynamically adjusted to ensure that the security of the system is always in the optimal state. The present application continuously monitors the network behavior of IoT devices, analyzes the communication mode and security status of the devices in real time, can promptly detect abnormal activities and trigger alarms, and automatically performs security authentication on the devices according to the optimal security authentication strategy, thereby reducing manual intervention and improving response speed and efficiency.

[0019] In a preferred example of the present application, the method of identifying and collecting network behavior data of multiple IoT devices and obtaining communication mode information of each IoT device according to the historical connection records of each IoT device in the target IoT network specifically includes:

[0020] According to the historical connection records of each IoT device in the target IoT network, network communication data and corresponding security event logs of each IoT device are obtained, and multiple historical communication time periods are obtained;

[0021] Based on the security identification of IoT devices, the network communication data and corresponding security event logs of each IoT device in multiple historical communication periods are classified to obtain multiple historical network behavior data of each IoT device during operation;

[0022] Different communication mode information of each IoT device is determined based on the security event logs in the plurality of historical network behavior data and preset communication mode determination criteria.

[0023] By adopting the above technical solution, the historical connection records of each IoT device are obtained, multiple historical communication time periods are divided, and classified based on the security identification of the device. The communication mode of the device can be identified more finely. Combined with network communication data and security event logs, the network behavior of the device can be comprehensively analyzed, which can more accurately identify abnormal behavior and security incidents and reduce false positives and negatives; based on the preset communication mode determination criteria, the communication mode determination criteria are dynamically adjusted to ensure that the system can adapt to the ever-changing network environment and improve the robustness of the system. By analyzing historical network behavior, the system can quickly locate the cause of abnormal behavior, initiate corresponding response mechanisms, and reduce the impact of security incidents. The security event log provides detailed audit tracking to ensure the transparency and traceability of the system. The security event report includes the time, location, type and affected device list of abnormal activities to help administrators quickly locate and solve problems.

[0024] In a preferred example of the present application: the security association degree includes the communication frequency between devices, the data transmission volume, the consistency of the security protocol, and the association of historical security events;

[0025] The security association coefficients of all IoT devices are determined by analyzing and recording the security association degrees between IoT devices based on the historical connection records of IoT devices, specifically including:

[0026] Based on the historical connection records of IoT devices, the communication frequency, data volume of each communication, type of security protocol used, and historical security events between IoT devices are extracted, and the communication frequency coefficient, data transmission volume coefficient, security protocol consistency coefficient, and historical security event correlation coefficient between IoT devices are calculated;

[0027] By combining various coefficients and the preset algorithm, the security association coefficient between each IoT device is calculated.

[0028] By adopting the above technical solution, a multi-dimensional security assessment is performed in combination with communication frequency, data transmission volume, security protocol consistency, and historical security event correlation. Since the interaction frequency between devices is evaluated, high-frequency communication may indicate that there is a close business connection between devices, and a large amount of data transmission may indicate that there is important data interaction between devices. Inconsistent security protocols may increase security risks. By evaluating the correlation of historical security events between devices, it is understood that devices with frequent security incidents may require stricter security monitoring. These coefficients can help the system dynamically adjust security policies to ensure that devices with high security correlation coefficients enjoy stricter protection measures. This not only significantly enhances the security, reliability, and transparency of the system, but also improves the efficiency of anomaly detection and response, and realizes intelligent security management.

[0029] In a preferred example of the present application: the security association coefficients between the IoT devices are calculated by combining the coefficients and the preset algorithm, including:

[0030] The security association degree of IoT devices is calculated according to formula (1):

[0031] In the formula, Indicates the security association between device i and device j; represents the communication frequency coefficient between device i and device j; represents the data transmission coefficient between device i and device j; represents the security protocol consistency coefficient between device i and device j; Represents the historical security event correlation coefficient between device i and device j; , , , is the weight coefficient, satisfying .

[0032] By adopting the above technical solution, the security correlation coefficient between devices can be accurately calculated, providing quantitative evaluation results, reducing the error of subjective judgment, and multiple weight coefficients can be adjusted according to the security requirements of actual application scenarios, so that the system can flexibly respond to different security threats. For example, in some scenarios, communication frequency may be more important, while in other scenarios, historical security events may be more influential.

[0033] In a preferred example of the present application: after performing security authentication on each IoT device in the target IoT network according to the optimal security authentication strategy, the method further includes:

[0034] Obtain basic information of the IoT device, and generate a unique device identifier in the IoT system based on the basic information;

[0035] Taking each of the device identifiers as a basic unit, a preliminary security assessment is performed according to a preset security authentication factor to obtain a corresponding security risk level;

[0036] Taking each of the device identifiers as a basic unit, evaluating and analyzing the corresponding optimal security authentication strategy according to the preset security authentication factors, and obtaining a first security authentication result of the IoT device;

[0037] Taking each of the device identifiers as a basic unit, performing real-time security verification according to a preset dynamic verification factor, and generating a dynamic security verification code; executing a corresponding security authentication process based on the dynamic security verification code and security authentication strategy corresponding to each of the device identifiers, and obtaining a second security authentication result of the IoT device;

[0038] An analysis is performed based on the first security authentication result and the second security authentication result of all IoT devices in the target IoT network to obtain a security authentication evaluation report.

[0039] By adopting the above technical solution, a multi-level security assessment is provided, that is, a unique device identifier is generated through basic information, a preliminary security assessment is performed, and then a multi-level security assessment is performed in combination with the best security authentication strategy and dynamic verification factors to ensure the security of each IoT device. At the same time, by generating a dynamic security verification code and performing real-time security verification, the dynamic security and anti-attack capabilities of the system are improved to prevent static authentication information from being cracked. A comprehensive analysis is performed based on the first security authentication result and the second security authentication result to generate a security authentication assessment report to comprehensively evaluate the security status of the entire IoT network. This application is based on detailed historical data and security authentication results. The system can make more scientific decisions and improve the intelligence level of security authentication management. Through continuous analysis and learning, the system can adaptively adjust the security assessment standards to improve the flexibility and adaptability of the system.

[0040] In the second aspect, the invention objective of the present application is achieved by adopting the following technical solutions:

[0041] A blockchain-based Internet of Things network security authentication device, used to execute the blockchain-based Internet of Things network security authentication method as described above, the device comprising:

[0042] A registration module is used to receive a registration request from an IoT device, wherein the registration request includes at least a unique device identifier, a public key, and an initial access timestamp, and determine device registration information of a target IoT network;

[0043] The ledger module is used to establish a distributed ledger based on blockchain, record the device registration information in the ledger distributed throughout the network, and enable all blockchain nodes to hold the latest copy of the ledger;

[0044] A verification module is used to verify the access request according to the consensus algorithm of the blockchain when receiving an access request from a new device, and obtain a consensus algorithm verification result;

[0045] A rights management module, configured to determine whether the access request is approved by a plurality of associated blockchain nodes exceeding a preset number threshold in the blockchain network based on the consensus algorithm verification result; if approved, granting the device corresponding network access rights;

[0046] The audit tracking module is used to deploy an automated audit tracking algorithm, which continuously monitors the network communications and device behaviors of the target IoT network, marks activities that deviate from predefined patterns and automatically triggers alarms; for activities marked as potential threats, an in-depth investigation procedure is initiated until the threat is resolved.

[0047] By adopting the above technical solution, the registration module receives the device's registration request and records the device registration information to ensure that only legal devices can join the Internet of Things network and prevent unauthorized devices from accessing. The ledger module establishes a distributed ledger based on blockchain to ensure the immutability and transparency of the device registration information, thereby improving the security of the system. The verification module uses the consensus algorithm of the blockchain to verify the access request to ensure that only devices recognized by multiple associated blockchain nodes can join the network to prevent the intrusion of malicious devices. The permission management module dynamically adjusts the device's access rights based on the consensus algorithm verification results to ensure that devices with high trust levels enjoy more network resources and devices with low trust levels are subject to stricter access restrictions. The audit tracking module continuously monitors network communications and device behaviors, promptly discovers and marks activities that deviate from predefined patterns, and initiates in-depth investigation procedures until the threat is removed, thereby ensuring the security and stability of the system.

[0048] In a preferred example of the present application: the device further includes:

[0049] Behavior data collection module: used to identify and collect network behavior data of multiple IoT devices according to the historical connection records of each IoT device in the target IoT network, and obtain communication mode information of each IoT device; the communication mode information covers multiple communication phase intervals corresponding to different security levels; the IoT device is associated with a security identifier;

[0050] Security authentication optimization module: used to retrieve the historical security authentication data set of each IoT device based on the security identification, and perform training optimization for each security level in the preset security authentication model to obtain an optimized security authentication model;

[0051] Security association analysis module: used to analyze and record the security association between IoT devices based on the historical connection records of IoT devices, and determine the security association coefficients of all IoT devices;

[0052] Security policy generation module: used to determine the minimum security threshold of each IoT device in each communication phase interval in the optimized security authentication model, combining the security association coefficients of all IoT devices and the security policy adjustment rules corresponding to the historical security authentication data set, and generate the best security authentication strategy;

[0053] Security authentication execution module: used to perform security authentication on each IoT device in the target IoT network according to the optimal security authentication strategy.

[0054] In the third aspect, the invention objective of the present application is achieved by adopting the following technical solutions:

[0055] A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the above-mentioned blockchain-based Internet of Things network security authentication method are implemented.

[0056] In a fourth aspect, the invention objective of the present application is achieved by adopting the following technical solutions:

[0057] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the above-mentioned blockchain-based Internet of Things network security authentication method.

[0058] In summary, the present application includes at least one of the following beneficial technical effects:

[0059] 1. In-depth investigation procedures involve suspending device access rights, forcing re-authentication, or implementing advanced data analysis; This application improves the fault tolerance of the system by establishing a distributed ledger based on blockchain. Even if some nodes fail, the entire system can still operate normally. Through blockchain technology, the latest ledger information can be quickly synchronized to ensure the consistency of all blockchain nodes, which is conducive to improving the reliability and stability of the system. At the same time, the automated audit tracking algorithm continuously monitors the network to provide new and device behaviors, and can promptly discover abnormal activities and abnormal data flows of the target IoT network. For activities that deviate from the predefined pattern, it automatically marks and triggers alarms, prompting the security team to take prompt action. In addition, the formula algorithm can automatically verify the access request of new devices, reducing manual intervention, simplifying the device access process, and improving the efficiency of abnormal detection and response, thereby improving the user experience.

[0060] 2. Identify and collect network behavior data of IoT devices, obtain communication mode information of each device, and divide it into multiple security levels, realizing multi-level security authentication to provide differentiated security protection measures according to the security requirements of different devices. The optimized security authentication model is conducive to improving the accuracy and reliability of security authentication. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 This is a flow chart of a blockchain-based IoT network security authentication method in an embodiment of the present application;

[0062] Figure 2This is another flow chart of a blockchain-based IoT networking security authentication method in an embodiment of the present application;

[0063] Figure 3 It is a schematic diagram of a device in an embodiment of the present application. DETAILED DESCRIPTION

[0064] The present application is further described in detail below in conjunction with the accompanying drawings.

[0065] In one embodiment, if Figure 1 As shown, the present application discloses a blockchain-based IoT network security authentication method, which specifically includes the following steps:

[0066] S1: Receive a registration request from an IoT device, where the registration request contains at least a unique device identifier, a public key, and an initial access timestamp, and determines the device registration information of the target IoT network.

[0067] In this embodiment, the IoT device generates a registration request, which includes a unique device identifier (such as a MAC address, serial number, etc.), a public key (used for encrypted communication), and an initial access timestamp (recording the time when the device first accesses). The registration request is sent to the authentication server through a secure channel (such as HTTPS, TLS, etc.). After receiving the registration request, the authentication server parses the information in the request to verify the integrity and legitimacy of the information.

[0068] S2: Establish a distributed ledger based on blockchain, record the device registration information in the ledger distributed throughout the network, and enable all blockchain nodes to hold the latest copy of the ledger.

[0069] In this embodiment, a decentralized distributed ledger system is constructed through blockchain technology.

[0070] S3: When receiving an access request from a new device, the access request is verified according to the consensus algorithm of the blockchain to obtain a consensus algorithm verification result.

[0071] In this embodiment, the device registration information (device unique identifier, public key and initial access timestamp) is packaged into blocks, and the blocks are added to the ledger through a consensus algorithm (such as PoW, PoS, PBFT, etc.). All blockchain nodes synchronize the latest copy of the ledger through the consensus algorithm to ensure the consistency and integrity of the ledger.

[0072] S4: Based on the consensus algorithm verification result, determine whether the access request is recognized by multiple associated blockchain nodes exceeding a preset number threshold in the blockchain network; if recognized, grant the device corresponding network access rights.

[0073] In this embodiment, a preset number threshold is set, for example, more than 50% of the blockchain nodes need to approve the access request. According to the result of the consensus algorithm, the number of approved blockchain nodes is counted. If the number of approved nodes exceeds the preset threshold, the access request is considered legal, the device is granted corresponding network access rights, the distributed ledger is updated, and the device's access time and access rights are recorded.

[0074] S5: The target IoT network deploys an automated audit tracking algorithm, which continuously monitors the network communications and device behaviors of the target IoT network, marks activities that deviate from predefined patterns and automatically triggers alarms; for activities marked as potential threats, an in-depth investigation procedure is initiated until the threat is resolved.

[0075] In this embodiment, the automated audit tracking algorithm is an audit tracking algorithm that utilizes machine learning (such as decision trees, random forests, and neural networks). By collecting historical behavior data and network communication data of IoT devices to perform data feature advance and model training, an intelligent model is obtained, which can evaluate whether the device behavior conforms to the normal model. The present application can also mark data streams that deviate from predefined patterns and automatically trigger alarms.

[0076] A predefined pattern refers to a series of standards or templates for normal behaviors and data flows that are pre-set during the system design and deployment phase. They are defined based on historical data, expert knowledge, and system requirements. Predefined patterns are used to describe the behaviors and data flows that an IoT system should exhibit under normal conditions. Predefined patterns include communication patterns (including communication frequency, data transmission volume, communication time, and communication protocols), access patterns (including access frequency, access time, and access rights), behavior patterns (command execution methods, execution order, and exception handling methods), and data flow patterns (including the direction of data flow inside and outside the system, data format, and data transmission frequency).

[0077] Specifically, it continuously collects network communication data and device behavior logs, including the source, destination, timestamp and content of data packets, builds a normal behavior model of the device, identifies the normal behavior pattern of the device based on historical data, monitors network communication and device behavior in real time, compares current activities with the behavior model, identifies activities that deviate from predefined patterns, and automatically marks and triggers alarms for activities that deviate from predefined patterns, generating detailed abnormal activity reports; for activities marked as potential threats, initiating in-depth investigation procedures, including suspending access rights to affected devices, forcing reauthentication, or implementing advanced data analysis until the threat is lifted.

[0078] In one embodiment, if Figure 2 As shown, a blockchain-based IoT networking security authentication method also includes:

[0079] S10: Based on the historical connection records of each IoT device in the target IoT network, identify and collect the network behavior data of multiple IoT devices, and obtain the communication mode information of each IoT device; the communication mode information covers multiple communication phase intervals corresponding to different security levels; IoT devices are associated with security identifiers.

[0080] In this embodiment, network behavior data refers to the network communication data of the collected device, including the source, destination, timestamp and content of the data packet; communication mode information refers to analyzing the network behavior data of the device and identifying the communication mode of the device, including communication frequency, data transmission volume, communication time, etc.; communication phase interval refers to dividing the communication process of the device into multiple different phases or time intervals according to the communication behavior and security requirements of the device, each phase interval corresponds to different security levels and security requirements, and the communication phase interval may include initialization phase, data transmission phase and end phase; the security identifier is unique and is used for subsequent security authentication and management.

[0081] S20: Based on the security identifier, retrieve the historical security authentication data set of each IoT device, perform training optimization for each security level in the preset security authentication model, and obtain an optimized security authentication model.

[0082] In this embodiment, the historical security authentication data set includes various data generated by the IoT device during past authentication processes.

[0083] Specifically, the historical security authentication records of the device are retrieved from the database, including authentication time, authentication results, authentication methods, etc. The preset security authentication model can be a rule-based model or a machine learning model; the preset security authentication model is used to train the historical data, adjust the model parameters, optimize the model performance, and ensure the effectiveness and accuracy of the model.

[0084] S30: Analyze and record the security association between the IoT devices based on the historical connection records of the IoT devices, and determine the security association coefficients of all IoT devices.

[0085] In this embodiment, the security association coefficient includes the communication frequency between devices, data transmission volume, security protocol consistency, and historical security event association coefficient.

[0086] Specifically, the security association coefficients between various IoT devices are calculated by combining various coefficients and preset algorithms, including:

[0087] The security association degree of IoT devices is calculated according to formula (1):

[0088] In the formula, Indicates the security association between device i and device j; represents the communication frequency coefficient between device i and device j; represents the data transmission coefficient between device i and device j; represents the security protocol consistency coefficient between device i and device j; Represents the historical security event correlation coefficient between device i and device j; , , , is the weight coefficient, satisfying .

[0089] S40: In the optimized security authentication model, the security association coefficients of all IoT devices and the security policy adjustment rules corresponding to the historical security authentication data set are combined to determine the minimum security threshold of each IoT device in each communication stage interval and generate the best security authentication strategy.

[0090] In this embodiment, the security policy adjustment rules are used to adjust the security policy based on the security association coefficient and historical authentication records of the device; the optimal security authentication policy includes the device's access rights, authentication methods, security thresholds, etc.; the generated optimal security authentication policy is recorded in the database for subsequent security authentication and management.

[0091] S50: Perform security authentication on each IoT device in the target IoT network according to the best security authentication strategy.

[0092] In this embodiment, when a device initiates an access request, the request is verified according to the optimal security authentication strategy to ensure that only legitimate devices can access the network. Based on the authentication result, a decision is made as to whether to grant the device access rights, and the authentication result is fed back to the device. The process and results of each authentication are recorded for auditing and tracking.

[0093] In one embodiment, in step S10, according to the historical connection records of each IoT device in the target IoT network, the network behavior data of multiple IoT devices are identified and collected, and the communication mode information of each IoT device is obtained, which specifically includes:

[0094] S101: According to the historical connection records of each IoT device in the target IoT network, the network communication data and the corresponding security event log of each IoT device are obtained, and multiple historical communication time periods are obtained.

[0095] Specifically, the security event log includes login failures, abnormal access, and attack attempts; parse the log file, extract network communication data and security event logs, and divide the data into multiple historical communication periods according to timestamps, such as one period per hour, day, or week.

[0096] S102: Based on the security identification of the IoT device, the network communication data and the corresponding security event logs of each IoT device in multiple historical communication time periods are classified to obtain multiple historical network behavior data of each IoT device during operation.

[0097] Specifically, the data of each device in different historical communication periods are archived to form a historical network behavior data set.

[0098] S103: Determine different communication mode information of each IoT device based on security event logs in multiple historical network behavior data and preset communication mode determination criteria.

[0099] In this embodiment, the historical network behavior data includes the network communication data of the device and the historical data of the security event log, and the communication mode determination criterion is a preset rule or standard for identifying and classifying the communication mode of the device.

[0100] Specifically, data analysis and machine learning methods are used to identify the communication patterns of devices, such as normal communication, abnormal communication, and high-frequency communication.

[0101] In one embodiment, in step S30, based on the historical connection records of the IoT devices, the security associations between the IoT devices are analyzed and recorded to determine the security association coefficients of all IoT devices, specifically including:

[0102] S301: Based on the historical connection records of IoT devices, the communication frequency between IoT devices, the amount of data for each communication, the type of security protocol used, and the historical security events are extracted, and the communication frequency coefficient, data transmission volume coefficient, security protocol consistency coefficient, and historical security event correlation coefficient between IoT devices are calculated.

[0103] In this embodiment, the security correlation includes the communication frequency between devices, the data transmission volume, the consistency of the security protocol, and the correlation of historical security events.

[0104] S302: The security association coefficients between the IoT devices are calculated by combining the coefficients and the preset algorithm.

[0105] Furthermore, in addition to formula (1), the preset algorithm can also use a weighted average algorithm and a linear combination method for calculation.

[0106] In one embodiment, after step S50, the blockchain-based IoT network security authentication method further includes:

[0107] S501: Obtain basic information of the IoT device, and generate a unique device identifier in the IoT system based on the basic information.

[0108] In this embodiment, the basic information of the device includes the device model, hardware serial number, MAC address, etc.

[0109] Specifically, a hash function is used to generate a unique identifier of the physical network device based on basic information.

[0110] S502: Taking each device identifier as a basic unit, a preliminary security assessment is performed according to a preset security authentication factor to obtain a corresponding security risk level.

[0111] Specifically, the preset security authentication factors include device type, geographic location, access frequency and security protocol; after defining a set of security authentication factors, the security risk level of the IoT device is calculated based on the weight of each security authentication factor and the actual value of the device. The security risk level of the device is calculated based on the weight of each factor and the actual value of the device. For example, the weighted scoring method can be used: device type weight: 0.3, geographic location weight: 0.2, access frequency weight: 0.2, security protocol weight: 0.3; then the risk level is divided according to the total score, such as low risk (0-30 points), medium risk (31-70 points), and high risk (71-100 points).

[0112] S503: Taking each device identifier as a basic unit, evaluating and analyzing the corresponding optimal security authentication strategy according to the preset security authentication factor, and obtaining a first security authentication result of the IoT device.

[0113] Specifically, compare the actual values ​​of the device with the requirements in the best security certification policy, evaluate the compliance of the device, and record the evaluation results, including whether it complies with the policy and the reasons for non-compliance.

[0114] S504: Taking each device identification as a basic unit, perform real-time security verification according to a preset dynamic verification factor and generate a dynamic security verification code; based on the dynamic security verification code and security authentication policy corresponding to each device identification, execute the corresponding security authentication process to obtain a second security authentication result of the IoT device.

[0115] In this embodiment, the dynamic verification factors include timestamp, random number, geographic location change, etc.

[0116] Specifically, a dynamic security verification code is generated based on the dynamic verification factor. When the device initiates an authentication request, the validity of the dynamic security verification code is verified, and the results of the real-time verification are recorded, including whether the verification code is valid and the verification time.

[0117] S505: Analyze the first security authentication results and the second security authentication results of all IoT devices in the target IoT network to obtain a security authentication evaluation report.

[0118] In this embodiment, the security authentication evaluation report includes the overall security status, risk point analysis, and improvement suggestions; the first security authentication result is the result obtained after a static evaluation of the device based on the basic information of the device and the preset security authentication factors, reflecting the security status of the device under static conditions; the second security authentication result is the result obtained after a real-time evaluation of the device based on the real-time data and dynamic verification factors of the device, reflecting the security status of the device at the current moment.

[0119] Specifically, the evaluation time of the first security authentication result is the initial evaluation or periodic evaluation, the dependent data is historical data and static information, and the evaluation content is basic information and security authentication factors; the second security authentication result is each time the device initiates an authentication request, the dependent data is real-time data and dynamic information, and the evaluation content is dynamic verification factors and real-time network behavior; through two levels of security authentication results, the security status of IoT devices can be comprehensively evaluated and managed to ensure the security and reliability of the system.

[0120] Collect the first security authentication results and the second security authentication results from each IoT device, then organize the collected data and output a security authentication evaluation report.

[0121] It should be understood that the serial numbers of the steps in the above embodiments do not imply a sequence of execution. The execution sequence of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0122] In one embodiment, a blockchain-based Internet of Things network security authentication device is provided, and the blockchain-based Internet of Things network security authentication device corresponds to the blockchain-based Internet of Things network security authentication method in the above-mentioned embodiment.

[0123] A blockchain-based IoT networking security authentication device includes a registration module, an account book module, a verification module, a rights management module, and an audit tracking module. The detailed description of each functional module is as follows:

[0124] A registration module is used to receive a registration request from an IoT device, the registration request at least including a unique device identifier, a public key, and an initial access timestamp, and determine the device registration information of the target IoT network;

[0125] The ledger module is used to establish a distributed ledger based on blockchain, record the device registration information in the ledger distributed throughout the network, and enable all blockchain nodes to hold the latest copy of the ledger;

[0126] A verification module is used to verify the access request according to the consensus algorithm of the blockchain when receiving an access request from a new device, and obtain a consensus algorithm verification result;

[0127] The permission management module is used to determine whether the access request is approved by a plurality of associated blockchain nodes exceeding a preset number threshold in the blockchain network based on the consensus algorithm verification result; if approved, the device is granted corresponding network access rights;

[0128] The audit tracking module is used to deploy an automated audit tracking algorithm. The automated audit tracking algorithm continuously monitors the network communications and device behaviors of the target IoT network, marks activities that deviate from predefined patterns and automatically triggers alarms; for activities marked as potential threats, an in-depth investigation procedure is initiated until the threat is eliminated.

[0129] Optionally, the IoT networking security authentication device based on blockchain also includes:

[0130] Behavior data collection module: used to identify and collect network behavior data of multiple IoT devices based on the historical connection records of each IoT device in the target IoT network, and obtain the communication mode information of each IoT device; the communication mode information covers multiple communication phase intervals corresponding to different security levels; IoT device associated security identification;

[0131] Security authentication optimization module: used to retrieve the historical security authentication data set of each IoT device based on the security identification, and perform training optimization for each security level in the preset security authentication model to obtain an optimized security authentication model;

[0132] Security association analysis module: used to analyze and record the security association between IoT devices based on the historical connection records of IoT devices, and determine the security association coefficients of all IoT devices;

[0133] Security policy generation module: used to determine the minimum security threshold of each IoT device in each communication phase interval in the optimized security authentication model, combining the security association coefficients of all IoT devices and the security policy adjustment rules corresponding to the historical security authentication data set, and generate the best security authentication strategy;

[0134] Security authentication execution module: used to perform security authentication on each IoT device in the target IoT network based on the best security authentication strategy.

[0135] For the specific limitations of the blockchain-based Internet of Things network security authentication device, please refer to the limitations of the blockchain-based Internet of Things network security authentication method above, which will not be repeated here; each module in the above-mentioned blockchain-based Internet of Things network security authentication device can be fully or partially implemented through software, hardware and their combination; the above-mentioned modules can be embedded in or independent of the processor in the computer device in hardware form, or can be stored in the memory of the computer device in software form, so that the processor can call and execute the corresponding operations of the above modules.

[0136] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 3 As shown. The computer device includes a processor, a memory, a network interface and a database connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store device registration information, network access rights and automated audit tracking algorithms, etc. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a blockchain-based Internet of Things networking security authentication method is implemented.

[0137] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the following steps when executing the computer program:

[0138] S1: Receive a registration request from an IoT device, where the registration request contains at least a unique device identifier, a public key, and an initial access timestamp, and determine the device registration information of the target IoT network;

[0139] S2: Establish a distributed ledger based on blockchain, record the device registration information in the ledger distributed throughout the network, so that all blockchain nodes have the latest copy of the ledger;

[0140] S3: When receiving an access request from a new device, verify the access request according to the consensus algorithm of the blockchain and obtain the consensus algorithm verification result;

[0141] S4: judging whether the access request is approved by a plurality of associated blockchain nodes exceeding a preset number threshold in the blockchain network based on the consensus algorithm verification result; if approved, granting the device corresponding network access rights;

[0142] S5: The target IoT network deploys an automated audit tracking algorithm, which continuously monitors the network communications and device behaviors of the target IoT network, marks activities that deviate from predefined patterns and automatically triggers alarms; for activities marked as potential threats, an in-depth investigation procedure is initiated until the threat is resolved.

[0143] In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0144] S1: Receive a registration request from an IoT device, where the registration request contains at least a unique device identifier, a public key, and an initial access timestamp, and determine the device registration information of the target IoT network;

[0145] S2: Establish a distributed ledger based on blockchain, record the device registration information in the ledger distributed throughout the network, so that all blockchain nodes have the latest copy of the ledger;

[0146] S3: When receiving an access request from a new device, verify the access request according to the consensus algorithm of the blockchain and obtain the consensus algorithm verification result;

[0147] S4: judging whether the access request is approved by a plurality of associated blockchain nodes exceeding a preset number threshold in the blockchain network based on the consensus algorithm verification result; if approved, granting the device corresponding network access rights;

[0148] S5: The target IoT network deploys an automated audit tracking algorithm, which continuously monitors the network communications and device behaviors of the target IoT network, marks activities that deviate from predefined patterns and automatically triggers alarms; for activities marked as potential threats, an in-depth investigation procedure is initiated until the threat is resolved.

[0149] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0150] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0151] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, it should be understood by those skilled in the art that the technical solutions described in the aforementioned embodiments may still be modified, or some of the features thereof may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A blockchain-based Internet of Things network security authentication method, characterized in that: include: Receive a registration request from an IoT device, the registration request including at least a unique device identifier, a public key, and an initial access timestamp, and determine device registration information of a target IoT network; Establish a distributed ledger based on blockchain, record the device registration information in the ledger distributed throughout the network, so that all blockchain nodes hold the latest copy of the ledger; When receiving an access request from a new device, the access request is verified according to the consensus algorithm of the blockchain to obtain the consensus algorithm verification result; Determining whether the access request is recognized by a plurality of associated blockchain nodes exceeding a preset number threshold in the blockchain network based on the consensus algorithm verification result; If approved, the device is granted corresponding network access rights; The target IoT network deploys an automated audit tracking algorithm, which continuously monitors network communications and device behaviors of the target IoT network, marks activities that deviate from a predefined pattern, and automatically triggers an alarm; for Activities flagged as potential threats will trigger in-depth investigation procedures until the threat is resolved; The method also includes: According to the historical connection records of each IoT device in the target IoT network, the network behavior data of multiple IoT devices are identified and collected to obtain the communication mode information of each IoT device; the communication mode information covers multiple communication phase intervals corresponding to different security levels; the IoT device is associated with a security identifier; Based on the security identifier, retrieve the historical security authentication data set of each IoT device, perform training optimization for each security level in the preset security authentication model, and obtain an optimized security authentication model; Based on the historical connection records of IoT devices, analyze and record the security association between IoT devices and determine the security association coefficient of all IoT devices; In the optimized security authentication model, the security association coefficients of all IoT devices and the security policy adjustment rules corresponding to the historical security authentication data set are combined to determine the minimum security threshold of each IoT device in each communication phase interval and generate the best security authentication strategy; According to the optimal security authentication strategy, security authentication is performed on each IoT device in the target IoT network.

2. According to the blockchain-based Internet of Things network security authentication method of claim 1, it is characterized in that: The step of identifying and collecting network behavior data of multiple IoT devices according to the historical connection records of each IoT device in the target IoT network and obtaining communication mode information of each IoT device specifically includes: According to the historical connection records of each IoT device in the target IoT network, network communication data and corresponding security event logs of each IoT device are obtained, and multiple historical communication time periods are obtained; Based on the security identification of IoT devices, the network communication data and corresponding security event logs of each IoT device in multiple historical communication periods are classified to obtain multiple historical network behavior data of each IoT device during operation; Different communication mode information of each IoT device is determined based on the security event logs in the plurality of historical network behavior data and preset communication mode determination criteria.

3. According to the blockchain-based Internet of Things network security authentication method of claim 1, it is characterized in that: The security correlation includes the communication frequency between devices, data transmission volume, security protocol consistency, and historical security event correlation; The security association coefficients of all IoT devices are determined by analyzing and recording the security association degrees between IoT devices based on the historical connection records of IoT devices, specifically including: Based on the historical connection records of IoT devices, the communication frequency, data volume of each communication, type of security protocol used, and historical security events between IoT devices are extracted, and the communication frequency coefficient, data transmission volume coefficient, security protocol consistency coefficient, and historical security event correlation coefficient between IoT devices are calculated; By combining various coefficients and the preset algorithm, the security association coefficient between each IoT device is calculated.

4. According to claim 3, a blockchain-based Internet of Things network security authentication method is characterized in that: The security association coefficients between the IoT devices are calculated by combining the coefficients and the preset algorithm, including: The security association degree of IoT devices is calculated according to formula (1): S ij =α×F ij +β×D ij +γ×P ij +δ×E ij (1) In the formula, S ij represents the security association between device i and device j; F ij represents the communication frequency coefficient between device i and device j; D ij represents the data transmission coefficient between device i and device j; P ij represents the security protocol consistency coefficient between device i and device j; E ij It represents the historical security event correlation coefficient between device i and device j; α, β, γ, δ are weight coefficients, satisfying α+β+γ+δ=1.

5. According to the blockchain-based Internet of Things network security authentication method of claim 1, it is characterized in that: After performing security authentication on each IoT device in the target IoT network according to the optimal security authentication strategy, the method further includes: Obtain basic information of the IoT device, and generate a unique device identifier in the IoT system based on the basic information; Taking each of the device identifiers as a basic unit, a preliminary security assessment is performed according to a preset security authentication factor to obtain a corresponding security risk level; Taking each of the device identifiers as a basic unit, evaluating and analyzing the corresponding optimal security authentication strategy according to the preset security authentication factors, and obtaining a first security authentication result of the IoT device; Taking each of the device identifiers as a basic unit, performing real-time security verification according to a preset dynamic verification factor, and generating a dynamic security verification code; executing a corresponding security authentication process based on the dynamic security verification code and security authentication strategy corresponding to each of the device identifiers, and obtaining a second security authentication result of the IoT device; An analysis is performed based on the first security authentication result and the second security authentication result of all IoT devices in the target IoT network to obtain a security authentication evaluation report.

6. A blockchain-based IoT networking security authentication device, characterized in that: The device is used to execute a blockchain-based Internet of Things network security authentication method as described in any one of claims 1 to 5, the device comprising: A registration module is used to receive a registration request from an IoT device, wherein the registration request includes at least a unique device identifier, a public key, and an initial access timestamp, and determine device registration information of a target IoT network; The ledger module is used to establish a distributed ledger based on blockchain, record the device registration information in the ledger distributed throughout the network, and enable all blockchain nodes to hold the latest copy of the ledger; A verification module is used to verify the access request according to the consensus algorithm of the blockchain when receiving an access request from a new device, and obtain a consensus algorithm verification result; A rights management module, configured to determine whether the access request is approved by a plurality of associated blockchain nodes exceeding a preset number threshold in the blockchain network based on the consensus algorithm verification result; if approved, granting the device corresponding network access rights; An audit tracking module is used to deploy an automated audit tracking algorithm, which continuously monitors the network communications and device behaviors of the target IoT network, marks activities that deviate from predefined patterns and automatically triggers alarms; for activities marked as potential threats, an in-depth investigation procedure is initiated until the threat is resolved; The device also includes: Behavior data collection module: used to identify and collect network behavior data of multiple IoT devices according to the historical connection records of each IoT device in the target IoT network, and obtain communication mode information of each IoT device; the communication mode information covers multiple communication phase intervals corresponding to different security levels; the IoT device is associated with a security identifier; Security authentication optimization module: used to retrieve the historical security authentication data set of each IoT device based on the security identification, and perform training optimization for each security level in the preset security authentication model to obtain an optimized security authentication model; Security association analysis module: used to analyze and record the security association between IoT devices based on the historical connection records of IoT devices, and determine the security association coefficients of all IoT devices; Security policy generation module: used to determine the minimum security threshold of each IoT device in each communication phase interval in the optimized security authentication model, combining the security association coefficients of all IoT devices and the security policy adjustment rules corresponding to the historical security authentication data set, and generate the best security authentication strategy; Security authentication execution module: used to perform security authentication on each IoT device in the target IoT network according to the optimal security authentication strategy.

7. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of a blockchain-based Internet of Things network security authentication method as described in any one of claims 1 to 5 are implemented.

8. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by the processor, the steps of a blockchain-based Internet of Things network security authentication method as described in any one of claims 1 to 5 are implemented.

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