Internet of Things data security system based on trust

Abstract

The invention belongs to the technical field of data security, and particularly relates to a trust-based Internet of Things data security system, which comprises a hierarchical trust evaluation module used for converting equipment identity legality, data integrity and behavior compliance into quantifiable trust values through an identity-data-behavior three-dimensional model; and the dynamic trust updating module is used for realizing periodic updating of trust values, linkage authority control of trust levels, and abnormity repair and access blocking of low-trusted equipment according to trust data acquired in real time, and ensuring synchronization of a trust state and an actual safety condition of the equipment. According to the invention, a multi-factor identity authentication mechanism of equipment hardware fingerprint + dynamic token + cloud identity verification is constructed through the hierarchical trust evaluation module, so that equipment identity credible verification is realized, and the risks of identity counterfeiting and certificate leakage are resisted.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data security, in particular to a trust-based Internet of Things data security system. BACKGROUND

[0002] With the wide application of Internet of Things technology in the fields of smart home, industrial control, intelligent medical treatment, etc., the number of networked devices has broken through the level of 10 billion, and the amount of data interaction between devices is growing exponentially. Internet of Things data has real-time and sensitivity characteristics (such as physiological data of patients of medical devices, production control data of industrial devices), and its security is directly related to user privacy, production safety and even public safety.

[0003] Current Internet of Things security protection focuses on single dimensions such as data encryption transmission (such as TLS / SSL protocol) and device identity authentication (such as traditional password and device serial number), and lacks a full-life-cycle protection mechanism based on "trust relationship" - that is, the correlation and linkage of device identity trustworthiness, data integrity trustworthiness and behavior compliance trustworthiness are not considered, resulting in obvious loopholes in security protection. There are the following problems:

[0004] 1. Single identity authentication mechanism, weak trust foundation: existing systems mostly use static identity verification (such as preset password, fixed device certificate), which cannot cope with the risk of device identity impersonation or certificate leakage.

[0005] 2. Data transmission and trust evaluation are disconnected, and are easy to be tampered with or misused: existing solutions only encrypt data transmission, but do not associate data source trustworthiness with data processing authority - even if the data is encrypted, if the source device has been hijacked, the recipient will still decrypt and use the tampered data.

[0006] 3. Trust value is static, cannot adapt to dynamic changes of device behavior: existing systems only evaluate trust value when the device is initially accessed (such as presetting trust level based on device brand and model), and do not monitor subsequent device behavior (such as abnormal data sending frequency, unauthorized access request), resulting in that the device that has been maliciously invaded remains in a trusted state for a long time.

[0007] 4. Lack of cross-domain device trust interconnection, multi-scene collaborative protection fails: the trust evaluation standards of different Internet of Things subsystems (such as lighting system and security system in smart home) are independent, and cannot share device trust status, resulting in security risks being superimposed when cross-system data interaction occurs.

[0008] Based on the above, a trust-based Internet of Things data security system is invented. SUMMARY

[0009] To solve the above technical problems, the present application provides the following technical solutions:

[0010] A trust-based Internet of Things data security system comprises:

[0011] A hierarchical trust evaluation module is configured to convert device identity legitimacy, data integrity and behavior compliance into quantifiable trust values through an identity-data-behavior three-dimensional model;

[0012] A dynamic trust updating module is configured to realize periodic updating of trust values, linkage permission control of trust levels, and abnormal repair and access blocking of low-trust devices according to real-time collected trust data, so as to ensure synchronization of trust states and actual security conditions of devices;

[0013] A trust risk prediction and self-learning optimization module is configured to realize risk early prediction and autonomous optimization of an evaluation model according to historical trust data and abnormal cases, so as to avoid security vulnerabilities caused by sudden reduction of trust values;

[0014] A trust binding encryption transmission module is configured to attach device comprehensive trust values and trust level labels to data encryption;

[0015] A cross-domain trust coordination module is configured to realize desensitization sharing of device trust values and risk prediction results among subsystems through a distributed trust alliance chain; when devices interact across subsystems, a receiver can quickly query cross-domain trust records of the devices, limit operation permissions of high-risk devices in advance, and ensure that there is no dead angle in trust protection under multi-scene coordination;

[0016] A trust visualization and alarm module is configured to display device three-dimensional trust values, comprehensive trust levels and abnormal triggering records in real time; meanwhile, the module is configured to push alarms through multiple channels and provide abnormal traceability suggestions, so as to help administrators quickly locate problems and respond to risks.

[0017] As a preferred scheme of the trust-based Internet of Things data security system, the hierarchical trust evaluation module comprises:

[0018] A three-dimensional evaluation model construction module is configured to construct an identity trust layer-data trust layer-behavior trust layer three-dimensional evaluation model;

[0019] A comprehensive trust value calculation module is configured to calculate a comprehensive trust value, and a formula of the comprehensive trust value is:

[0020] Comprehensive trust value = identity trust value × 40% + data trust value × 35% + behavior trust value × 25%.

[0021] As a preferred scheme of the trust-based Internet of Things data security system, the identity trust layer is generated by multi-factor authentication of device hardware fingerprints, dynamic tokens and cloud identity verification, and an identity trust value of 0-100 points is generated.

[0022] The data trust layer is to check the transmission data by a hash value, and compare the data format, range and historical baseline to generate a data integrity trust value.

[0023] The behavior trust layer is to monitor the device behavior by a machine learning model, and compare the normal behavior feature library to generate a behavior compliance trust value.

[0024] As a preferred scheme of the trust-based Internet of Things data security system, the dynamic trust updating module comprises:

[0025] The real-time updating mechanism module is used to collect the trust value data of each layer once every 5 minutes, and if the trust value of a certain layer decreases by more than 20%, the comprehensive trust value is recalculated;

[0026] The trust level linkage module is used to divide the comprehensive trust value into three levels of high trust, medium trust and low trust, and different levels correspond to different permissions.

[0027] The abnormality repair mechanism module is used to automatically send a trust repair instruction to the device when the device trust value decreases to the low trust level, and gradually restore the trust value after successful repair, and block the network access of the device if the repair fails.

[0028] As a preferred scheme of the trust-based Internet of Things data security system, the trust risk prediction and self-learning optimization module comprises:

[0029] The risk prediction module is used to analyze the historical trust data by a time series algorithm, predict the risk of decrease of the device trust value in advance, and trigger the pre-repair mechanism and administrator warning;

[0030] The self-learning optimization module is used to realize the autonomous iteration of the trust evaluation model through abnormal event feature extraction, scenario-based weight dynamic adjustment and cross-scenario model migration.

[0031] As a preferred scheme of the trust-based Internet of Things data security system, the risk prediction module comprises:

[0032] The analysis module is used to analyze the historical change trend of the three-dimensional trust value of the device by a time series prediction algorithm, and establish a trust value decrease risk prediction model;

[0033] The risk warning module is used to set a risk warning threshold, so that when the decrease amplitude of the trust value of a certain layer in the next 10 minutes is greater than or equal to 15%, the pre-repair mechanism is automatically triggered, a lightweight health check instruction is sent to the device, and a warning notification is pushed to the administrator;

[0034] The risk classification prediction module is used to customize the prediction dimension for different scenarios.

[0035] As a preferred embodiment of the trust-based IoT data security system described in this invention, the self-learning optimization module includes:

[0036] The feedback module is used to establish a feedback mechanism for the trust assessment model, so that after handling abnormal events, the key features of the event can be automatically extracted and updated to the behavioral feature library and prediction model.

[0037] The dynamic adjustment evaluation weight module is used to autonomously optimize the weight ratio of the three-dimensional trust layer according to the changes in security requirements in different scenarios.

[0038] The cross-scenario model migration module is used to migrate the core parameters of a mature trust assessment model for a certain type of device to the same type of device in other scenarios when a mature trust assessment model has been developed in a certain scenario.

[0039] As a preferred embodiment of the trust-based IoT data security system described in this invention, the trust-binding encrypted transmission module includes:

[0040] The transmission module is used to package and transmit encrypted data along with the comprehensive trust value and trust level identifier as trust tags during data transmission.

[0041] The receiving module is used to enable the receiver to first verify the trust label. If the overall trust value is greater than or equal to a preset threshold, the data is then decrypted using a symmetric key. If the trust value is lower than the threshold, the data packet is discarded and an alarm is sent to the trust management module.

[0042] As a preferred embodiment of the trust-based IoT data security system described in this invention, the cross-domain trust collaboration module includes:

[0043] Build a distributed trust consortium blockchain module to enable each IoT subsystem to act as a consortium blockchain node and share the device's identity trust value and historical anomaly records;

[0044] The cross-domain trust verification module is used to allow the receiver to query the cross-domain trust record of the device through the consortium blockchain when the device interacts across subsystems. If there is a historical low-trust record, secondary identity authentication is required.

[0045] As a preferred embodiment of the trust-based IoT data security system described in this invention, the trust visualization and alarm module includes:

[0046] The visualization module provides administrators with a trust value dashboard that displays the three-dimensional trust value, overall trust level, and abnormal trigger records of each device in real time.

[0047] The alarm module is used to send alarms through multiple methods and provide suggestions for anomaly tracing when the device trust value drops sharply or when the low trust level is triggered three times in a row.

[0048] Compared with existing technologies:

[0049] 1. By constructing a multi-factor authentication mechanism that combines device hardware fingerprinting, dynamic tokens, and cloud-based identity verification through a layered trust assessment module, it can achieve trusted device identity verification and resist the risks of identity spoofing and certificate leakage.

[0050] 2. The trusted binding encrypted transmission module uses the device's comprehensive trust value and trust level identifier as trust tags, packages them with encrypted data, and transmits them. The receiver verifies the trust tags first, which enables deep correlation between data transmission and trust assessment, and processes only data from trusted sources to avoid data tampering and abuse.

[0051] 3. The dynamic trust update module collects trust value data at each layer in real time, triggers the recalculation of the comprehensive trust value, and links the trust level permissions and anomaly repair mechanism. It can realize the dynamic adjustment of trust value according to device behavior and ensure that the trust status is synchronized with the actual security situation of the device.

[0052] 4. By building a distributed trust alliance chain through the cross-domain trust collaboration module, the trust status and historical anomaly records of devices in each subsystem are shared, and trust verification is performed when devices interact across subsystems. This enables trust interoperability across IoT subsystems and ensures effective collaborative protection in multiple scenarios. Attached Figure Description

[0053] Figure 1 This is a schematic diagram of the overall framework of the present invention;

[0054] Figure 2 This is a schematic diagram of the hierarchical trust assessment module framework of the present invention;

[0055] Figure 3 This is a schematic diagram of the dynamic trust update module framework of the present invention;

[0056] Figure 4 This is a schematic diagram of the trust risk prediction and self-learning optimization module framework of the present invention;

[0057] Figure 5 This is a schematic diagram of the trust binding encrypted transmission module framework of the present invention;

[0058] Figure 6 This is a schematic diagram of the cross-domain trust collaboration module framework of the present invention;

[0059] Figure 7 This is a schematic diagram of the trust visualization and alarm module framework of the present invention. Detailed Implementation

[0060] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be described in further detail below with reference to the accompanying drawings.

[0061] This invention provides a trust-based Internet of Things (IoT) data security system. Please refer to [link / reference]. Figures 1-7 ,include:

[0062] The layered trust assessment module is used to transform the legality of device identity, data integrity, and behavioral compliance into quantifiable trust values ​​through a three-dimensional model of identity-data-behavior.

[0063] The dynamic trust update module is used to periodically update trust values, link trust level-based access control, and repair and block access of low-trust devices based on real-time collected trust data, ensuring that the trust status is synchronized with the actual security status of the devices.

[0064] The Trust Risk Prediction and Self-Learning Optimization Module is used to predict risks in advance and optimize the assessment model autonomously based on historical trust data and abnormal cases, so as to avoid security vulnerabilities caused by a sudden drop in trust value.

[0065] The trust-binding encrypted transmission module is used to add a comprehensive trust value and trust level label to the device on the basis of data encryption;

[0066] The cross-domain trust collaboration module is used to achieve de-identified sharing of device trust values ​​and risk prediction results among various subsystems through a distributed trust alliance chain. When devices interact across subsystems, the receiving party can quickly query its cross-domain trust records, restrict the operation permissions of high-risk devices in advance, and ensure that trust protection is comprehensive in multi-scenario collaboration.

[0067] The Trust Visualization and Alarm module is used to display the device's three-dimensional trust value, comprehensive trust level, and abnormal trigger records in real time. At the same time, it pushes alarms through multiple channels for abnormal situations and provides suggestions for tracing the source of the anomalies to help administrators quickly locate problems and respond to risks.

[0068] The hierarchical trust assessment module includes:

[0069] A three-dimensional evaluation model module is constructed to build a three-dimensional evaluation model of identity trust layer, data trust layer, and behavior trust layer;

[0070] The identity trust layer generates an identity trust value of 0-100 points through multi-factor authentication using device hardware fingerprints (such as CPU serial number + MAC address), dynamic tokens (updated every 10 minutes), and cloud identity verification (if the hardware fingerprint matches and the dynamic token is valid, the initial identity trust value is 80 points).

[0071] The data trust layer performs hash value verification (such as SHA-256) on the transmitted data and compares the data format, range and historical baseline (such as the normal range of temperature sensor data is -20℃ to 80℃) to generate a data integrity trust value (such as a data trust value of 90 points if the hash value matches and the data is within the baseline).

[0072] The behavior trust layer monitors device behavior (such as data transmission frequency and access permission range) through machine learning models (such as LSTM), compares it with a normal behavior feature library, and generates a behavior compliance trust value (such as a behavior trust value of 85 points if the number of data transmissions fluctuates by ≤10% within 1 hour).

[0073] The module for calculating the overall trust score uses the following formula:

[0074] Overall Trust Score = Identity Trust Score × 40% + Data Trust Score × 35% + Behavioral Trust Score × 25% (weights can be dynamically adjusted according to the scenario).

[0075] The dynamic trust update module includes:

[0076] The real-time update mechanism module collects trust value data for each layer every 5 minutes. If the trust value of a certain layer drops by more than 20% (e.g., the data trust value drops from 90 points to 65 points), the overall trust value is recalculated.

[0077] The trust level linkage module is used to divide the overall trust value into three levels: high trust (80-100 points), medium trust (50-79 points), and low trust (0-49 points). Different levels correspond to different permissions (e.g., high trust devices can transmit sensitive data, while low trust devices are only allowed to transmit public data).

[0078] The anomaly repair mechanism module is used to automatically send trust repair instructions (such as forced firmware update or re-authentication) to the device when the device's trust value drops to a low trust level. If the repair is successful, the trust value will be gradually restored; if the repair fails, its network access will be blocked.

[0079] The trust risk prediction and self-learning optimization module includes:

[0080] The risk prediction module is used to analyze historical trust data through time-series algorithms, predict the risk of a decline in device trust value in advance, and trigger a pre-repair mechanism and administrator alerts.

[0081] The self-learning optimization module is used to achieve autonomous iteration of the trust assessment model through abnormal event feature extraction, dynamic adjustment of scenario-based weights, and cross-scenario model migration.

[0082] The risk prediction module includes:

[0083] The analysis module is used to analyze the historical trend of the device's three-dimensional trust value (identity, data, behavior) using time-series prediction algorithms (such as ARIMA and Prophet) and to establish a risk prediction model for trust value decline.

[0084] The risk warning module is used to set risk warning thresholds. When the predicted trust value of a certain layer drops by ≥15% within the next 10 minutes (e.g., the predicted behavioral trust value drops from 85 to 68), the pre-repair mechanism is automatically triggered. This mechanism sends a lightweight health check instruction to the device (e.g., checking firmware integrity and network connection nodes) and pushes a warning notification to the administrator (e.g., "The ECG monitor's behavioral trust value will drop to the warning line; it is recommended to prioritize investigation").

[0085] The risk classification and prediction module is used to customize prediction dimensions for different scenarios (such as focusing on predicting data trust value risk in industrial scenarios and focusing on predicting behavioral trust value risk in medical scenarios). For example, if an industrial temperature sensor has a history of "network fluctuations 30 minutes before data jumps", the model will use "network fluctuations" as an early warning feature for a decrease in data trust value.

[0086] The self-learning optimization module includes:

[0087] The feedback module is used to establish a trust assessment model feedback mechanism to automatically extract key features of an abnormal event (such as "malware causes the behavioral trust value to drop by 10 points every 5 minutes") after handling an abnormal event (such as restoring trust after a device is hijacked) and update it to the behavioral feature library and prediction model.

[0088] The dynamic adjustment evaluation weight module is used to autonomously optimize the weight ratio of the three-dimensional trust layer according to the changes in security requirements of different scenarios. For example, when "data tampering attacks" frequently occur in smart healthcare scenarios recently, the module automatically increases the weight of the data trust layer from 35% to 45% and decreases the weight of the identity trust layer from 40% to 30% by analyzing abnormal cases in the past 3 months, ensuring that the evaluation model matches the actual risk scenario.

[0089] The cross-scenario model migration module is used to migrate the core parameters of a certain type of device (such as temperature and humidity sensors) to the same type of device in other scenarios (such as industrial scenarios) when a mature trust assessment model has been formed in a certain scenario (such as smart home scenario).

[0090] The trust-binding encrypted transmission module includes:

[0091] The transmission module is used to package and transmit the comprehensive trust value and trust level identifier as trust tags along with the encrypted data (encrypted using the national cryptographic SM4 algorithm) during data transmission.

[0092] The receiving module is used to enable the receiver (device / cloud) to first verify the trust label. If the overall trust value is greater than or equal to a preset threshold (e.g., 70 points in an industrial scenario), the data is then decrypted using a symmetric key. If the trust value is lower than the threshold, the data packet is discarded and an alarm is sent to the trust management module.

[0093] The cross-domain trust collaboration module includes:

[0094] Build a distributed trust alliance chain module to enable various IoT subsystems (such as smart homes, community access control, and medical devices) to act as alliance chain nodes and share the device's identity trust value and historical anomaly records (de-sensitized, retaining only the device's unique identifier and trust level);

[0095] The cross-domain trust verification module is used to query the cross-domain trust records of a device through the consortium blockchain when the device interacts across subsystems (such as when a camera sends a request to an access control system). If there are historical low-trust records, secondary identity authentication is required.

[0096] The trust visualization and alerting module includes:

[0097] The visualization module provides administrators with a trust value dashboard that displays the three-dimensional trust value, overall trust level, and abnormal trigger records of each device in real time.

[0098] The alarm module is used to send alarms via SMS, APP push and other means when the device trust value drops suddenly (e.g., drops by 30 points within 1 minute) or when the low trust level is triggered 3 times in a row, and provides anomaly tracing suggestions (e.g., locating the transmission node of abnormal data and displaying abnormal behavior characteristics).

[0099] In practical use, the specific steps are as follows:

[0100] S1: Through the layered trust assessment module, the identity-data-behavior three-dimensional model is used to transform the device identity legitimacy, data integrity, and behavior compliance into quantifiable trust values;

[0101] S2: Through the dynamic trust update module, based on the real-time collected trust data, the trust value is periodically updated, the trust level is linked to the permission control, and the abnormal repair and access blocking of low-trust devices are implemented to ensure that the trust status is synchronized with the actual security status of the device.

[0102] S3: Through the trust risk prediction and self-learning optimization module, based on historical trust data and abnormal cases, the risk prediction and assessment model can be optimized in advance to avoid security vulnerabilities caused by a sudden drop in trust value.

[0103] S4: By using the trust binding encrypted transmission module, in addition to data encryption, a comprehensive device trust value and trust level label are added;

[0104] S5: Through the cross-domain trust collaboration module, a distributed trust alliance chain is used to achieve de-identified sharing of device trust values ​​and risk prediction results among various subsystems; when devices interact across subsystems, the receiver can quickly query its cross-domain trust records, restrict the operation permissions of high-risk devices in advance, and ensure that trust protection is comprehensive in multi-scenario collaboration.

[0105] S6: The trust visualization and alarm module displays the device's three-dimensional trust value, comprehensive trust level, and abnormal trigger records in real time. At the same time, it pushes alarms through multiple channels for abnormal situations and provides suggestions for tracing the source of abnormalities, helping administrators to quickly locate problems and respond to risks.

[0106] Although the present invention has been described above with reference to embodiments, various modifications can be made and components can be replaced with equivalents without departing from the scope of the invention. In particular, as long as there is no structural conflict, the features in the disclosed embodiments can be combined with each other in any manner. The lack of an exhaustive description of these combinations in this specification is merely for the sake of brevity and resource conservation. Therefore, the present invention is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

Claims

1. A trust-based Internet of Things (IoT) data security system, characterized in that, include: The layered trust assessment module is used to transform the legality of device identity, data integrity, and behavioral compliance into quantifiable trust values ​​through a three-dimensional model of identity-data-behavior. The dynamic trust update module is used to periodically update trust values, link trust level-based access control, and repair and block access of low-trust devices based on real-time collected trust data, ensuring that the trust status is synchronized with the actual security status of the devices. The Trust Risk Prediction and Self-Learning Optimization Module is used to predict risks in advance and optimize the assessment model autonomously based on historical trust data and abnormal cases, so as to avoid security vulnerabilities caused by a sudden drop in trust value. The trust-binding encrypted transmission module is used to add a comprehensive trust value and trust level label to the device on the basis of data encryption; The cross-domain trust collaboration module is used to achieve de-identified sharing of device trust values ​​and risk prediction results among various subsystems through a distributed trust alliance chain. When devices interact across subsystems, the receiving party can quickly query its cross-domain trust records, restrict the operation permissions of high-risk devices in advance, and ensure that trust protection is comprehensive in multi-scenario collaboration. The Trust Visualization and Alarm module is used to display the device's three-dimensional trust value, comprehensive trust level, and abnormal trigger records in real time. At the same time, it pushes alarms through multiple channels for abnormal situations and provides suggestions for tracing the source of the anomalies to help administrators quickly locate problems and respond to risks.

2. The trust-based IoT data security system according to claim 1, characterized in that, The hierarchical trust assessment module includes: A three-dimensional evaluation model module is constructed to build a three-dimensional evaluation model of identity trust layer, data trust layer, and behavior trust layer; The module for calculating the overall trust score uses the following formula: Overall Trust Score = Identity Trust Score × 40% + Data Trust Score × 35% + Behavioral Trust Score × 25%.

3. The trust-based IoT data security system according to claim 2, characterized in that, The identity trust layer generates an identity trust value of 0-100 through multi-factor authentication using device hardware fingerprints, dynamic tokens, and cloud-based identity verification. The data trust layer verifies the hash value of the transmitted data and compares the data format, range, and historical baseline to generate a data integrity trust value. The behavior trust layer monitors device behavior through machine learning models, compares it with a normal behavior feature library, and generates a behavior compliance trust value.

4. The trust-based IoT data security system according to claim 1, characterized in that, The dynamic trust update module includes: The real-time update mechanism module collects trust value data for each layer every 5 minutes. If the trust value of a certain layer drops by more than 20%, the overall trust value is recalculated. The trust level linkage module is used to divide the overall trust value into three levels: high trust, medium trust, and low trust, with different levels corresponding to different permissions; The anomaly repair mechanism module is used to automatically send a trust repair command to the device when the device's trust value drops to a low trust level. If the repair is successful, the trust value will be gradually restored; if the repair fails, the device's network access will be blocked.

5. A trust-based IoT data security system according to claim 1, characterized in that, The trust risk prediction and self-learning optimization module includes: The risk prediction module is used to analyze historical trust data through time-series algorithms, predict the risk of a decline in device trust value in advance, and trigger a pre-repair mechanism and administrator alerts. The self-learning optimization module is used to achieve autonomous iteration of the trust assessment model through abnormal event feature extraction, dynamic adjustment of scenario-based weights, and cross-scenario model migration.

6. A trust-based Internet of Things (IoT) data security system according to claim 5, characterized in that, The risk prediction module includes: The analysis module is used to analyze the historical trend of the device's three-dimensional trust value using a time-series prediction algorithm, and to establish a risk prediction model for the decline in trust value. The risk warning module is used to set risk warning thresholds. When the predicted decrease in the trust value of a certain layer is ≥15% within the next 10 minutes, the pre-repair mechanism is automatically triggered, a lightweight health check instruction is sent to the device, and a warning notification is pushed to the administrator. The risk classification prediction module is used to customize prediction dimensions for different scenarios.

7. A trust-based Internet of Things (IoT) data security system according to claim 6, characterized in that, The self-learning optimization module includes: The feedback module is used to establish a feedback mechanism for the trust assessment model, so that after handling abnormal events, the key features of the event can be automatically extracted and updated to the behavioral feature library and prediction model. The dynamic adjustment evaluation weight module is used to autonomously optimize the weight ratio of the three-dimensional trust layer according to the changes in security requirements in different scenarios. The cross-scenario model migration module is used to migrate the core parameters of a mature trust assessment model for a certain type of device to the same type of device in other scenarios when a mature trust assessment model has been developed in a certain scenario.

8. A trust-based Internet of Things (IoT) data security system according to claim 1, characterized in that, The trust-binding encrypted transmission module includes: The transmission module is used to package and transmit encrypted data along with the comprehensive trust value and trust level identifier as trust tags during data transmission. The receiving module is used to enable the receiver to first verify the trust label. If the overall trust value is greater than or equal to a preset threshold, the data is then decrypted using a symmetric key. If the trust value is lower than the threshold, the data packet is discarded and an alarm is sent to the trust management module.

9. A trust-based Internet of Things (IoT) data security system according to claim 1, characterized in that, The cross-domain trust collaboration module includes: Build a distributed trust consortium blockchain module to enable each IoT subsystem to act as a consortium blockchain node and share the device's identity trust value and historical anomaly records; The cross-domain trust verification module is used to allow the receiver to query the cross-domain trust record of the device through the consortium blockchain when the device interacts across subsystems. If there is a historical low-trust record, secondary identity authentication is required.

10. A trust-based Internet of Things (IoT) data security system according to claim 1, characterized in that, The trust visualization and alerting module includes: The visualization module provides administrators with a trust value dashboard that displays the three-dimensional trust value, overall trust level, and abnormal trigger records of each device in real time. The alarm module is used to send alarms through multiple methods and provide suggestions for anomaly tracing when the device trust value drops sharply or when the low trust level is triggered three times in a row.

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