Information access security system based on iterative trust strategy

Abstract

The present invention discloses an information access security system based on an iterative trust strategy, which relates to the field of information technology. The system includes reading a configuration file and initializing the trust thresholds of different user groups based on historical data; collecting multi-dimensional data of users in real time and performing preprocessing; extracting key features of users from the preprocessed data and constructing a multi-factor trust evaluation model; calculating the user's trust based on the multi-factor trust evaluation model and dynamically adjusting the trust threshold; dynamically adjusting the user's access rights based on the adjusted trust threshold; recording all trust threshold adjustment and access rights change operations and reviewing them through user feedback; calculating the user's trust based on the multi-factor trust evaluation model and dynamically adjusting the trust threshold. The present application can evaluate the user's trust in real time and accurately, improve the real-time and accuracy of the trust evaluation, and can respond to various security threats in a timely manner, thereby improving the security and reliability of the system.

Description

Technical Field

[0001] The present invention relates to the field of information technology, and in particular to an information access security system based on iterative trust strategy. Background Art

[0002] In recent years, with the rapid development of information technology and the popularization of network applications, information security issues have become increasingly prominent. In particular, the security of information access control has become a major research and application topic. Traditional information access control methods mainly rely on static permission management and authentication mechanisms, such as role-based access control (RBAC) and attribute-based access control (ABAC). Although these methods have improved system security to a certain extent, their flexibility and adaptability are insufficient when faced with dynamically changing user behavior and complex network environments. Especially in multi-user, multi-scenario application environments, static access control policies are difficult to effectively respond to ever-changing security threats, making the system vulnerable to attacks and abuse. In addition, traditional access control methods generally lack the ability to deeply analyze user behavior and dynamically adjust, making them unable to promptly detect and respond to potential security risks, which to a certain extent limits the overall security performance of the system.

[0003] Existing information access control technologies have many shortcomings when dealing with dynamically changing user behaviors and complex network environments. First, most existing systems lack the ability to comprehensively monitor and deeply analyze user behaviors. They can often only perform access control based on fixed rules and static user information, and are unable to promptly capture abnormal user behaviors and potential risks. Second, existing trust assessment methods mostly rely on single assessment indicators, such as login frequency and operation records, and lack a multi-dimensional and multi-level comprehensive assessment mechanism, resulting in low accuracy and reliability of trust assessment results. In addition, existing access control policies usually use fixed time intervals or manual triggering when adjusting user permissions, which cannot achieve dynamic and real-time permission adjustments. This is particularly insufficient when responding to sudden security incidents. Summary of the Invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides an information access security system based on an iterative trust policy, which solves the problems of insufficient flexibility and low security caused by the use of static authority management and a single trust evaluation method in existing information access control systems.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, an embodiment of the present invention provides an information access security system based on an iterative trust policy, which includes a configuration management module that reads a configuration file and initializes trust thresholds for different user groups based on historical data;

[0008] Data collection module, collects multi-dimensional data of users in real time and performs pre-processing;

[0009] Feature extraction module, which extracts key features of users from preprocessed data and builds a multi-factor trust evaluation model;

[0010] Trust evaluation module, based on a multi-factor trust evaluation model, calculates the user's trust level and dynamically adjusts the trust threshold;

[0011] The access control module dynamically adjusts the user's access rights based on the adjusted trust threshold;

[0012] The log management module records all trust threshold adjustments and access rights changes, and reviews them based on user feedback.

[0013] As a preferred solution of the information access security system based on iterative trust policy of the present invention, reading the configuration file and initializing the trust thresholds of different user groups according to historical data specifically includes:

[0014] When the system starts, it first reads the various parameters and settings required for initialization in the configuration file;

[0015] Obtaining initial trust thresholds for different user groups from configuration files;

[0016] Initialize trust thresholds for different user groups based on the parsed configuration files.

[0017] As a preferred solution of the information access security system based on iterative trust policy of the present invention, wherein: the multi-dimensional data includes the user's login time, access frequency, operation type, device information, geographic location and social network relationship;

[0018] The preprocessing includes data cleaning and standardization.

[0019] As a preferred solution of the information access security system based on iterative trust policy of the present invention, wherein: the key features include user behavior features, user device security features and user social network features;

[0020] Create a feature list and add the extracted features to the list in sequence to form a feature vector;

[0021] Verify the format of the feature vector and store the feature vector as a user feature dictionary;

[0022] Building a multi-factor trust evaluation model based on extracting key user features specifically includes:

[0023] Get the storage path of the model file of the model path from the configuration file and verify whether the path exists and whether it is a file;

[0024] Define a standard model loading function to load the pre-trained trust evaluation model from the specified path;

[0025] For a successfully loaded model, verify the model type and the validity of the model attributes.

[0026] As a preferred solution of the information access security system based on iterative trust strategy of the present invention, the calculation of the user's trustworthiness based on the multi-factor trust evaluation model specifically includes:

[0027] Select the random forest model, convert the feature vector into a format suitable for model prediction, and use the pre-trained trust evaluation model to predict the original trust score, which is expressed as:

[0028]

[0029] Among them, P i is the predicted original trust score of the i-th user, N is the number of decision trees in the random forest, F i is the feature vector of the i-th user, T j is the jth decision tree, j is the decision tree index;

[0030] Define the trust calculation function, map the predicted original trust score to the model through the sigmoid function, and obtain the user's final trust. The expression is:

[0031]

[0032] Among them, T i is the trust of the i-th user, θ is the steepness parameter, and μ is the mean shift parameter.

[0033] As a preferred solution of the information access security system based on iterative trust policy of the present invention, dynamically adjusting the trust threshold according to the trust calculation result specifically includes:

[0034] Continuously monitor network traffic, user behavior, and system log data to collect real-time security information;

[0035] Integrate real-time threat intelligence systems to obtain the latest threat intelligence information on malicious IP addresses and known attack patterns;

[0036] Use anomaly detection algorithms to calculate anomaly scores based on system data and threat intelligence information to identify potential security threats;

[0037] Calculate a comprehensive threat index based on detected abnormal behaviors and threat intelligence;

[0038] Dynamically adjust the trust threshold based on the comprehensive threat index.

[0039] As a preferred solution of the information access security system based on iterative trust policy of the present invention, dynamically adjusting the user's access rights according to the adjusted trust threshold specifically includes:

[0040] Get the latest trust score and adjusted trust threshold for each user;

[0041] Preliminarily determine the user's access permission level based on the user's latest trust level and the adjusted trust threshold;

[0042] Calculate the access rights adjustment factor for each user to quantify the gap between the user's trust level and the trust threshold;

[0043] The user's access permission level is ultimately determined based on the access permission adjustment factor.

[0044] As a preferred solution of the information access security system based on iterative trust policy of the present invention, recording all trust threshold adjustment and access permission change operations and reviewing them through user feedback specifically includes:

[0045] Obtain the trust threshold before and after adjustment, and record the time and reason for the adjustment;

[0046] Record the trust threshold before and after adjustment, adjustment time, and adjustment reason in the log file;

[0047] Obtain access rights before and after the change, and record the time and reason for the change;

[0048] Record the access rights before and after the change, the change time and the reason for the change in the log file;

[0049] The system collects feedback information submitted by users, including user ID, feedback content, and feedback time;

[0050] Review the feedback to determine whether there is any misjudgment;

[0051] If there is a misjudgment, the system adjusts the trust threshold and access rights of the relevant user and records the adjustment operation.

[0052] The beneficial effects of the present invention are as follows: by reading the configuration file and initializing the trust thresholds of different user groups based on historical data, the system can quickly configure the initial trust environment at startup, ensuring that the system has basic security protection at the initial startup; by collecting the user's multi-dimensional data in real time and preprocessing it, the system can comprehensively and accurately capture the user's dynamic behavior characteristics, and provide high-quality data support for subsequent trust evaluation; by extracting the user's key features from the preprocessed data and constructing a multi-factor trust evaluation model, the system can comprehensively and multi-angle evaluate the user's trust; by calculating the user's trust based on the multi-factor trust evaluation model and dynamically adjusting the trust threshold, the system can evaluate the user's trust in real time and accurately, and dynamically adjust the trust threshold according to the latest security information and threat intelligence, thereby improving the real-time and accuracy of the trust evaluation and significantly improving the security and reliability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0054] Figure 1 This is a flowchart of the information access security system based on the iterative trust policy in Example 1.

[0055] Figure 2 This is a diagram for determining the user's access authority level in Example 1. DETAILED DESCRIPTION

[0056] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0057] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0058] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0059] Example 1, with reference to Figure 1 and Figure 2 , which is the first embodiment of the present invention, provides an information access security system based on an iterative trust policy, comprising the following steps:

[0060] Step 1: Read the configuration file and initialize the trust thresholds of different user groups based on historical data;

[0061] Collect users' multi-dimensional data in real time and perform pre-processing;

[0062] Extract key features of users from preprocessed data and build a multi-factor trust evaluation model;

[0063] Based on a multi-factor trust evaluation model, the user's trust level is calculated and the trust threshold is dynamically adjusted;

[0064] Dynamically adjust user access rights based on the adjusted trust threshold;

[0065] All trust threshold adjustments and access rights changes are recorded and reviewed with user feedback.

[0066] Reading the configuration file and initializing the trust thresholds for different user groups based on historical data specifically includes:

[0067] When the system starts, it first reads the various parameters and settings required for initialization in the configuration file, such as database connection information, log file path, model path, etc., to ensure that the system has basic security protection at the initial startup. The use of configuration files makes system parameters easy to adjust and maintain, and can be flexibly configured according to different application scenarios and needs;

[0068] Obtaining initial trust thresholds for different user groups from configuration files, such as regular employees, senior managers, and technical support personnel, provides a reliable foundation for subsequent dynamic adjustments and avoids security risks caused by improper initial settings.

[0069] Initialize trust thresholds for different user groups based on the parsed configuration files.

[0070] Step 2: Multi-dimensional data includes user login time, access frequency, operation type, device information, geographic location, and social network relationships. This ensures that the system can capture users' dynamic behavior in a timely manner and provide the latest data support for subsequent trust assessments.

[0071] Preprocessing includes data cleaning and standardization.

[0072] Specifically, data cleaning includes removing invalid and abnormal data, and standardization processing includes normalizing numerical data and encoding conversion of categorical data.

[0073] Step 3: Key features include user behavior features, user device security features, and user social network features;

[0074] Create a feature list and add the extracted features to the list in sequence to form a feature vector;

[0075] Verify the format of the feature vector and store the feature vector as a user feature dictionary;

[0076] Building a multi-factor trust evaluation model based on extracting key user features specifically includes:

[0077] Get the storage path of the model file of the model path from the configuration file and verify whether the path exists and whether it is a file;

[0078] If the validation passes, the system will output a message indicating that the model path is valid; otherwise, it will output a message indicating that the model path is invalid and handle the corresponding error. This is done to ensure the correctness and accessibility of the model path and avoid path-related problems during subsequent loading.

[0079] Define a standard model loading function to load the pre-trained trust evaluation model from the specified path;

[0080] If the loading is successful, the system will return the model object; if the loading fails, the system will capture the exception and record the error message, indicating that the loading failed;

[0081] Specific exception handling includes file not found exceptions and other unknown exceptions to ensure the robustness and reliability of the model loading process;

[0082] For a successfully loaded model, verify the model type and the validity of the model attributes.

[0083] If the verification passes, the system will output a message indicating that the model preparation is successful; otherwise, it will output a message indicating that the model preparation has failed and handle the corresponding errors to ensure that the model can be used normally in the trust calculation module.

[0084] Step 4: Calculate the user's trustworthiness based on the multi-factor trust evaluation model. Specifically, the following steps are involved:

[0085] Select the random forest model, convert the feature vector into a format suitable for model prediction, and use the pre-trained trust evaluation model to predict the original trust score, which is expressed as:

[0086]

[0087] Among them, P i is the predicted original trust score of the i-th user, N is the number of decision trees in the random forest, F i is the feature vector of the i-th user, Tj is the jth decision tree, j is the decision tree index;

[0088] Define the trust calculation function, map the predicted original trust score to the model through the sigmoid function, and obtain the user's final trust. The expression is:

[0089]

[0090] Among them, T i is the trust of the i-th user, θ is the steepness parameter, and μ is the mean shift parameter.

[0091] Step 5: Dynamically adjust the trust threshold based on the trust calculation results. Specifically,

[0092] Continuously monitor network traffic, user behavior, and system log data to collect real-time security information and ensure the real-time and dynamic nature of the system;

[0093] Integrate a real-time threat intelligence system to obtain the latest threat intelligence information on malicious IP addresses and known attack patterns, improve the system's threat perception capabilities, and enhance the system's adaptability and security;

[0094] Use anomaly detection algorithms to calculate anomaly scores based on system data and threat intelligence information to identify potential security threats;

[0095] Calculates a comprehensive threat index based on detected abnormal behaviors and threat intelligence, enabling the system to respond to various security threats in a timely manner;

[0096] By dynamically adjusting the trust threshold based on the comprehensive threat index, the system can more accurately identify potential security threats and reduce the possibility of misjudgment and missed judgment.

[0097] Specifically, the expression of the comprehensive threat index is: C = α·A + β·T;

[0098] Among them, C is the comprehensive threat index, A is the abnormal behavior index, and the calculation formula is: a k is the severity of the kth abnormal behavior, K is the number of abnormal behavior categories, T is the threat intelligence index, and the calculation formula is t l is the severity of the lth category of threat intelligence, L is the number of categories of threat intelligence, α is the abnormal behavior weight parameter, and β is the threat intelligence weight parameter.

[0099] The expression for dynamically adjusting the trust threshold is: T th =T base +γ·(CC base );

[0100] Among them, Tth is the adjusted threshold, T base is the initial trust threshold, C base is the baseline comprehensive threat index, and γ is the adjustment coefficient, which is used to control the change range of the trust threshold.

[0101] The value range of the comprehensive threat index C is [0, 1]. The closer the value is to 1, the worse the security status of the system is, and the closer the value is to 0, the better the security status of the system is.

[0102] Adjusted trust threshold T th The value range of is [0, 1]. The closer the value is to 1, the higher the trust threshold is, and the closer the value is to 0, the lower the trust threshold is.

[0103] Step 6: Dynamically adjust the user's access rights based on the adjusted trust threshold. Specifically, the following steps are performed:

[0104] Obtain the latest trust level and adjusted trust threshold of each user to ensure the real-time and accuracy of data;

[0105] Based on the user's latest trust and the adjusted trust threshold, the user's access permission level is preliminarily determined. If T i ≥T th , the user's access rights are "full" (full access); otherwise, the user's access rights are "restricted" (restricted access), ensuring the rationality and security of access rights;

[0106] Calculate the access rights adjustment factor for each user to quantify the gap between the user's trust level and the trust threshold;

[0107] The user's access permission level is ultimately determined based on the access permission adjustment factor.

[0108] The expression of the access rights adjustment factor is:

[0109] Among them, H i The access permission adjustment factor for the i-th user.

[0110] Specifically, by dynamically adjusting access rights, the system can adjust user access rights in real time based on changes in user trust and trust thresholds, improving system flexibility and security. Access rights are adjusted based on user trust and trust thresholds, ensuring system fairness and transparency, and increasing user trust and satisfaction. Dynamically adjusting access rights can promptly restrict potentially high-risk users, reducing security risks and improving the overall security of the system.

[0111] Step 7: Record all trust threshold adjustments and access rights changes, and review them based on user feedback. This includes:

[0112] Obtain the trust threshold before and after adjustment, and record the time and reason for the adjustment to ensure the traceability of the operation and improve user trust and satisfaction;

[0113] Record the trust threshold before and after adjustment, adjustment time, and adjustment reason in the log file;

[0114] Obtain access rights before and after the change, and record the time and reason for the change;

[0115] Record the access rights before and after the change, the change time and the reason for the change in the log file;

[0116] The system collects feedback information submitted by users, including user ID, feedback content, and feedback time;

[0117] Review the feedback to determine whether there is any misjudgment;

[0118] If there is a misjudgment, the system adjusts the trust threshold and access rights of the relevant user and records the adjustment operation.

[0119] This embodiment also provides a computer device, which is suitable for the information access security system based on an iterative trust policy, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement the information access security system based on an iterative trust policy proposed in the above embodiment.

[0120] The computer device may be a terminal, comprising a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device comprises a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner may be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a button, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse.

[0121] This embodiment also provides a storage medium having a computer program stored thereon, which, when executed by a processor, implements the information access security system based on the iterative trust policy proposed in the above embodiment; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, disk or optical disk.

[0122] In summary, the present invention reads the configuration file and initializes the trust thresholds of different user groups based on historical data, so that the system can quickly configure the initial trust environment at startup, ensuring that the system has basic security protection at the initial startup; by collecting multi-dimensional data of users in real time and preprocessing it, the system can comprehensively and accurately capture the dynamic behavior characteristics of users, and provide high-quality data support for subsequent trust evaluation; by extracting the key features of users from the preprocessed data and constructing a multi-factor trust evaluation model, the system can comprehensively and multi-angle evaluate the trust of users; by calculating the trust of users based on the multi-factor trust evaluation model and dynamically adjusting the trust threshold, the system can evaluate the trust of users in real time and accurately, and dynamically adjust the trust threshold according to the latest security information and threat intelligence, thereby improving the real-time and accuracy of trust evaluation and significantly improving the security and reliability of the system.

[0123] Example 2, referring to Table 1, is the second embodiment of the present invention. To further verify the technical solution of the present invention, experimental simulation data of an information access security system based on an iterative trust strategy is provided.

[0124] A detailed experiment was conducted within the internal network environment of a large enterprise. The experimental subjects included different user groups, such as regular employees, senior management, and technical support personnel. The primary goal of the experiment was to evaluate the system's performance in dynamically adjusting trust thresholds and access permissions, particularly its ability to cope with complex network environments and potential security threats.

[0125] When the system starts, it first reads the various parameters and settings required for initialization in the configuration file, including the initial trust thresholds for different user groups.

[0126] The initial trust thresholds for different user groups are obtained from the configuration file, and the trust thresholds for different user groups are initialized based on the parsed configuration file. For example, the initial trust threshold for ordinary employees is set to 0.5, the initial trust threshold for senior managers is set to 0.7, and the initial trust threshold for technical support personnel is set to 0.6.

[0127] Collect multi-dimensional data of users in real time, including login time, access frequency, operation type, device information, geographic location and social network relationships.

[0128] Preprocess the collected data, including data cleaning and standardization, to ensure data consistency and availability.

[0129] Extract key features of users from the preprocessed data, including user behavioral features, device security features, and social network features.

[0130] Create a feature list, add the extracted features to the list in sequence to form a feature vector, and verify the format of the feature vector.

[0131] The feature vectors are stored as user feature dictionaries, and a multi-factor trust evaluation model is constructed based on the extracted key features of users.

[0132] Get the storage path of the model file from the configuration file and verify whether the path exists and is a file.

[0133] Define a standard model loading function to load a pre-trained trust assessment model from a specified path and verify the model type and the validity of the model's properties.

[0134] Select the random forest model, convert the feature vector into a format suitable for model prediction, and use the pre-trained trust assessment model to predict the raw trust score.

[0135] Define the trust calculation function, map the predicted original trust score to the model through the sigmoid function, and obtain the user's final trust.

[0136] Continuously monitor network traffic, user behavior, and system log data to collect real-time security information and integrate with real-time threat intelligence systems to obtain the latest threat intelligence information on malicious IP addresses and known attack patterns.

[0137] Use anomaly detection algorithms to calculate anomaly scores based on system data and threat intelligence information to identify potential security threats. Based on detected abnormal behavior and threat intelligence, calculate a comprehensive threat index and dynamically adjust the trust threshold.

[0138] Obtain the latest trust level and adjusted trust threshold of each user, and preliminarily determine the user's access permission level based on the user's latest trust level and adjusted trust threshold.

[0139] Calculate each user's access rights adjustment factor to quantify the gap between the user's trust level and the trust threshold, and ultimately determine the user's access rights level based on the access rights adjustment factor.

[0140] All trust threshold adjustments and access rights changes are recorded and reviewed through user feedback to ensure the accuracy and reliability of the system.

[0141] The details are shown in Table 1 below:

[0142] Table 1 Experimental data record table

[0143]

[0144] As can be seen from the table, the initial trust threshold for ordinary employees is 0.5. After the system's multi-factor trust evaluation, the final trust levels are 0.75 and 0.65, respectively. This demonstrates that the system can dynamically increase users' trust levels based on their actual behavior and characteristics, reflecting its flexibility and accuracy.

[0145] The initial trust threshold of senior managers is 0.7, and the final trust levels are 0.85 and 0.80 respectively, which also shows the dynamic adjustment ability of the system.

[0146] The initial trust threshold of the technical support staff was 0.6, and the final trust levels were 0.70 and 0.65, respectively, which further verified the effectiveness of the system.

[0147] The access rights adjustment factor reflects the gap between a user's trust level and the trust threshold. The access rights adjustment factors for ordinary employees are 1.2 and 1.1, respectively. This indicates that the system dynamically adjusts access rights based on user trust levels, improving system security and flexibility.

[0148] The access rights adjustment factors for senior managers are 1.3 and 1.2, respectively, further demonstrating the effectiveness and accuracy of the system among the advanced user groups.

[0149] The access rights adjustment factors for technical support personnel are 1.1 and 1.0, respectively, indicating that the system's adjustment strategy for different user groups is reasonable and effective.

[0150] Login time, access frequency, and anomaly score are important factors affecting user trust. As can be seen from the table, ordinary employees have earlier login times, higher access frequencies, lower anomaly scores, and ultimately higher trust.

[0151] Senior managers have earlier login times, higher access frequencies, moderate anomaly scores, and ultimately higher trust levels.

[0152] Technical support personnel have late login times, moderate access frequencies, low anomaly scores, and moderate final trust levels.

[0153] In summary, the present invention significantly improves the security and adaptability of the information access control system through innovative designs in many aspects, such as dynamic adjustment of trust thresholds, multi-factor trust assessment, real-time threat intelligence integration, dynamic adjustment of access rights, log management and user feedback, and effectively solves the shortcomings of the existing technology.

[0154] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. An information access security system based on an iterative trust policy, characterized by: include, Configuration management module, which reads configuration files and initializes trust thresholds for different user groups based on historical data; Data collection module, collects multi-dimensional data of users in real time and performs pre-processing; Feature extraction module, which extracts key features of users from preprocessed data and builds a multi-factor trust evaluation model; Trust evaluation module, based on a multi-factor trust evaluation model, calculates the user's trust level and dynamically adjusts the trust threshold; The access control module dynamically adjusts the user's access rights based on the adjusted trust threshold; Log management module, which records all trust threshold adjustments and access rights changes and reviews them based on user feedback; The dynamic adjustment of the trust threshold is as follows: Continuously monitor network traffic, user behavior, and system log data to collect real-time security information; Integrate real-time threat intelligence systems to obtain the latest threat intelligence information on malicious IP addresses and known attack patterns; Use anomaly detection algorithms to calculate anomaly scores based on system data and threat intelligence information to identify potential security threats; Calculate a comprehensive threat index based on detected abnormal behaviors and threat intelligence; Dynamically adjust the trust threshold based on the comprehensive threat index; The expression of the comprehensive threat index is: C = α·A + β·T; Among them, C is the comprehensive threat index, A is the abnormal behavior index, and the calculation formula is: a k is the severity of the kth abnormal behavior, K is the number of abnormal behavior categories, T is the threat intelligence index, and the calculation formula is t l is the severity of the lth category of threat intelligence, L is the number of categories of threat intelligence, α is the abnormal behavior weight parameter, and β is the threat intelligence weight parameter; The expression for dynamically adjusting the trust threshold is: T th =T base +γ·(CC base ); Among them, T th is the adjusted threshold, T base is the initial trust threshold, C base is the baseline comprehensive threat index, and γ is the adjustment coefficient, which is used to control the change range of the trust threshold.

2. The information access security system based on iterative trust policy according to claim 1, characterized in that: Read the configuration file and initialize the trust thresholds for different user groups based on historical data, as follows: When the system starts, it first reads the various parameters and settings required for initialization in the configuration file; Obtaining initial trust thresholds for different user groups from configuration files; Initialize trust thresholds for different user groups based on the parsed configuration files.

3. The information access security system based on iterative trust policy according to claim 2, characterized in that: The multi-dimensional data includes the user's login time, access frequency, operation type, device information, geographic location and social network relationships; The preprocessing includes data cleaning and standardization.

4. The information access security system based on iterative trust policy according to claim 3, characterized in that: The key features include user behavior features, user device security features, and user social network features, as follows: Create a feature list and add the extracted features to the list in sequence to form a feature vector; Verify the format of the feature vector and store the feature vector as a user feature dictionary; The multi-factor trust evaluation model is constructed based on the extraction of key features of users: Get the storage path of the model file of the model path from the configuration file and verify whether the path exists and whether it is a file; Define a standard model loading function to load the pre-trained trust evaluation model from the specified path; For a successfully loaded model, verify the model type and the validity of the model attributes.

5. The information access security system based on iterative trust policy according to claim 4, characterized in that: Based on the multi-factor trust evaluation model, the user's trust level is calculated as follows: Select the random forest model, convert the feature vector into a format suitable for model prediction, and use the pre-trained trust evaluation model to predict the original trust score, which is expressed as: Among them, P i is the predicted original trust score of the i-th user, N is the number of decision trees in the random forest, F i is the feature vector of the i-th user, T j is the jth decision tree, j is the decision tree index; Define the trust calculation function, map the predicted original trust score to the model through the sigmoid function, and obtain the user's final trust. The expression is: Among them, T i is the trust of the i-th user, θ is the steepness parameter, and μ is the mean shift parameter.

6. The information access security system based on iterative trust policy according to claim 5, characterized in that: Based on the adjusted trust threshold, the user's access rights are dynamically adjusted as follows: Get the latest trust score and adjusted trust threshold for each user; Preliminarily determine the user's access permission level based on the user's latest trust level and the adjusted trust threshold; Calculate the access rights adjustment factor for each user to quantify the gap between the user's trust level and the trust threshold; The user's access permission level is ultimately determined based on the access permission adjustment factor.

7. The information access security system based on iterative trust policy according to claim 6, characterized in that: All trust threshold adjustments and access rights changes are recorded and reviewed with user feedback, specifically: Obtain the trust threshold before and after adjustment, and record the time and reason for the adjustment; Record the trust threshold before and after adjustment, adjustment time, and adjustment reason in the log file; Obtain access rights before and after the change, and record the time and reason for the change; Record the access rights before and after the change, the change time and the reason for the change in the log file; The system collects feedback information submitted by users, including user ID, feedback content, and feedback time; Review the feedback to determine whether there is any misjudgment; If there is a misjudgment, the system adjusts the trust threshold and access rights of the relevant user and records the adjustment operation.

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