E-commerce platform information security desensitization scheme analysis system based on artificial intelligence

Abstract

The invention relates to the technical field of data desensitization, in particular to an electronic commerce platform information security desensitization scheme analysis system based on artificial intelligence, which comprises a data classification module, an access evaluation module, a dynamic desensitization module and a security audit module. According to the method, the data of the e-commerce platform is intelligently classified and evaluated by adopting an artificial intelligence technology, dynamic desensitization and finer access control of the data are realized, various data access requirements can be dynamically met by evaluating the sensitivity level of the file and the security level of the access request, data processing is performed according to requirements, and the data processing efficiency is improved. The security and privacy of user data are effectively protected, through automation of security audit, the recognition efficiency of abnormal behaviors is improved, the transparency and traceability of overall data management are enhanced, through real-time analysis and response, the risk of data leakage is reduced, and the practical value of data and the fluency of service operation are kept.

Description

Technical Field

[0001] The present invention relates to the technical field of data desensitization, and in particular to an information security desensitization solution analysis system for an e-commerce platform based on artificial intelligence. Background Art

[0002] The field of data desensitization technology focuses on allowing secure sharing and analysis of data while ensuring data privacy and security. The technology is often applied in environments where sensitive information needs to be protected, such as healthcare, finance, education, and e-commerce. The main methods of data desensitization include data masking, camouflage, randomization, generalization, and deletion of specific information to prevent sensitive data from being maliciously exploited during processing. The core of this technology is to effectively reduce the risk of data leakage without affecting the practicality of the data, so that the data can maintain its value while meeting regulatory requirements and business needs.

[0003] Among them, the e-commerce platform information security desensitization solution analysis system is a data desensitization analysis system designed for e-commerce platforms to improve the security of transaction data and protect user privacy. The main purpose of the system is to analyze and process user data collected by e-commerce platforms, such as purchase history and personal information, and ensure that these data will not leak sensitive information during storage and transmission through desensitization. Therefore, even if the data is accessed without authorization, its content will not expose the user's personal identity information, reducing the risk of data leakage and enhancing data security.

[0004] Traditional desensitizing systems lack sufficient flexibility and precision when dealing with large-scale data and complex data access scenarios. They operate under fixed rules and are difficult to adapt to rapidly changing data access requirements and complex data types, resulting in delays in the processing process or limitations in data use. The audit mechanism in traditional systems is usually a post-processing, which is not sufficient to prevent data leakage incidents. The response time after abnormal behavior occurs is long, which increases data security risks. In the face of modern e-commerce environments with high security and efficient operation requirements, it will lead to inefficient operations and reduced user experience, affecting the company's competitiveness and market reputation. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings existing in the prior art and to propose an information security desensitization solution analysis system for an e-commerce platform based on artificial intelligence.

[0006] In order to achieve the above purpose, the present invention adopts the following technical solution: an e-commerce platform information security desensitization solution analysis system based on artificial intelligence, the system comprising: The data classification module uses AI classification models based on the data information of the e-commerce platform to compare the file characteristics with the known file type characteristics, identify the file type, and evaluate the sensitivity level of the file based on the importance of the file to obtain the sensitivity level information; The access assessment module extracts the source IP, user, user group information and timestamp of the access request based on the file access request, compares it with the normal access request, evaluates the security level required for data access, adjusts the maximum number of rows of data query results according to the security level, and obtains the access control parameters; The dynamic desensitization module evaluates the matching degree of the differentiated desensitization methods based on the sensitivity level information and access control parameters, selects a matching desensitization method, desensitizes the data in the request, desensitizes the data field according to the processing rules, returns the desensitized data to the user, and obtains the data desensitization result; Based on the data desensitization results, the security audit module collects the operation logs of the database, including user login, query, update and deletion behaviors, compares them with the normal operation mode, evaluates the deviation of operation behavior, and combines the timestamp of the operation to evaluate the degree of abnormal behavior, identify security risks, and obtain abnormal behavior information.

[0007] The present invention is improved in that the step of identifying the file type is: Based on the data information of the e-commerce platform, extract the file data from the e-commerce platform, including the text format, size and creation time, and obtain the file feature set; Based on the file feature set, the file features are input into the AI ​​classification model to perform feature extraction and analysis to obtain extracted feature data; Based on the extracted feature data, compared with the known file type features, the formula is: ; Calculate the type similarity score of the file, select the file type with the largest similarity score as the matching file type, and obtain the file type recognition result; in, Represents the similarity score between the file and known file types, represents the kth feature of the file, represents the kth feature of a known file type, represents the weight of the kth feature, represents the standard deviation of feature k, and n represents the total number of features.

[0008] The present invention is improved in that the step of obtaining the sensitivity level information is: Based on the file type identification result, extract file metadata, including author, creation date and access frequency, to obtain file importance data; Based on the file importance data, according to the file type, extract the basic sensitivity index of the file, and extract the file access frequency and the sensitivity level of the file user to obtain basic sensitivity assessment information; Based on the basic sensitivity assessment information, the formula: ; Calculate the sensitivity level score of the file and obtain the sensitivity level information; in, Indicates the sensitivity level score of the file. Indicates the basic sensitivity of the file type. Indicates the access frequency of the file. Indicates the sensitivity of the file user, Indicates the version number of the file. , , , is the weight coefficient.

[0009] The present invention is improved in that the step of evaluating the security level required for data access is: Based on file access requests, we analyze file access logs, collect the IP address, user name, user group, and access timestamp of each request, obtain real-time data of each parameter from the technical log, and obtain access feature data sets; Based on the access feature data set, the collected IP addresses, user names, user groups and access timestamps are cleaned and formatted to optimize data consistency and availability, and the comparative analysis results are obtained by comparing the database query with the historical normal access mode; Based on the comparative analysis results, the formula: ; Calculate the security level required for data access and obtain the security level assessment result; in, Assign a risk score to the IP address, Score the risk of user behavior, Score the overall behavior of the user group, Score the risk of the timestamp, , , , is the weight coefficient, The security level required for data access.

[0010] The present invention is improved in that the steps of obtaining the access control parameters are: Based on the security level assessment result, combined with the preset maximum number of query result rows, the formula is: ; Calculate the maximum number of rows of the adjusted data query results; in, The security level required for data access, The maximum number of rows of the preset query results. is the adjustment factor, The maximum number of rows for the adjusted data query results; Based on the adjusted maximum number of rows of data query results, the adjusted maximum number of rows of data query results is applied to the database query control logic, and the number of data rows returned is limited according to the security level required for data access to obtain access control parameters.

[0011] The present invention is improved in that the steps of obtaining the data desensitization result are: Based on the sensitivity level information and access control parameters, the sensitivity level score of the file is calculated by the formula: ; Calculate the matching score between the desensitization method and the file; in, is the matching score, Indicates the intensity of desensitization, Score the sensitivity level of the file. is the base of natural logarithms; Based on the matching scores of the desensitizing methods and the files and the access control parameters, the desensitizing methods are sorted according to the matching scores, and the desensitizing method with the highest matching score is selected to perform desensitization operations on the data fields of the files. The desensitized data is returned to the user according to the maximum number of rows of the data query results to obtain the data desensitization results.

[0012] The present invention is improved in that the step of evaluating the deviation of the operation behavior is: Based on the data desensitization results, each user's database interaction behavior, including login, query, update and deletion, is automatically collected and recorded to obtain an operation log; Based on the operation log, the operation log is compared with the preset normal operation, and the pattern recognition technology is applied to analyze the frequency and type of each operation to obtain the basic behavior deviation analysis result; Based on the basic behavior deviation analysis results, through the formula: ; Calculate the behavior deviation quantitative value and obtain the behavior deviation evaluation result; in, Quantify the behavioral deviation. For the The actual frequency of the operation, The first The frequency of the operation, The number of monitored operation types.

[0013] The present invention is improved in that the step of obtaining the abnormal behavior information is: Based on the operation log, the timestamp recorded in the operation log is compared with the normal operation time, using the formula: ; Calculate the time deviation of the behavior to obtain time deviation data; in, Represents the actual time of the operation, is the average time of similar operations, Quantify the time deviation; Based on the time deviation data and behavior deviation evaluation results, the formula: ; Calculate the anomaly index for each operation; in, is the abnormal index of the operation, represents the behavioral deviation index, Quantify the time deviation; Based on the abnormal index of each operation, a risk assessment is performed on each operation, risk operations that exceed the normal range are marked, and abnormal behavior information is obtained.

[0014] Compared with the prior art, the advantages and positive effects of the present invention are: In the present invention, artificial intelligence technology is used to intelligently classify and evaluate the data of the e-commerce platform, thereby realizing dynamic desensitization of data and more sophisticated access control. By evaluating the sensitivity level of the file and the security level of the access request, it is possible to dynamically respond to various data access needs and perform data processing on demand, thereby effectively protecting the security and privacy of user data. Through the automation of security audits, the efficiency of identifying abnormal behaviors is improved, the transparency and traceability of overall data management are enhanced, and through real-time analysis and response, the risk of data leakage is reduced, thereby maintaining the practical value of data and the smoothness of business operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 is a system flow chart of the present invention; Figure 2 A flowchart for identifying file types according to the present invention; Figure 3 A flow chart for obtaining sensitivity level information for the present invention; Figure 4 A flow chart for evaluating the security level required for data access for the present invention; Figure 5 A flow chart for obtaining access control parameters for the present invention; Figure 6 A flow chart of obtaining data desensitization results for the present invention; Figure 7 A flow chart for evaluating deviations in operating behavior for the present invention; Figure 8 The present invention is a flow chart for obtaining abnormal behavior information. DETAILED DESCRIPTION

[0016] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0017] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating positions or positional relationships, are based on the positions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, the meaning of "multiple" is two or more, unless otherwise clearly and specifically defined.

[0018] See also Figure 1 The present invention provides a technical solution: an information security desensitization solution analysis system for an e-commerce platform based on artificial intelligence, the system comprising: The data classification module uses AI classification models based on the data information of the e-commerce platform to compare the file characteristics with the known file type characteristics, identify the file type, and evaluate the sensitivity level of the file based on the importance of the file to obtain the sensitivity level information; The access assessment module extracts the source IP, user, user group information and timestamp of the access request based on the file access request, compares it with the normal access request, evaluates the security level required for data access, adjusts the maximum number of rows of data query results according to the security level, and obtains the access control parameters; The dynamic desensitization module evaluates the matching degree of differentiated desensitization methods based on the sensitivity level information and access control parameters, selects the matching desensitization method, desensitizes the data in the request, desensitizes the data field according to the processing rules, returns the desensitized data to the user, and obtains the data desensitization result; Based on the data desensitization results, the security audit module collects the database operation logs, including user login, query, update and deletion behaviors, compares them with the normal operation mode, evaluates the deviation of operation behavior, and combines the timestamp of the operation to evaluate the degree of abnormal behavior, identify security risks, and obtain abnormal behavior information.

[0019] Sensitivity level information includes sensitivity grade, data type information and potential risk information; access control parameters include data access level, row limit strategy and access time window information; data desensitization results include desensitized data fields, desensitization method application information and data access path; abnormal behavior information includes abnormal event type, abnormal access time and associated user identity information.

[0020] See also Figure 2 , the steps to identify the file type are: Based on the data information of the e-commerce platform, extract the file data from the e-commerce platform, including the text format, size and creation time, and obtain the file feature set; When extracting file data from an e-commerce platform, it is necessary to set extraction rules and data features, including file types such as text, images, videos, etc., file size ranges, and file creation time intervals. Use data extraction scripts to regularly extract qualified file data from the database to obtain a specific set of file features. Data is the basis for AI model training and classification. Determining the specific source and type of data can ensure processing accuracy and data reliability.

[0021] Based on the file feature set, the file features are input into the AI ​​classification model for feature extraction and analysis to obtain the extracted feature data; The collected file feature data is input into the AI ​​classification model. The process includes data preprocessing, feature extraction and model feeding. Preprocessing includes data cleaning, format unification, missing value processing, etc. Feature extraction is based on the file metadata such as file size, type, modification date, etc., and key information is extracted from the file features, such as keyword extraction of text files and color and shape analysis of image files. The processed feature data is sent to the trained classification model for category prediction to obtain the extracted feature data.

[0022] Based on the extracted feature data, it is compared with the known file type features through the formula: ; Calculate the type similarity score of the file, select the file type with the largest similarity score as the matching file type, and obtain the file type recognition result; in, Represents the similarity score between the file and known file types, represents the kth feature of the file, represents the kth feature of a known file type, represents the weight of the kth feature, represents the standard deviation of feature k, and n represents the total number of features.

[0023] formula: ; The benefit of the formula is that it adjusts the difference between each file feature and the known file type feature by combining the weight of each feature and the standard deviation through weighted absolute difference, thereby increasing the flexibility and accuracy of the calculation and being suitable for fine identification of file types.

[0024] Detailed explanation of the formula and the process of formula calculation and derivation: For example, there is a file with feature values ​​of [0.5, 1.2, 0.3], the known file type features are [0.4, 1.3, 0.25], the weights are [1, 2, 1.5], and the standard deviations are [0.1, 0.2, 0.15]. The calculation process is as follows: ; The results show that the similarity score between the file and the known file type is 2.5, indicating that the file is close to the known file type to a certain extent. The result is used for further file classification decisions, and the final classification of the file is determined by the similarity score.

[0025] See also Figure 3 , the steps to obtain sensitive level information are: Based on the file type identification results, extract the file metadata, including author, creation date and access frequency, to obtain the file importance data; Based on the file type identification results, combined with the file's metadata, such as author, creation date, access frequency, etc., the data is obtained through statistical data on the storage frequency, modification times, and access frequency of each type of file in the database. The data can be used to analyze the file's activity and the frequency of team collaboration, and combined with the file's content sensitivity score, a reasonable assessment of the file's confidentiality level can be made to obtain the file's importance data.

[0026] Based on the file importance data and file type, the basic sensitivity index of the file is extracted, and the file access frequency and the sensitivity level of the file user are extracted to obtain the basic sensitivity assessment information; Based on the file importance data, the file access frequency and the user's sensitivity level are analyzed to determine the basic sensitivity of the file. The file access frequency is captured by the log analysis system, including the IP address, access time and operation type. The user sensitivity level is assessed by the file user's position, historical file creation records and feedback from team members to obtain basic sensitivity assessment information, which provides basic data for subsequent in-depth analysis of sensitive files.

[0027] Based on the basic sensitivity assessment information, the formula is: ; Calculate the sensitivity level score of the file and obtain the sensitivity level information; in, Indicates the sensitivity level score of the file. Indicates the basic sensitivity of the file type. Indicates the access frequency of the file. Indicates the sensitivity of the file user, Indicates the version number of the file. , , , is the weight coefficient.

[0028] formula: ; The benefit of the formula is that it calculates the sensitivity level of a file by integrating multiple key indicators, making the assessment more accurate and dynamic, and the weight can be adjusted based on real-time data to adapt to different security needs.

[0029] Detailed explanation of the formula and the process of formula calculation and derivation: Set the given parameters as follows: (File type basic sensitivity, a fixed value set according to the file type), (File access frequency, count the number of accesses in the past month), (The sensitivity level of the file user, which is assessed based on the user's position and permissions), (number of file versions, count the number of revisions of the file), , , , (Weight coefficient, determined based on past data analysis).

[0030] Calculation process: ; The results show that the overall sensitivity level of the file is 61.15. This score will be used to determine the security level and access control policy of the file. The higher the value, the more sensitive the file is and the more stringent security measures are required.

[0031] See also Figure 4 , the steps to assess the security level required for data access are: Based on file access requests, we analyze file access logs, collect the IP address, user name, user group, and access timestamp of each request, obtain real-time data of each parameter from the technical log, and obtain access feature data sets; Based on file access requests, file access records are extracted from various log sources such as Web servers, application servers, and database logs, including the IP address, user name, user group, and timestamp of each access. The data is automatically collected through real-time monitoring and log aggregation tools to ensure the timeliness and integrity of the data, and obtain an access feature data set.

[0032] Based on the access feature data set, the collected IP addresses, user names, user groups, and access timestamps are cleaned and formatted to optimize data consistency and availability. The comparative analysis results are obtained by comparing database queries with historical normal access patterns. Based on the access feature data set, a preliminary cleaning process is performed on the extracted data to remove invalid or erroneous log information to ensure the accuracy of subsequent analysis, and the data is formatted, including standardized IP address format, unified user name naming rules and time zone conversion of timestamps. The processing steps ensure the consistency of data between different sources and platforms, laying a solid foundation for comparative analysis. The processed data is compared with the historical normal access pattern. The comparison is performed through pattern recognition and machine learning technology to identify abnormal access behavior and obtain comparative analysis results.

[0033] Based on the comparative analysis results, the formula: ; Calculate the security level required for data access and obtain the security level assessment result; in, Assign a risk score to the IP address, Score the risk of user behavior, Score the overall behavior of the user group, Score the risk of the timestamp, , , , is the weight coefficient, The security level required for data access.

[0034] formula: ; The benefit of the formula is that by parameterizing different security dimensions, it provides a way to quantify security risks, allowing security teams to dynamically adjust security policies based on real-time data, increasing the system's responsiveness and ability to prevent potential threats.

[0035] Detailed explanation of the formula and the process of formula calculation and derivation: Set the parameter value to: , , , ; The weights are set to: , , , .

[0036] Calculation process: ; The results show that the system scores the risk of the current access request as 23.5. The score will be used to decide whether access needs to be restricted or further reviewed. The higher the score, the greater the potential risk of the access request and the more stringent security measures need to be taken.

[0037] See also Figure 5 , the steps to obtain access control parameters are: Based on the security level assessment results and the preset maximum number of query result rows, the formula is: ; Calculate the maximum number of rows of the adjusted data query results; in, The security level required for data access, The maximum number of rows of the preset query results. is the adjustment factor, The maximum number of rows for the adjusted data query results; formula: ; The formula is useful because it introduces an adjustment factor and safety level , making the row limit for data queries more flexible and able to be dynamically adjusted based on real-time assessed security conditions, enhancing the security and adaptability of the system.

[0038] Detailed explanation of the formula and the process of formula calculation and derivation: set up , , .

[0039] but: ; The adjusted maximum number of rows has been slightly reduced to accommodate lower security risks, showing the system's sensitivity to risky access.

[0040] Based on the adjusted maximum number of rows of the data query result, the adjusted maximum number of rows of the data query result is applied to the database query control logic, and the number of rows of data returned is limited according to the security level required for data access, so as to obtain the access control parameter; Based on the adjusted maximum number of rows of data query results, the parameter directly determines the amount of data that users can receive when querying. Through system-level security assessment adjustments, it is ensured that only requests that meet security standards can access more data. The process takes into account multiple factors such as user behavior, access frequency, and timeliness of requests, and incorporates variables into the decision-making model to develop a data access strategy that best meets current security needs.

[0041] See also Figure 6 , the steps to obtain the data desensitization results are: Based on the sensitivity level information and access control parameters, the sensitivity level score of the file is calculated using the formula: ; Calculate the matching score between the desensitization method and the file; in, is the matching score, Indicates the intensity of desensitization, Score the sensitivity level of the file. is the base of natural logarithms; formula: ; The formula uses logistic regression to elegantly handle the relationship between desensitization strength and data sensitivity level. By adjusting the desensitization strength, it can ensure that sensitive data is adequately protected while maintaining maximum data availability. This formula is particularly flexible and adaptable when processing data of different sensitivity levels, ensuring that data processing meets security and compliance requirements.

[0042] Detailed explanation of the formula and the process of formula calculation and derivation: set up: : Indicates that the intensity of the selected desensitization method is higher. : Indicates that the sensitivity level of the file is relatively medium.

[0043] Calculation process: Calculate the exponential part of the formula ; Substitute the calculated results into the logic function: ; Calculate the denominator of the formula: ; Calculate the result of the entire formula: ; The results obtained This means that the currently selected desensitization method is highly consistent with the data sensitivity level and almost achieves the best desensitization effect. This means that implementing this desensitization method can effectively protect data without excessively restricting the use of data.

[0044] Based on the matching scores of the desensitization methods and the files and the access control parameters, the desensitization methods are sorted according to the matching scores, and the desensitization method with the highest matching score is selected to perform desensitization operations on the data fields of the files. The desensitized data is returned to the user according to the maximum number of rows in the data query results to obtain the data desensitization results.

[0045] Based on the matching scores of the desensitization methods and the file and the access control parameters, the desensitization methods are sorted according to the matching scores, and the desensitization method with the highest matching score is selected. After the desensitization method is selected, the desensitization processing of the data field is performed. By applying desensitization rules field by field, such as data masking or data deletion, the corresponding desensitization operations are performed according to the data type and sensitivity level of the field, and the desensitized data is generated. The data is returned to the user according to the maximum number of rows of the data query results, ensuring that the data meets all privacy protection requirements before being returned, and obtaining the data desensitization result.

[0046] See also Figure 7 , the steps to evaluate operational behavior deviations are: Based on the data desensitization results, each user's database interaction behavior, including login, query, update and delete, is automatically collected and recorded to ensure that each user activity is monitored and operation logs are obtained; Based on the data desensitization results, database operation logs are collected, involving records of user login, query, update and deletion behaviors. By capturing basic activities in real time, sufficient log data can be accumulated for subsequent analysis. Data collection includes all interaction information from the server to the database, ensuring that every action is recorded in detail. The comprehensiveness of the data provides a solid foundation for the subsequent comparative analysis, thereby evaluating deviant behaviors that need to be paid attention to.

[0047] Based on the operation log, the frequency and type of each operation are analyzed by comparing it with the preset normal operation, and the basic behavior deviation analysis results are obtained by applying pattern recognition technology; Based on the operation log, the behavior is compared with the preset normal operation mode. The behavior analysis technology is used to standardize various user operations and set the detection threshold of abnormal behavior. All behaviors that exceed the normal mode will be marked and classified by the system. During the analysis process, not only the behavior types are compared, but also the time and frequency of each operation are detailed. It can accurately assess which operations indicate potential risks or misoperations, providing accurate input data for security assessment.

[0048] Based on the basic behavior deviation analysis results, through the formula: ; Calculate the behavior deviation quantitative value and obtain the behavior deviation evaluation result; in, Quantify the behavioral deviation. For the The actual frequency of the operation, The first The frequency of the operation, The number of monitored operation types.

[0049] formula: ; Parameter explanation: : No. The actual frequency of the operation is obtained by monitoring the database log, for example times / hour. : The first in the scheduled operation mode The frequency of the operation is set based on the standard operating procedure document, such as times / hour. : The number of types of operations monitored, read from the system configuration file, for example .

[0050] Detailed explanation of the formula and the process of formula calculation and derivation: The behavior deviation value is calculated by the formula involving the average of the absolute differences between each operation frequency and its standard frequency. For example, if there are three operations, their actual operation frequencies is [5, 7, 2], the standard operating frequency is [3, 5, 1], then: Calculation results , indicating that the average frequency deviation of each operation type is 1.67, which shows that during the inspection period, there is a certain degree of deviation between the actual operation frequency and the predetermined operation mode. This deviation indicator helps the security team quantify the degree of abnormality of operation behavior and assess potential security risks.

[0051] See also Figure 8 ,The steps to obtain abnormal behavior information are: Based on the operation log, the timestamp recorded in the operation log is compared with the normal operation time, using the formula: ; Calculate the time deviation of the behavior to obtain time deviation data; in, Represents the actual time of the operation, is the average time of similar operations, Quantify the time deviation; formula: ; The benefit of the formula is that by calculating the difference between the actual time of an operation and the average time, the time deviation of user behavior can be effectively measured, which is an important indicator for evaluating behavioral anomalies.

[0052] Detailed explanation of the formula and the process of formula calculation and derivation: set up (i.e. 39150 seconds), (i.e. 36630 seconds), ; The results showed a deviation of 2520 seconds from the mean time, indicating a significant time deviation.

[0053] Based on the time deviation data and behavior deviation evaluation results, the formula is: ; Calculate the anomaly index for each operation; in, is the abnormal index of the operation, represents the behavioral deviation index, Quantify the time deviation; formula: ; The formula is useful in that it provides a comprehensive indicator that quantifies abnormal behavior by combining the square root of the sum of the squares of the behavioral deviation and the time deviation.

[0054] Detailed explanation of the formula and the process of formula calculation and derivation: set up ; ; The results show that, considering both behavioral deviation and time deviation, the anomaly index is 2520, which is a relatively high anomaly value, indicating that further safety checks are needed.

[0055] Based on the abnormal index of each operation, a risk assessment is performed on each operation, risk operations that exceed the normal range are marked, and abnormal behavior information is obtained.

[0056] Based on the anomaly index of each operation, after identifying security risks and obtaining abnormal behavior information, the operations that exceed the normal range are analyzed according to the anomaly index, and judgments are made based on the set security thresholds. All abnormal behaviors are marked. The generated abnormal behavior information provides key security warnings, supports real-time security monitoring and subsequent risk management strategy formulation, and ensures the protection of data security and business continuity.

[0057] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. An information security desensitization solution analysis system for e-commerce platforms based on artificial intelligence, characterized by: The system comprises: The data classification module uses AI classification models based on the data information of the e-commerce platform to compare the file characteristics with the known file type characteristics, identify the file type, and evaluate the sensitivity level of the file based on the importance of the file to obtain the sensitivity level information; The access assessment module extracts the source IP, user, user group information and timestamp of the access request based on the file access request, compares it with the normal access request, evaluates the security level required for data access, adjusts the maximum number of rows of data query results according to the security level, and obtains the access control parameters; The dynamic desensitization module evaluates the matching degree of the differentiated desensitization methods based on the sensitivity level information and access control parameters, selects a matching desensitization method, desensitizes the data in the request, desensitizes the data field according to the processing rules, returns the desensitized data to the user, and obtains the data desensitization result; Based on the data desensitization results, the security audit module collects the operation logs of the database, including user login, query, update and deletion behaviors, compares them with the normal operation mode, evaluates the deviation of operation behavior, and combines the timestamp of the operation to evaluate the degree of abnormal behavior, identify security risks, and obtain abnormal behavior information.

2. The information security desensitization solution analysis system for an e-commerce platform based on artificial intelligence according to claim 1 is characterized in that: The steps of identifying the file type are: Based on the data information of the e-commerce platform, extract the file data from the e-commerce platform, including the text format, size and creation time, and obtain the file feature set; Based on the file feature set, the file features are input into the AI ​​classification model to perform feature extraction and analysis to obtain extracted feature data; Based on the extracted feature data, compared with the known file type features, the formula is: ； Calculate the type similarity score of the file, select the file type with the largest similarity score as the matching file type, and obtain the file type recognition result; in, Represents the similarity score between the file and known file types, represents the kth feature of the file, represents the kth feature of a known file type, represents the weight of the kth feature, represents the standard deviation of feature k, and n represents the total number of features.

3. The information security desensitization solution analysis system for an e-commerce platform based on artificial intelligence according to claim 2 is characterized in that: The steps for obtaining the sensitivity level information are as follows: Based on the file type identification result, extract file metadata, including author, creation date and access frequency, to obtain file importance data; Based on the file importance data, according to the file type, extract the basic sensitivity index of the file, and extract the file access frequency and the sensitivity level of the file user to obtain basic sensitivity assessment information; Based on the basic sensitivity assessment information, the formula: ； Calculate the sensitivity level score of the file and obtain the sensitivity level information; in, Indicates the sensitivity level score of the file. Indicates the basic sensitivity of the file type. Indicates the access frequency of the file. Indicates the sensitivity of the file user, Indicates the version number of the file. , , , is the weight coefficient.

4. The information security desensitization solution analysis system for an e-commerce platform based on artificial intelligence according to claim 1 is characterized in that: The steps of evaluating the security level required for data access are: Based on file access requests, we analyze file access logs, collect the IP address, user name, user group, and access timestamp of each request, obtain real-time data of each parameter from the technical log, and obtain access feature data sets; Based on the access feature data set, the collected IP addresses, user names, user groups and access timestamps are cleaned and formatted to optimize data consistency and availability, and the comparative analysis results are obtained by comparing the database query with the historical normal access mode; Based on the comparative analysis results, the formula: ； Calculate the security level required for data access and obtain the security level assessment result; in, Assign a risk score to the IP address, Score the risk of user behavior, Score the overall behavior of the user group, Score the risk of the timestamp, , , , is the weight coefficient, The security level required for data access.

5. The information security desensitization solution analysis system for an e-commerce platform based on artificial intelligence according to claim 4 is characterized in that: The steps for obtaining the access control parameters are: Based on the security level assessment result, combined with the preset maximum number of query result rows, the formula is: ； Calculate the maximum number of rows of the adjusted data query results; in, The security level required for data access, The maximum number of rows of the preset query results. is the adjustment factor, The maximum number of rows for the adjusted data query results; Based on the adjusted maximum number of rows of data query results, the adjusted maximum number of rows of data query results is applied to the database query control logic, and the number of data rows returned is limited according to the security level required for data access to obtain access control parameters.

6. The information security desensitization solution analysis system for e-commerce platforms based on artificial intelligence according to claim 1 is characterized in that: The steps for obtaining the data desensitization results are: Based on the sensitivity level information and access control parameters, the sensitivity level score of the file is calculated by the formula: ； Calculate the matching score between the desensitization method and the file; in, is the matching score, Indicates the intensity of desensitization, Score the sensitivity level of the file. is the base of natural logarithms; Based on the matching scores of the desensitizing methods and the files and the access control parameters, the desensitizing methods are sorted according to the matching scores, and the desensitizing method with the highest matching score is selected to perform desensitization operations on the data fields of the files. The desensitized data is returned to the user according to the maximum number of rows of the data query results to obtain the data desensitization results.

7. The information security desensitization solution analysis system for an e-commerce platform based on artificial intelligence according to claim 1 is characterized in that: The steps of evaluating the deviation of operational behavior are: Based on the data desensitization results, each user's database interaction behavior, including login, query, update and deletion, is automatically collected and recorded to obtain an operation log; Based on the operation log, the operation log is compared with the preset normal operation, and the pattern recognition technology is applied to analyze the frequency and type of each operation to obtain the basic behavior deviation analysis result; Based on the basic behavior deviation analysis results, through the formula: ； Calculate the behavior deviation quantitative value and obtain the behavior deviation evaluation result; in, Quantify the behavior deviation. For the The actual frequency of the operation, The first The frequency of the operation, The number of monitored operation types.

8. The information security desensitization solution analysis system for an e-commerce platform based on artificial intelligence according to claim 7 is characterized in that: The steps for obtaining the abnormal behavior information are: Based on the operation log, the timestamp recorded in the operation log is compared with the normal operation time, using the formula: ； Calculate the time deviation of the behavior to obtain time deviation data; in, Represents the actual time of the operation, is the average time of similar operations, Quantify the time deviation; Based on the time deviation data and behavior deviation evaluation results, the formula: ； Calculate the anomaly index for each operation; in, is the abnormal index of the operation, represents the behavioral deviation index, Quantify the time deviation; Based on the abnormal index of each operation, a risk assessment is performed on each operation, risk operations that exceed the normal range are marked, and abnormal behavior information is obtained.

Images