Method for preventing SQL (Structured Query Language) statement misoperation
By analyzing SQL statements using machine learning and neural network models, high-risk operations can be identified and predicted, solving the problem of the inability of existing technologies to effectively filter and audit SQL statements, and achieving a balance between database security and business efficiency.
Patent Information
- Application Number
- CN202510870576.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-06-26
- Publication Date
- 2025-11-07
Smart Images
Figure CN120910070A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the technical field of database management, and particularly relates to a method for preventing SQL statement misoperation. BACKGROUND
[0002] In the process of database management and operation, SQL (Structured Query Language) statements are used very frequently. However, due to human negligence, business requirement understanding deviation or program error, etc., misoperation of SQL statements often occurs. These misoperations may include misdeletion of key tables, mismodification of a large amount of data, etc., and once they occur, may cause serious consequences such as data loss, business interruption, etc., and bring huge economic losses to enterprises.
[0003] At present, although some database management systems provide basic permission management and log recording functions, for complex SQL statement execution scenarios, there is a lack of intelligent analysis and prediction ability for the influence range of SQL statements. The risk that may be brought by SQL statements cannot be accurately evaluated before execution, and high-risk SQL statements cannot be effectively screened and audited and controlled, so that misoperations (such as misdeletion of key tables, batch data error modification) are difficult to intercept in advance, and the growing demand for database security management cannot be met, and there is a risk of data security. Therefore, there is an urgent need for a more intelligent and effective method to prevent misoperation of SQL statements. SUMMARY
[0004] To solve the above-mentioned prior art problems, the present application provides a method for preventing misoperation of SQL statements.
[0005] A method for preventing misoperation of SQL statements, comprising the following steps:
[0006] SQL statement marking: identifying SQL statements based on machine learning, and marking SQL statements that have potential possibility of misoperation;
[0007] Judging operation type: parsing and analyzing the content of the marked SQL statement, and judging whether the SQL statement is a read operation or a write operation;
[0008] Evaluating influence range: for the SQL statement of write operation, analyzing the data volume of the table involved in the modification by the SQL statement and whether the table is a key table;
[0009] Execution strategy decision: if the data volume of the table involved in the SQL statement exceeds a pre-set threshold or the modification involves a key table, it is determined that the SQL statement is a high-risk SQL statement, and is submitted to a database administrator for auditing; otherwise, it is determined that the SQL statement is a low-risk SQL statement, and an execution prompt is provided.
[0010] Further, the machine learning-based SQL statement identification is performed through a classification model and optimized using a neural network model, specifically including:
[0011] Building a classification model: a supervised learning algorithm is used to build a classification model for classifying input SQL statements as normal operations or high-risk operations (i.e., with potential for misoperation);
[0012] Building a neural network model: analyzing the semantics and context of SQL statements;
[0013] Model compilation: configuring the loss function, optimizer, and evaluation metrics of the model;
[0014] Model training: training the model based on collected SQL statement samples of various types and complexities;
[0015] Model saving and deployment: deploying the model into a server application;
[0016] Real-time inference and labeling: using the deployed model to determine the type of new SQL statements and label them;
[0017] Continuous optimization: collecting and feeding back SQL statements from actual use to the model.
[0018] Further, the data volume scale of the table modified by the SQL statement is analyzed by querying the metadata information of the database to obtain the record count and data space information of the relevant table, and combining the conditions in the SQL statement to estimate the data volume affected by the SQL statement.
[0019] Further, the analysis of whether the table modified by the SQL statement is a key table is performed by pre-setting a key table list based on business requirements and data importance, and checking whether the table involved in the SQL statement is in the key table list.
[0020] Further, the prompt for executing the low-risk SQL statement includes a pop-up window or message notification to prompt the user of the impact of the SQL statement execution.
[0021] Further, the determination of whether the SQL statement is a read operation or a write operation is achieved by identifying keywords in the SQL statement.
[0022] The beneficial effects of the present application are as follows: the present application performs comprehensive risk assessment and screening of SQL statements before execution, effectively identifies high-risk SQL statements and performs mandatory review, avoiding database security problems caused by misoperation; at the same time, it provides execution prompts for low-risk SQL statements, ensuring the security of the database without excessively affecting the efficiency of normal business operations. Attached Figure Description
[0023] Figure 1 This is a flowchart of the method of the present invention.
[0024] Figure 2 Flowchart for optimizing anomaly identification in classification models. Detailed Implementation
[0025] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0026] In this embodiment: Refer to Figure 1 As shown, an embodiment of the present invention provides a method for preventing accidental SQL statement manipulation, comprising:
[0027] SQL statement tagging: Based on machine learning, SQL statements are identified and marked as potentially prone to errors.
[0028] Determine the operation type: Parse and analyze the content of the marked SQL statement to determine whether the SQL statement is a read operation or a write operation;
[0029] Assess the scope of impact: For SQL statements that involve write operations, analyze the scale of data in the tables that the SQL statements modify and whether they are critical tables;
[0030] Execution strategy decision: If the amount of data in the table involved by the SQL statement exceeds the pre-set threshold or involves the modification of a critical table, the SQL statement is determined to be a high-risk SQL statement and submitted to the database administrator for review; otherwise, the SQL statement is determined to be a low-risk SQL statement and execution prompts are provided.
[0031] In this embodiment, an intelligent analysis SQL statement inspection and analysis gateway is deployed between the database server and the application server to ensure that all SQL statements sent from the application server to the database server can be analyzed and processed through the gateway.
[0032] In a preferred embodiment, such as Figure 2 As shown, SQL statements are identified based on machine learning. This is achieved through a classification model, which is then trained and optimized using a neural network model. Specifically, this includes:
[0033] Build a classification model: Use supervised learning algorithms (such as decision trees, support vector machines, etc.) to build a classification model for classifying input SQL statements as normal operations or high-risk operations;
[0034] Build a neural network model: (such as using recurrent neural networks (RNN), long short-term memory networks (LSTM), etc.) to analyze the semantics and context of SQL statements; deep learning models can automatically extract features from SQL statements, making it more accurate to detect misoperations;
[0035] Model compilation: Configure the loss function, optimizer, and evaluation metrics of the model;
[0036] Model training: Train the model based on the collected SQL statement samples covering various types and complexities;
[0037] Model saving and deployment: Deploy the model to the server application;
[0038] Real-time inference and labeling: Use the deployed model to determine the type of new SQL statements and label them;
[0039] Continuous optimization: Collect and feedback SQL statements in actual use to the model. Through the above steps, you can train a neural network model to determine the type of SQL statements and label them. This process needs to be iterated and optimized continuously to ensure the accuracy and reliability of the model in actual application.
[0040] In another preferred embodiment, determine the operation type: Before executing the SQL statement, the gateway parses and analyzes the content of the SQL statement to determine whether it is a read operation (such as SELECT statement) or a write operation (such as INSERT, UPDATE, DELETE statement). This can be achieved by identifying keywords in the SQL statement, for example, if the statement contains the "SELECT" keyword, it is determined to be a read operation; if it contains "INSERT", "UPDATE", or "DELETE" keywords, it is determined to be a write operation. Here is a simple Python example code to determine the operation type of the SQL statement:
[0041] python
[0042] def determine_operation_type(sql):
[0043] sql = sql.strip().upper()
[0044] if sql.startswith("SELECT"):
[0045] return "read" operation
[0046] elif sql.startswith(("INSERT", "UPDATE", "DELETE")):
[0047] return "write" operation
[0048] return "unknown" operation
[0049] # example usage
[0050] sql = "SELECT * FROM users" print(determine_operation_type(sql))
[0051] With the above example code, the operation type of the SQL statement can be simply determined.
[0052] In another preferred embodiment, the impact range is evaluated: for SQL statements of write operations, further analysis is performed on the data volume scale of the modified table involved and whether it is a key table. The gateway interacts with the database to query the metadata information of the relevant table, and combines the conditions in the SQL statement to estimate the data volume scale.
[0053] Data volume scale evaluation: by querying the metadata information of the database, the record number and data storage space of the relevant table are obtained, and combined with the condition judgment in the SQL statement, the data volume that the SQL statement may affect is estimated. For example, for an UPDATE statement, the number of records that meet the conditions in the WHERE clause is estimated.
[0054] Key table determination: a list of key tables in the database is defined in advance, which can be set according to business requirements and data importance. When analyzing the SQL statement, check whether the table involved is in the key table list.
[0055] In another preferred embodiment, the execution strategy decision is made:
[0056] High-risk SQL statement processing: if the SQL statement involves a large number of tables and large data volume, and exceeds the pre-set threshold, or involves modification of a key table, it is determined that the SQL statement is a high-risk SQL statement. For high-risk SQL statements, instead of being executed directly, it is forced to be handed over to the database administrator for review. The database administrator can decide whether to allow the SQL statement to be executed according to the actual situation during the review process.
[0057] Low-risk SQL statement processing: if the SQL statement involves non-critical tables or small data volume, i.e. does not reach the high-risk threshold, an execution prompt is provided. The execution prompt can be presented to the user in the form of a pop-up window, a message notification, etc., informing the user of the possible impact of the execution of the SQL statement and asking the user to confirm whether to continue execution.
[0058] Among them, according to the actual situation of the database and business needs, the threshold of the data volume scale and the list of key tables are set in advance. For example, when the data volume affected by the SQL statement exceeds 1000 records, it is determined to be a large data volume; the tables involving user account information, financial data and other important business data are defined as key tables.
[0059] In the description of the embodiments of the application, the terms "first", "second", "third", "fourth" are only used for description purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features. Therefore, the features defined with "first", "second", "third", "fourth" can be explicitly or implicitly included one or more of the features. In the description of the application, unless otherwise specified, the meaning of "a plurality of" is two or more.
[0060] In the description of the embodiments of the application, the term "and / or" herein only describes the association relationship of the associated objects, which means that there can be three relationships, for example, A and / or B, which can represent the three cases of A alone, A and B together, and B alone. In addition, the character " / " herein generally represents an "or" relationship between the associated objects before and after it.
[0061] Although the embodiments of the application have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, replacements and variations of the embodiments can be made without departing from the principles and spirits of the application, and the scope of the application is defined by the appended claims and their equivalents.
Claims
1. A method for preventing misoperation of an SQL statement, characterized by, The method comprises the following steps: SQL statement marking: identifying SQL statements based on machine learning and marking SQL statements with potential possibility of misoperation; Judging operation type: parsing and analyzing the content of the marked SQL statement to determine whether the SQL statement is a read operation or a write operation; Evaluating the impact range: for the SQL statement of write operation, analyzing the data volume of the table involved in the SQL statement and whether it is a key table; Executing strategy decision: if the data volume of the table involved in the SQL statement exceeds the pre-set threshold or involves modification of a key table, the SQL statement is determined as a high-risk SQL statement and submitted to the database administrator for review; Otherwise, the SQL statement is determined as a low-risk SQL statement and an execution prompt is provided.
2. The method for preventing misoperation of a SQL statement according to claim 1, characterized in that, The method of identifying SQL statements based on machine learning comprises the following steps: Building a classification model: using a supervised learning algorithm to build a classification model for classifying input SQL statements as normal operation or high-risk operation; Building a neural network model: analyzing the semantics and context of SQL statements; Model compilation: configuring the loss function, optimizer and evaluation index of the model; Model training: training the model according to the collected SQL statement samples of various types and complexity; Model saving and deployment: deploying the model to a server application; Real-time inference and labeling: using the deployed model to determine the type and label of new SQL statements; Continuous optimization: collecting and feeding back SQL statements in actual use to the model.
3. The method for preventing misoperation of a SQL statement according to claim 1, characterized in that, The method of analyzing the data volume of the table involved in the SQL statement modification specifically comprises the following steps:
4. The method for preventing misoperation of a SQL statement according to claim 1, characterized in that, Obtaining the record number and data space information of the relevant table by querying the metadata information of the database, and combining the conditions in the SQL statement to estimate the data volume affected by the SQL statement.
5. The method for preventing misoperation of a SQL statement according to claim 1, wherein, The method of analyzing whether the table involved in the SQL statement modification is a key table specifically comprises the following steps:
6. The method for preventing misoperation of a SQL statement according to claim 1, wherein, According to the business requirements and data importance, a list of key tables is pre-set, and when analyzing the SQL statement, it is checked whether the table involved is in the list of key tables. The prompt for executing the low-risk SQL statement comprises: prompting the user of the impact of the SQL statement execution through a pop-up window or a message notification. The method of determining whether the SQL statement is a read operation or a write operation is realized by identifying the keywords in the SQL statement.