Database security protection method and system based on artificial intelligence

Abstract

The invention discloses a database security protection method and system based on artificial intelligence, and belongs to the technical field of database management, and the method comprises the following steps: 1) data collection: collecting logs and other related data from a database server; 2) behavior analysis: analyzing the collected data to identify normal and abnormal database access modes; 3) threat detection: identifying and classifying security threats; and 4) a response module which acts on the detected security threats. According to the method, the activity of the database can be monitored in real time, security threats can be accurately identified, and corresponding protection measures are taken to protect the database from being attacked.

Description

Technical Field

[0001] The present invention relates to the technical field of database management, and in particular to an artificial intelligence-based database security protection method and system. Background Art

[0002] With the rapid development of information technology, database systems have become an indispensable part of enterprises and organizations for storing and managing large amounts of data. However, the complexity of database systems and the increase in data volume make the systems more susceptible to abnormal behaviors and failures. Traditional anomaly detection methods, such as rule-based systems and statistical methods, often have difficulty adapting to rapidly changing environments and complex data patterns. Therefore, a more advanced technology is needed to improve the accuracy and efficiency of database anomaly detection. Summary of the invention

[0003] The technical task of the present invention is to address the above shortcomings and provide an artificial intelligence-based database security protection method and system that can monitor database activities in real time, accurately identify security threats, and take corresponding protective measures to protect the database from attacks.

[0004] The technical solution adopted by the present invention to solve its technical problem is:

[0005] A database security protection method based on artificial intelligence, the implementation of which includes the following steps:

[0006] 1) Data collection: Collect logs and other relevant data from the database server;

[0007] 2) Behavioral analysis: Analyze the collected data to identify normal and abnormal database access patterns;

[0008] 3) Threat detection: identifying and classifying security threats;

[0009] 4) Response module: Take actions on detected security threats.

[0010] Furthermore, the data collection specifically includes:

[0011] Collect operation logs from the database server through the log collector;

[0012] Monitor database performance indicators through performance monitor;

[0013] Monitor the database server's network traffic with a network monitor to detect unusual traffic patterns.

[0014] Furthermore, the performance indicators of the database include response time and query delay.

[0015] Furthermore, the behavior analysis specifically includes:

[0016] Analyze users’ access patterns, including access frequency and query types, through user behavior analyzers;

[0017] Analyze the structure and content of SQL queries through query analyzer to identify potential SQL injection attacks;

[0018] Detect anomalous behavior using statistical methods and machine learning models through anomaly detectors.

[0019] Furthermore, the threat detection specifically includes:

[0020] Through the deep learning classifier, the output of the behavior analysis module is classified using a deep learning model to identify security threats;

[0021] Analyze natural language queries through natural language processing engines to identify potential phishing attacks;

[0022] Integrate external threat intelligence through the threat intelligence integrator to identify known security threats.

[0023] Furthermore, the implementation of the deep learning classifier includes:

[0024] Data preprocessing: Clean and format the collected logs and network traffic data for subsequent processing;

[0025] Feature engineering: extract useful features from preprocessed data, including user access frequency, query complexity, and network traffic patterns;

[0026] Model training: Use annotated security threat data to train deep learning models, such as convolutional neural networks (CNNs) or recurrent neural networks (RNNs);

[0027] Model evaluation: Evaluate the performance of the model through methods such as cross-validation and perform optimization;

[0028] Model deployment: The trained model is deployed to the production environment to monitor database activities in real time and detect security threats.

[0029] Furthermore, the response module specifically includes:

[0030] Generate alerts when security threats are detected via an alert generator;

[0031] Automatically block malicious access and attacks through automatic blockers;

[0032] With the Forensic Analyzer, collect and analyze attack evidence to support subsequent investigation and response.

[0033] The present invention also claims protection for a database security protection system based on artificial intelligence, the system comprising:

[0034] A data collection module, used to collect logs and other relevant data from the database server;

[0035] A behavioral analysis module that analyzes the collected data to identify normal and abnormal database access patterns;

[0036] Threat detection module, used to identify and classify security threats;

[0037] A response module to take action on detected security threats;

[0038] The system specifically implements database security protection through the above-mentioned artificial intelligence-based database security protection method.

[0039] The present invention also claims protection for an artificial intelligence-based database security protection implementation device, comprising: at least one memory and at least one processor;

[0040] The at least one memory is used to store a machine-readable program;

[0041] The at least one processor is used to call the machine-readable program to implement the above method.

[0042] The present invention also claims protection for a computer-readable medium having computer instructions stored thereon, which implement the above method when executed by a processor.

[0043] Compared with the prior art, the database security protection method and system based on artificial intelligence of the present invention have the following beneficial effects:

[0044] The present invention enhances the security of databases through advanced machine learning technology, can monitor database activities in real time, identify and prevent potential security threats, including unauthorized access, SQL injection attacks and other malicious behaviors. By using deep learning and natural language processing technology, it can adaptively learn normal and abnormal database access patterns, thereby improving detection accuracy and reducing false positives.

[0045] The method and system have beneficial effects in database anomaly detection, such as improving detection accuracy, reducing false alarms, real-time monitoring, adaptive learning, and reducing maintenance costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 It is a schematic diagram showing the principle of the database security protection method based on artificial intelligence provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0047] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.

[0048] The embodiment of the present invention provides a database security protection method based on artificial intelligence, and the implementation of the method includes the following steps:

[0049] 1. Data collection: Collect logs and other relevant data from the database server;

[0050] 2. Behavioral analysis: Analyze the collected data to identify normal and abnormal database access patterns;

[0051] 3. Threat detection: identifying and classifying security threats;

[0052] 4. Response module: Take action on detected security threats.

[0053] The data collection specifically includes:

[0054] Collect operation logs from the database server through the log collector;

[0055] Monitor database performance indicators such as response time and query latency through performance monitor. ;

[0056] Monitor the database server's network traffic with a network monitor to detect unusual traffic patterns.

[0057] The behavior analysis specifically includes:

[0058] Analyze users’ access patterns, including access frequency and query types, through user behavior analyzers;

[0059] Analyze the structure and content of SQL queries through query analyzer to identify potential SQL injection attacks;

[0060] Detect anomalous behavior using statistical methods and machine learning models through anomaly detectors.

[0061] The threat detection specifically includes:

[0062] Through the deep learning classifier, the output of the behavior analysis module is classified using a deep learning model to identify security threats;

[0063] Analyze natural language queries through natural language processing engines to identify potential phishing attacks;

[0064] Integrate external threat intelligence through the threat intelligence integrator to identify known security threats.

[0065] Wherein, the implementation of the deep learning classifier includes:

[0066] Data preprocessing: Clean and format the collected logs and network traffic data for subsequent processing;

[0067] Feature engineering: extract useful features from preprocessed data, including user access frequency, query complexity, and network traffic patterns;

[0068] Model training: Use annotated security threat data to train deep learning models, such as convolutional neural networks (CNNs) or recurrent neural networks (RNNs);

[0069] Model evaluation: Evaluate the performance of the model through methods such as cross-validation and perform optimization;

[0070] Model deployment: The trained model is deployed to the production environment to monitor database activities in real time and detect security threats.

[0071] The response module specifically includes:

[0072] Generate alerts when security threats are detected via an alert generator;

[0073] Automatically block malicious access and attacks through automatic blockers;

[0074] With the Forensic Analyzer, collect and analyze attack evidence to support subsequent investigation and response.

[0075] This method uses advanced machine learning technology to enhance database security, monitor database activities in real time, and identify and prevent potential security threats, including unauthorized access, SQL injection attacks, and other malicious behaviors. By using deep learning and natural language processing technology, it can adaptively learn normal and abnormal database access patterns, thereby improving detection accuracy and reducing false positives.

[0076] An embodiment of the present invention also provides an artificial intelligence-based database security protection system, which implements database security protection through the artificial intelligence-based database security protection method described in the above embodiment.

[0077] The system includes:

[0078] 1. Data collection module.

[0079] The data collection module is responsible for collecting logs and other related data from the database server, which will be used for subsequent security analysis. This module includes the following submodules:

[0080] Log Collector: Collects operation logs from the database server.

[0081] Performance Monitor: Monitors database performance metrics such as response time and query latency.

[0082] Network Monitor: Monitors network traffic to database servers to detect unusual traffic patterns.

[0083] 2. Behavior analysis module.

[0084] The behavior analysis module is responsible for analyzing the collected data to identify normal and abnormal database access patterns. This module includes the following submodules:

[0085] User Behavior Analyzer: Analyzes users’ access patterns, including access frequency and query types.

[0086] Query Analyzer: Analyzes the structure and content of SQL queries to identify potential SQL injection attacks.

[0087] Anomaly detectors: Detect anomalous behavior using statistical methods and machine learning models.

[0088] 3. Threat detection module.

[0089] The threat detection module is responsible for identifying and classifying security threats. This module includes the following submodules:

[0090] Deep Learning Classifier: Uses a deep learning model to classify the output of the behavioral analysis module to identify security threats.

[0091] Natural Language Processing Engine: Analyzes natural language queries to identify potential phishing attacks.

[0092] Threat Intelligence Integrator: Integrate external threat intelligence to identify known security threats.

[0093] 4. Response module.

[0094] The response module is responsible for taking action on detected security threats. This module includes the following submodules:

[0095] Alert Generator: Generates alerts when security threats are detected.

[0096] Automatic Blocker: Automatically blocks malicious access and attacks.

[0097] Forensic Analyzer: Collects and analyzes attack evidence to support subsequent investigation and response.

[0098] Among them, the deep learning classifier is the core component, which can automatically learn and extract features from large amounts of data to identify and classify security threats. The following is a detailed description of the deep learning classifier, and its implementation includes:

[0099] Data preprocessing: Clean and format the collected logs and network traffic data for subsequent processing;

[0100] Feature engineering: extract useful features from preprocessed data, such as user access frequency, query complexity, and network traffic patterns;

[0101] Model training: Use annotated security threat data to train deep learning models, such as convolutional neural networks (CNNs) or recurrent neural networks (RNNs);

[0102] Model evaluation: Evaluate the performance of the model through methods such as cross-validation and perform optimization;

[0103] Model deployment: The trained model is deployed to the production environment to monitor database activities in real time and detect security threats.

[0104] Through the above modules, the system can achieve efficient detection and response to database security threats and improve the security of the database system.

[0105] The embodiment of the present invention also provides a database security protection implementation device based on artificial intelligence, comprising: at least one memory and at least one processor;

[0106] The at least one memory is used to store a machine-readable program;

[0107] The at least one processor is used to call the machine-readable program to implement the artificial intelligence-based database security protection method described in the above embodiment.

[0108] The embodiment of the present invention also provides a computer-readable medium, on which computer instructions are stored, and when the computer instructions are executed by a processor, the processor executes the database security protection method based on artificial intelligence described in the above embodiment. Specifically, a system or device equipped with a storage medium can be provided, on which a software program code that implements the functions of any of the above embodiments is stored, and a computer (or CPU or MPU) of the system or device reads and executes the program code stored in the storage medium.

[0109] In this case, the program code itself read from the storage medium can realize the function of any one of the above-mentioned embodiments, and thus the program code and the storage medium storing the program code constitute a part of the present invention.

[0110] The storage medium embodiments for providing the program code include a floppy disk, a hard disk, a magneto-optical disk, an optical disk (such as CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RAM, DVD-RW, DVD+RW), a magnetic tape, a non-volatile memory card, and a ROM. Alternatively, the program code can be downloaded from a server computer by a communication network.

[0111] In addition, it should be clear that the functions of any of the above embodiments can be implemented not only by executing the program code read by the computer, but also by enabling an operating system operating on the computer to complete part or all of the actual operations based on instructions from the program code.

[0112] In addition, it can be understood that the program code read from the storage medium is written to a memory provided in an expansion board inserted into the computer or written to a memory provided in an expansion unit connected to the computer, and then based on the instructions of the program code, a CPU installed on the expansion board or the expansion unit is enabled to perform part or all of the actual operations, thereby realizing the functions of any of the above-mentioned embodiments.

[0113] The present invention is shown and described in detail above through the accompanying drawings and preferred embodiments. However, the present invention is not limited to these disclosed embodiments. Based on the above multiple embodiments, those skilled in the art can know that the code review methods in the above different embodiments can be combined to obtain more embodiments of the present invention, and these embodiments are also within the protection scope of the present invention.

Claims

1. A database security protection method based on artificial intelligence, characterized in that: The implementation of this method includes the following steps: 1) Data collection: Collect logs and other relevant data from the database server; 2) Behavioral analysis: Analyze the collected data to identify normal and abnormal database access patterns; 3) Threat detection: identifying and classifying security threats; 4) Response module: Take actions on detected security threats.

2. According to the artificial intelligence-based database security protection method of claim 1, it is characterized in that: The data collection specifically includes: Collect operation logs from the database server through the log collector; Monitor database performance indicators through performance monitor; Monitor the database server's network traffic with a network monitor to detect unusual traffic patterns.

3. According to the artificial intelligence-based database security protection method of claim 1, it is characterized in that: The performance metrics of the database, including response time and query latency.

4. According to the artificial intelligence-based database security protection method of claim 1, it is characterized in that: The behavior analysis specifically includes: Analyze users’ access patterns, including access frequency and query types, through user behavior analyzers; Analyze the structure and content of SQL queries through query analyzer to identify potential SQL injection attacks; Detect anomalous behavior using statistical methods and machine learning models through anomaly detectors.

5. According to the artificial intelligence-based database security protection method of claim 1, it is characterized in that: The threat detection specifically includes: Through the deep learning classifier, the output of the behavior analysis module is classified using a deep learning model to identify security threats; Analyze natural language queries through natural language processing engines to identify potential phishing attacks; Integrate external threat intelligence through the threat intelligence integrator to identify known security threats.

6. The method for protecting database security based on artificial intelligence according to claim 5, characterized in that: The implementation of the deep learning classifier includes: Data preprocessing: cleaning and formatting the collected logs and network traffic data; Feature engineering: extract useful features from preprocessed data, including user access frequency, query complexity, and network traffic patterns; Model training: Use labeled security threat data to train deep learning models; Model evaluation: evaluate the performance of the model and perform optimization; Model deployment: The trained model is deployed to the production environment to monitor database activities in real time and detect security threats.

7. The method for protecting database security based on artificial intelligence according to claim 1, characterized in that: The response module specifically includes: Generate alerts when security threats are detected via an alert generator; Automatically block malicious access and attacks through automatic blockers; With the Forensic Analyzer, collect and analyze attack evidence to support subsequent investigation and response.

8. A database security protection system based on artificial intelligence, characterized in that: The system includes: A data collection module, used to collect logs and other relevant data from the database server; A behavioral analysis module that analyzes the collected data to identify normal and abnormal database access patterns; Threat detection module, used to identify and classify security threats; A response module to take action on detected security threats; The system specifically implements database security protection through the artificial intelligence-based database security protection method described in any one of claims 1 to 7.

9. A database security protection implementation device based on artificial intelligence, characterized in that: include: at least one memory and at least one processor; The at least one memory is used to store a machine-readable program; The at least one processor is used to call the machine-readable program to implement the method described in any one of claims 1 to 7.

10. A computer-readable medium, characterized in that The computer readable medium stores computer instructions, and when the computer instructions are executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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