Industrial control system network security monitoring platform

Abstract

The invention discloses a network security monitoring platform for an industrial control system, and relates to the field of industrial control systems, and the key points of the technical scheme of the network security monitoring platform comprise a data acquisition module which is used for carrying out the real-time monitoring and acquisition of network flow data in the industrial control system, and storing the data in a local or cloud data center. An industrial control system network security monitoring platform is provided with a security event management module which can be used for automatically identifying, classifying, processing, responding and recording security events, can also be used for carrying out statistical analysis and generating related reports, and can also be used for monitoring the network security of the industrial control system by setting a judgment rule for abnormal traffic. According to the technical scheme, whether the data traffic needs to be filtered or not is judged by calculating the security level judgment coefficient of the traffic data, so that the function of automatically filtering the abnormal traffic data is achieved, processing of low-level events not including security events can be avoided, loss of a monitoring platform can be reduced, and the monitoring efficiency of the monitoring platform can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial control system network security, and more specifically, to an industrial control system network security monitoring platform. Background Art

[0002] Industrial control systems are business process management systems that ensure the automated operation, process control and monitoring of industrial infrastructure, which are composed of various automated control components and process control components that collect and monitor real-time data. Industrial control systems are usually composed of control components that work together to achieve a certain industrial purpose. They are a key component of industrial production infrastructure and are widely used in important national infrastructure fields such as electricity, water conservancy, chemical industry, transportation, energy, metallurgy, aerospace, etc. More than 80% of the key infrastructure related to national economy and people's livelihood relies on industrial control systems to achieve automated operations.

[0003] For some large-scale industrial control systems, they are vulnerable to network data attacks during actual use. Therefore, a special network security monitoring platform is set up to supervise their network security to ensure the normal operation of the industrial control system. However, when defining security events based on network traffic data, the existing industrial control system network security monitoring platform is unable to determine the level of security events, making it unable to filter out some normal abnormal traffic data, resulting in high system power consumption and low monitoring efficiency. Summary of the invention

[0004] In view of the deficiencies in the prior art, an object of the present invention is to provide an industrial control system network security monitoring platform.

[0005] To achieve the above object, the present invention provides the following technical solutions, including:

[0006] Data acquisition module, which is used to monitor and collect network traffic data in industrial control systems in real time and store the data in local or cloud data centers;

[0007] Data processing and analysis module, which is used to process, analyze and mine the collected network traffic data, identify abnormal traffic and attack behaviors, detect and predict security events, and generate data analysis results;

[0008] Security incident management module, which is used to automatically identify, classify, process, respond to and record security incidents, perform statistical analysis on security incidents, and generate reports;

[0009] The specific process of automatically identifying and classifying security incidents is as follows:

[0010] S1. Data preprocessing: preprocess the collected network traffic data, including data cleaning, deduplication, and standardization;

[0011] S2. Feature extraction: Extract features from the preprocessed data for use by subsequent recognition algorithms. Features related to attack behaviors and abnormal traffic can be extracted, including traffic size, frequency, duration, IP address, and port number.

[0012] S3, security event classification: Use the classifier to classify the extracted features and classify security events into different types according to the features;

[0013] S4, rule matching: Based on the classifier, further identify and classify security events according to the established rules; the specific process is: set the flow data feature judgment threshold H0, mark the total flow of the identified flow data as Gk, the flow frequency as Bk, and the duration as Tk, where k is the sequence number corresponding to the flow data, and obtain the security level judgment coefficient Hk of the flow data through the formula Hk=Gk×a1+Bk×a2+Tk×a3, compare Hk with H0, if Hk≥H0, then mark the flow data as a high-level security event, according to the specific type of the flow data, execute automatic blocking of the attack behavior, and report the high-level event to the administrator; if Hk≤H0, then mark the flow data as a low-level event, directly filter the flow data, and do not count it as a security event;

[0014] The alarm and response module is used to promptly issue an alarm signal when abnormal traffic or attack behavior is found, and take corresponding response measures;

[0015] User management and permission control module, which is used to authenticate, authorize and manage platform users;

[0016] The data backup and recovery module is used to back up and restore important data.

[0017] Preferably, the objects of the data acquisition module in network data traffic should include production control systems, monitoring and data acquisition systems, distributed control systems, and process control systems, and the data acquisition technologies used by the data acquisition module include network traffic analysis, intrusion detection and defense.

[0018] Preferably, the specific process of the data processing and analysis module processing the data is:

[0019] S1. Data collection: Collect the required network traffic data through data collection equipment. The collected data includes original data and pre-processed data.

[0020] S2. Data cleaning: Data cleaning is performed on the collected data, including removing duplicate data, processing missing values, eliminating noise, etc., to ensure the accuracy of data analysis;

[0021] S3. Data conversion: Convert data as needed, including converting raw data into standardized data, or converting text data into numerical data, etc.

[0022] S4. Data mining: mining the processed data through cluster analysis, association rule mining, and sequence pattern methods to discover patterns, trends, and associations in the data;

[0023] S5. Anomaly detection: Analyze data through machine learning algorithms to identify abnormal traffic and attack behaviors, including building relevant data models, and classifying, clustering or regressing data, and analyzing data using machine learning algorithms;

[0024] S6. Security incident detection and prediction: Analyze the characteristics of abnormal traffic and attack behaviors through time series analysis, anomaly detection, and deep learning, build a security incident detection model, and predict possible security incidents in the future;

[0025] S7. Visual presentation: Through data visualization tools and chart making tool technology, the data analysis results are presented in charts, graphs or other visual forms, so that users can quickly understand the network security status of industrial control systems;

[0026] S8. Report generation: Generate corresponding security reports based on the analysis results, conduct detailed analysis and summary of security incidents, and provide corresponding suggestions and measures.

[0027] Preferably, when recording security events, the security event management module specifically records the detailed information of each security event, including time, location, attack type, IP address, and port number; at the same time, a security event report can be generated to count and analyze the security events over a period of time, including the type, quantity, level, and handling status of the security events, and generate various reports and charts as needed.

[0028] Preferably, in the security event management module, security events can be classified into network attacks, virus attacks, Trojan attacks, phishing attacks, and ransomware attacks according to the type of attack; security events can be classified into server attacks, terminal device attacks, database attacks, etc. according to the target of the attack.

[0029] Preferably, the specific process of the alarm and response module in performing alarm and response is:

[0030] S1. Traffic monitoring and anomaly detection: Use traffic monitoring tools to monitor network traffic in real time. When traffic data exceeds the normal range or shows abnormal patterns, an alarm mechanism will be triggered.

[0031] S2, alarm signal generation: once abnormal traffic or attack behavior is confirmed, the module will generate a corresponding alarm signal;

[0032] S3, alarm signal sending: the alarm signal will be sent to the administrator through the preset channel;

[0033] S4, Automatically block attacks: When the alarm signal reaches the set level, the alarm and response module can automatically block the attack behavior;

[0034] S5. Device isolation: If a device is confirmed to have been compromised or is highly suspected to have been compromised, the alarm and response module can isolate it to prevent it from further harming the system;

[0035] S6. Logging and auditing: During the entire alarm and response process, the alarm and response module will record all relevant operations, including the time when the alarm is triggered, the response operations performed, the operators who performed the operations, and other detailed information.

[0036] Preferably, when executing operations, the user management and authority control module monitors network traffic data in real time, promptly detects abnormal traffic and attack behaviors, and performs identity authentication, authorization, and management on users; at the same time, it can limit user access rights and operating functions to prevent data leakage and abuse.

[0037] Preferably, the data backup and recovery module implements the data backup process as follows:

[0038] Define backup strategies, including backup time, frequency, storage location, etc.;

[0039] Develop a backup plan and perform backup operations regularly according to the backup strategy;

[0040] When backing up data, use the mysql dump command to back up the specified database and store the backup file in the specified location;

[0041] Record backup information, including backup time, backup file name, and backup size;

[0042] The data backup and recovery module implements the data recovery process as follows:

[0043] Define the recovery strategy, including the time, frequency, and storage location of recovery;

[0044] Develop a recovery plan and perform recovery operations regularly according to the recovery strategy;

[0045] When restoring data, use the mysql command to restore the specified database and store the backup file in the specified location;

[0046] Record recovery information, including recovery time, recovery file name, and recovery size.

[0047] Compared with the prior art, the present invention has the following beneficial effects:

[0048] (1) By setting up a security event management module, it can be used to automatically identify, classify, process, respond to and record security events, and can also perform statistical analysis and generate relevant reports. At the same time, by setting up judgment rules for abnormal traffic and calculating the security level judgment coefficient of traffic data, it is determined whether the data traffic needs to be filtered. Therefore, the automatic filtering function of abnormal traffic data is realized, which can avoid processing low-level events that are not counted as security events, thereby helping to reduce the loss of the monitoring platform and improve the monitoring efficiency of the monitoring platform;

[0049] At the same time, it can comprehensively monitor network traffic data and automatically identify, classify, process and respond to security incidents, thereby improving the security of industrial control systems;

[0050] (2) By setting up data processing and analysis modules, the collected network traffic data can be processed, analyzed and mined, abnormal traffic and attack behaviors can be identified, security events can be detected and predicted, and data analysis results can be generated, which is conducive to improving the security and early warning capabilities of the monitoring platform;

[0051] By setting up a data backup and recovery module, the platform also has functions such as user management, authority control, and data backup and recovery, further enhancing the reliability and security of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 A system block diagram of an industrial control system network security monitoring platform provided by the present invention. DETAILED DESCRIPTION

[0053] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art may think of other obvious variations.

[0054] Reference Figure 1 An industrial control system network security monitoring platform embodiment of the present invention is further described.

[0055] An industrial control system network security monitoring platform, comprising: a data acquisition module, a data processing and analysis module, a security event management module, an alarm and response module and a data backup and recovery module;

[0056] Data acquisition module, which is used to monitor and collect network traffic data in industrial control systems in real time and store the data in local or cloud data centers;

[0057] Data processing and analysis module, which is used to process, analyze and mine the collected network traffic data, identify abnormal traffic and attack behaviors, detect and predict security events, and generate data analysis results;

[0058] Security incident management module, which is used to automatically identify, classify, process, respond to and record security incidents, perform statistical analysis on security incidents, and generate reports;

[0059] The alarm and response module is used to promptly issue an alarm signal when abnormal traffic or attack behavior is found, and take corresponding response measures;

[0060] User management and permission control module, which is used to authenticate, authorize and manage platform users;

[0061] The data backup and recovery module is used to back up and restore important data. The process of implementing data backup is as follows:

[0062] Define backup strategies, including backup time, frequency, storage location, etc.;

[0063] Develop a backup plan and perform backup operations regularly according to the backup strategy;

[0064] When backing up data, use the mysql dump command to back up the specified database and store the backup file in the specified location;

[0065] Record backup information, including backup time, backup file name, and backup size;

[0066] The process of data recovery is as follows:

[0067] Define the recovery strategy, including the time, frequency, and storage location of recovery;

[0068] Develop a recovery plan and perform recovery operations regularly according to the recovery strategy;

[0069] When restoring data, use the mysql command to restore the specified database and store the backup file in the specified location;

[0070] Record recovery information, including recovery time, recovery file name, and recovery size.

[0071] The specific process of the network security monitoring platform provided by the present invention in performing network security monitoring on an industrial control system is as follows:

[0072] Step 1: Use network traffic analysis, intrusion detection and defense technology through the data acquisition module to monitor and collect network traffic data in the industrial control system in real time, and store the collected data in a local or cloud data center and upload it to the data processing and analysis module. The collection objects should include production control systems, monitoring and data acquisition systems, distributed control systems, and process control systems;

[0073] Step 2: After receiving the data, the data processing and analysis module starts to process the data. The specific process is as follows:

[0074] S1. Data collection: Collect the required network traffic data through data collection equipment. The collected data includes original data and pre-processed data.

[0075] S2. Data cleaning: Data cleaning is performed on the collected data, including removing duplicate data, processing missing values, eliminating noise, etc., to ensure the accuracy of data analysis;

[0076] S3. Data conversion: Convert data as needed, including converting raw data into standardized data, or converting text data into numerical data, etc.

[0077] S4. Data mining: mining the processed data through cluster analysis, association rule mining, and sequence pattern methods to discover patterns, trends, and associations in the data;

[0078] S5. Anomaly detection: Analyze data through machine learning algorithms to identify abnormal traffic and attack behaviors, including building relevant data models, and classifying, clustering or regressing data, and analyzing data using machine learning algorithms;

[0079] S6. Security incident detection and prediction: Analyze the characteristics of abnormal traffic and attack behaviors through time series analysis, anomaly detection, and deep learning, build a security incident detection model, and predict possible security incidents in the future;

[0080] S7. Visual presentation: Through data visualization tools and chart making tool technology, the data analysis results are presented in charts, graphs or other visual forms, so that users can quickly understand the network security status of industrial control systems;

[0081] S8. Report generation: Generate corresponding security reports based on the analysis results, conduct detailed analysis and summary of security incidents, and provide corresponding suggestions and measures.

[0082] Step 3: Perform security event analysis on the processed and analyzed data through the security event management module, including:

[0083] S1. Automatically identify and classify security incidents:

[0084] Data preprocessing: preprocess the collected network traffic data, including data cleaning, deduplication, and standardization;

[0085] Feature extraction: Extract features from preprocessed data for use by subsequent recognition algorithms. Features related to attack behaviors and abnormal traffic can be extracted, including traffic size, frequency, duration, IP address, and port number.

[0086] Security event classification: Use a classifier to classify the extracted features and classify security events into different types based on the features. Machine learning algorithms can be used, such as support vector machine (SVM), random forest, neural network, etc.

[0087] Rule matching: Based on the classifier, security events are further identified and classified according to the established rules, specifically;

[0088] Set the traffic data feature judgment threshold H0, mark the total traffic of the identified traffic data as Gk, the traffic frequency as Bk, and the duration as Tk, where k is the sequence number corresponding to the traffic data, and obtain the security level judgment coefficient Hk of the traffic data through the formula Hk=Gk×a1+Bk×a2+Tk×a3, where a1, a2, and a3 are all proportional coefficients. Compare Hk with H0. If Hk≥H0, mark the traffic data as a high-level security event. According to the specific type of the traffic data, for example, according to the type of attack, security events can be divided into network attacks, virus attacks, Trojan attacks, phishing attacks, ransomware attacks, etc., execute automatic blocking of this attack behavior, and report the high-level event to the administrator; if Hk≤H0, mark the traffic data as a low-level event, directly filter the traffic data, and do not count it as a security event;

[0089] S3. Record and statistically analyze security events:

[0090] Record detailed information for each security incident, including time, location, attack type, IP address, and port number. For example, on October 1, 2022, a virus attack occurred in the first production workshop. The IP address was 123.456.789.123 and the port number was PC456. This will facilitate subsequent queries and analysis. At the same time, a security incident report can be generated to count and analyze security incidents over a period of time, including the type, quantity, level, and handling of security incidents, so that administrators can understand the security status and risks. Various reports and charts can be generated as needed to understand the security status more intuitively. For example, from October 1 to October 8, 2022, a total of 8 high-level incidents occurred, of which 5 high-level incidents have been automatically resolved, and solutions have been provided for the remaining 3 high-level incidents, which need to be resolved by relevant managers.

[0091] Step 4: After detecting abnormal data traffic, the alarm and response module performs alarm and response processing. The specific process is as follows:

[0092] S1. Traffic monitoring and anomaly detection: Use traffic monitoring tools to monitor network traffic in real time. When traffic data exceeds the normal range or shows abnormal patterns, an alarm mechanism will be triggered.

[0093] S2. Alarm signal generation: Once abnormal traffic or attack behavior is confirmed, the module will generate a corresponding alarm signal; this signal may include a warning message, log entry or other form of notification;

[0094] S3. Alarm signal transmission: Alarm signals will be sent to administrators through preset channels; these channels may include email, SMS, phone, instant messaging software, etc. At the same time, in order to ensure that administrators can receive and respond quickly, multiple channels are usually used for notification;

[0095] S4. Automatically block attacks: When the alarm signal reaches the set level, the alarm and response module can automatically block the attack behavior; this can be achieved by automatically closing related ports, blocking IP addresses, modifying network device configurations, etc.

[0096] S5. Device isolation: If a device is confirmed to have been compromised or is highly suspected of being compromised, the alarm and response module can isolate it to prevent it from continuing to harm the system; this can be achieved by disconnecting the network connection, modifying the device configuration file, restarting the device, etc.

[0097] S6. Logging and auditing: During the entire alarm and response process, the alarm and response module will record all relevant operations, including the time when the alarm was triggered, the response operation performed, the operator who performed the operation, and other detailed information; these logs can be used for subsequent auditing and analysis to understand the cause of the alarm, the response effect, and the attacker's behavior;

[0098] Step 4: After abnormal data traffic is detected, abnormal traffic and attack behaviors are discovered in a timely manner through the user management and authority control module, and the user is authenticated, authorized and managed; at the same time, the user's access rights and operation functions can be restricted to prevent data leakage and abuse;

[0099] Step 5: If data is lost after the industrial control system is attacked, data recovery is performed through the data backup and recovery module. The specific process is as follows:

[0100] Define the recovery strategy, including the time, frequency, and storage location of recovery;

[0101] Develop a recovery plan and perform recovery operations regularly according to the recovery strategy;

[0102] When restoring data, use the mysql command to restore the specified database and store the backup file in the specified location;

[0103] Record recovery information, including recovery time, recovery file name, and recovery size.

[0104] 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 the orientation or position relationship are based on the orientation or position relationship shown in the 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 a limitation on the present invention.

[0105] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, a feature defined as "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0106] The above is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technicians in this technical field, some improvements and modifications without departing from the principle of the present invention should also be regarded as the protection scope of the present invention.

Claims

1. An industrial control system network security monitoring platform, characterized in that: include: Data acquisition module, which is used to monitor and collect network traffic data in industrial control systems in real time and store the data in local or cloud data centers; Data processing and analysis module, which is used to process, analyze and mine the collected network traffic data, identify abnormal traffic and attack behaviors, detect and predict security events, and generate data analysis results; Security incident management module, which is used to automatically identify, classify, process, respond to and record security incidents, perform statistical analysis on security incidents, and generate reports; The specific process of automatically identifying and classifying security incidents is as follows: S1. Data preprocessing: preprocess the collected network traffic data, including data cleaning, deduplication, and standardization; S2. Feature extraction: Extract features from the preprocessed data for use by subsequent recognition algorithms. Features related to attack behaviors and abnormal traffic can be extracted, including traffic size, frequency, duration, IP address, and port number. S3, security event classification: Use the classifier to classify the extracted features and classify security events into different types according to the features; S4, rule matching: Based on the classifier, further identify and classify security events according to the established rules, specifically: set the flow data feature judgment threshold H0, mark the total flow of the identified flow data as Gk, the flow frequency as Bk, and the duration as Tk, where k is the sequence number corresponding to the flow data, and obtain the security level judgment coefficient Hk of the flow data through the formula Hk=Gk×a1+Bk×a2+Tk×a3, compare Hk with H0, if Hk≥H0, then mark the flow data as a high-level security event, according to the specific type of the flow data, execute automatic blocking of the attack behavior, and report the high-level event to the administrator; if Hk≤H0, then mark the flow data as a low-level event, directly filter the flow data, and do not count it as a security event; The alarm and response module is used to promptly issue an alarm signal when abnormal traffic or attack behavior is found, and take corresponding response measures; User management and permission control module, which is used to authenticate, authorize and manage platform users; The data backup and recovery module is used to back up and restore important data.

2. The industrial control system network security monitoring platform according to claim 1 is characterized in that: The objects of the data acquisition module in network data traffic should include production control systems, monitoring and data acquisition systems, distributed control systems, and process control systems. The data acquisition technologies used by the data acquisition module include network traffic analysis, intrusion detection and defense.

3. The industrial control system network security monitoring platform according to claim 1 is characterized in that: The specific process of the data processing and analysis module processing the data is as follows: S1. Data collection: Collect the required network traffic data through data collection equipment. The collected data includes original data and pre-processed data. S2. Data cleaning: Data cleaning is performed on the collected data, including removing duplicate data, processing missing values, eliminating noise, etc., to ensure the accuracy of data analysis; S3. Data conversion: Convert data as needed, including converting raw data into standardized data, or converting text data into numerical data, etc. S4. Data mining: mining the processed data through cluster analysis, association rule mining, and sequence pattern methods to discover patterns, trends, and associations in the data; S5. Anomaly detection: Analyze data through machine learning algorithms to identify abnormal traffic and attack behaviors, including building relevant data models, and classifying, clustering or regressing data, and analyzing data using machine learning algorithms; S6. Security incident detection and prediction: Analyze the characteristics of abnormal traffic and attack behaviors through time series analysis, anomaly detection, and deep learning, build a security incident detection model, and predict possible security incidents in the future; S7. Visual presentation: Through data visualization tools and chart making tool technology, the data analysis results are presented in charts, graphs or other visual forms, so that users can quickly understand the network security status of industrial control systems; S8. Report generation: Generate corresponding security reports based on the analysis results, conduct detailed analysis and summary of security incidents, and provide corresponding suggestions and measures.

4. The industrial control system network security monitoring platform according to claim 1 is characterized in that: When recording security events, the security event management module specifically records the detailed information of each security event, including time, location, attack type, IP address, and port number, for subsequent query and analysis; at the same time, it can generate a security event report to count and analyze the security events over a period of time, including the type, number, level, and handling status of the security events, so that the administrator can understand the security status and risks; and generate various reports and charts as needed to more intuitively understand the security status.

5. The industrial control system network security monitoring platform according to claim 1, characterized in that: In the security event management module, security events can be classified into network attacks, virus attacks, Trojan attacks, phishing attacks, and ransomware attacks according to the type of attack; security events can be classified into server attacks, terminal device attacks, database attacks, etc. according to the target of the attack.

6. The industrial control system network security monitoring platform according to claim 1, characterized in that: The specific process of the alarm and response module in performing alarm and response is as follows: S1. Traffic monitoring and anomaly detection: Use traffic monitoring tools to monitor network traffic in real time. When traffic data exceeds the normal range or shows abnormal patterns, an alarm mechanism will be triggered. S2. Alarm signal generation: Once abnormal traffic or attack behavior is confirmed, the module will generate a corresponding alarm signal; this signal may include a warning message, log entry or other form of notification. S3. Alarm signal transmission: Alarm signals will be sent to administrators through preset channels; these channels may include email, SMS, phone, instant messaging software, etc. At the same time, in order to ensure that administrators can receive and respond quickly, multiple channels are usually used for notification. S4. Automatically block attacks: When the alarm signal reaches the set level, the alarm and response module can automatically block the attack behavior; this can be achieved by automatically closing related ports, blocking IP addresses, modifying network device configurations, etc. S5. Device isolation: If the device is confirmed to have been compromised or is highly suspected of being compromised, the alarm and response module can isolate it to prevent it from continuing to harm the system; this can be achieved by disconnecting the network connection, modifying the device configuration file, restarting the device, etc. S6. Logging and auditing: During the entire alarm and response process, the alarm and response module will record all relevant operations, including the time when the alarm was triggered, the response operations performed, the operators who performed the operations, and other detailed information; these logs can be used for subsequent auditing and analysis to understand the cause of the alarm, the response effect, and the attacker's behavior.

7. The industrial control system network security monitoring platform according to claim 1, characterized in that: When executing operations, the user management and authority control module monitors network traffic data in real time, promptly discovers abnormal traffic and attack behaviors, and performs identity authentication, authorization, and management on users; at the same time, it can limit user access rights and operating functions to prevent data leakage and abuse.

8. The industrial control system network security monitoring platform according to claim 1, characterized in that: The data backup and recovery module implements the data backup process as follows: Define backup strategies, including backup time, frequency, storage location, etc.; Develop a backup plan and perform backup operations regularly according to the backup strategy; When backing up data, use the mysqldump command to back up the specified database and store the backup file in the specified location; Record backup information, including backup time, backup file name, and backup size; The data backup and recovery module implements the data recovery process as follows: Define the recovery strategy, including the time, frequency, and storage location of recovery; Develop a recovery plan and perform recovery operations regularly according to the recovery strategy; When restoring data, use the mysql command to restore the specified database and store the backup file in the specified location; Record recovery information, including recovery time, recovery file name, and recovery size.

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