Industrial control network intelligent safety monitoring and early warning method and system

Abstract

The invention discloses an intelligent security monitoring and early warning method and system for an industrial control network, relates to the technical field of network security monitoring, and can collect multi-dimensional data such as network traffic, user behaviors and equipment states in real time through a data acquisition module and a system to ensure the timeliness and comprehensiveness of monitoring. Therefore, potential security threats can be quickly identified, and the overall security protection capability is improved. And the data preprocessing module cleans and standardizes the collected data, so that the data quality and consistency are improved, a reliable basis is provided for subsequent risk assessment, and misjudgment caused by data inconsistency is reduced. And the risk assessment module calculates a comprehensive risk index through a multi-dimensional assessment formula, different types of security threats can be identified more accurately, and a scientific basis is provided for a subsequent early warning mechanism. The multi-level early warning mechanism module divides the risk into a low level, a middle level and a high level according to the comprehensive risk index, and a targeted response strategy can be realized.

Description

Technical Field

[0001] The present invention relates to the technical field of network security monitoring, and in particular to an industrial control network intelligent security monitoring and early warning method and system. Background Art

[0002] With the acceleration of digital transformation, industrial control systems (ICS) play an increasingly important role in automation and production efficiency improvement. Specifically, network security issues in industrial control systems have attracted widespread attention, especially in key infrastructure fields such as manufacturing, energy and transportation. These industries not only rely on highly integrated control systems for production and management, but also put forward higher requirements for real-time monitoring and security of data. There is an urgent need for an intelligent monitoring and early warning system to ensure the safety and stable operation of the system.

[0003] At present, the main problem facing industrial control systems is the complexity and diversity of security threats. Traditional security protection measures are often difficult to adapt to new attack methods.

[0004] Existing monitoring systems mostly use static early warning mechanisms and fail to effectively and flexibly respond to different types of threats. This results in the system being unable to identify and respond in a timely manner when faced with potential security incidents, leaving the enterprise without the necessary protection when attacked, increasing the risk of data leakage and system paralysis. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides an industrial control network intelligent security monitoring and early warning method and system, which solves the problems mentioned in the background technology.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: an industrial control network intelligent security monitoring and early warning system, including a data acquisition module, a data preprocessing module, a risk assessment module, a multi-level early warning mechanism module, a response strategy generation module and a report generation module;

[0007] The data acquisition module collects network traffic R, user behavior data U and device status data S in real time, wherein the user behavior data U includes login records, operation logs, access frequency and session duration; the device status data S includes temperature data, vibration level, current, voltage and fault code;

[0008] The data preprocessing module cleans and standardizes the network traffic R, user behavior data U and device status data S collected by the data collection module to obtain the standardized network traffic R B , Standardized user behavior data U B and standardized equipment status data S B ;

[0009] The risk assessment module obtains the standardized network traffic R according to the data preprocessing module. B , Standardized user behavior data U B and standardized equipment status data S B , use the multi-dimensional evaluation formula to calculate the comprehensive risk index W;

[0010] The multi-level early warning mechanism module performs early warning classification according to the comprehensive risk index W, and the early warning level is divided into three levels: low risk, medium risk and high risk;

[0011] The response strategy generation module generates a corresponding response strategy according to the warning level. Low risk levels continue to be monitored, medium risk levels notify administrators, and high risk levels require emergency measures to be initiated;

[0012] The report generation module generates a monitoring report according to the result of the response strategy generation module, including risk assessment results, early warning information and response measures.

[0013] Preferably, the data collection module includes a network traffic collection unit, a user behavior collection unit and a device status collection unit;

[0014] The network traffic collection unit captures data packets in real time through a network interface and records network traffic R, including inbound and outbound traffic, bandwidth utilization, and latency;

[0015] The user behavior collection unit records the user's login record, operation log, access frequency and session duration, and generates user behavior data U;

[0016] The device status acquisition unit collects the temperature data, vibration level, current and voltage of the device through temperature sensors, vibration sensors, current sensors and voltage sensors, and reads the fault code in real time through the self-diagnosis function of the device to form the device status data S.

[0017] Preferably, the data preprocessing module includes a data cleaning unit and a data standardization unit;

[0018] The data cleaning unit cleans the network traffic R, user behavior data U and device status data S collected from the data acquisition module, removes noise and abnormal values, and obtains the network cleaned traffic R C , user behavior cleaning data U C and equipment status cleaning data S C ;

[0019] The data standardization unit cleans the network flow R C , user behavior cleaning data U C and equipment status cleaning data S CPerform standardization to obtain standardized network traffic R B , Standardized user behavior data U B and standardized equipment status data S B ;

[0020] The normalized network traffic R B , Standardized user behavior data U B and standardized equipment status data S B , obtained by the following formula:

[0021]

[0022] In the formula, μ R , μ U and μ S , represent the mean of network traffic, user behavior data and device status data, σ R , σ U and σ S Represent the standard deviation of network traffic, user behavior data, and device status data respectively.

[0023] Preferably, the risk assessment module includes a risk index calculation unit, which calculates the risk index based on the standardized network traffic R B , Standardized user behavior data U B and standardized equipment status data S B Calculate the comprehensive risk index W;

[0024] The comprehensive risk index W is obtained by the following formula:

[0025]

[0026] In the formula, ω 1 ,ω 2 and ω 3 Represent the weight coefficients of network traffic, user behavior data and device status data respectively, and satisfy ω 1 +ω 2 +ω 3 =1,maxR B Represents the normalized network traffic R B The peak value of

[0027] The comprehensive risk index W ranges from 0 to 1.

[0028] Preferably, the multi-level warning mechanism module includes a warning level determination unit and a warning information generation unit;

[0029] The warning level determination unit determines the risk level according to the comprehensive risk index W and generates a corresponding warning level; the warning level is divided into three levels:

[0030] Low risk: comprehensive risk index W < 0.3;

[0031] Medium risk: 0.3≤comprehensive risk index W<0.7;

[0032] High risk: 0.7≤comprehensive risk index W<1;

[0033] The warning information generation unit generates corresponding warning information and response measures according to the risk level determined by the comprehensive risk index W:

[0034] Low risk: continued monitoring is recommended, no immediate intervention is required;

[0035] Medium risk: Send a warning notification and suggest checking related equipment;

[0036] High risk: Send an urgent response notification, requiring immediate action.

[0037] Preferably, the response strategy generation module includes a strategy judgment unit and a strategy execution unit;

[0038] The policy judgment unit receives risk level information, including low risk, medium risk and high risk;

[0039] Low-risk output strategy: "Continue monitoring", and continue to monitor system data and regularly update risk assessment reports;

[0040] Medium-risk output strategy: "Notify administrators", which includes sending emails to notify relevant administrators, including risk information and recommendations; pop-up alerts on the system dashboard to display the current risk status; and sending instant messages through internal communication tools to ensure rapid communication of information;

[0041] High-risk output strategy: "Initiate emergency measures", which specifically includes immediately calling the emergency response plan and conducting on-site inspections; launching team emergency drills to ensure that team members understand their respective responsibilities; shutting down affected equipment or systems and isolating them; collecting and recording relevant data on the incident as a basis for subsequent analysis.

[0042] Preferably, the policy execution unit performs specific operations according to the policy judgment unit to ensure that risks are effectively managed;

[0043] When the output strategy is "Continue Monitoring": it enters the continuous monitoring state to ensure that data collection is constantly updated;

[0044] When the output policy is "Notify Administrator": the administrator is notified by email and the dashboard pops up an alert;

[0045] When the output strategy is "Initiate emergency measures": the emergency response plan will start the preset emergency response process, assign tasks to each department, and isolate and shut down the affected equipment to ensure that the normal operation of other systems is not affected.

[0046] Preferably, the report generation module includes a report content arrangement unit and a report output unit;

[0047] The report content collating unit collates the content of the monitoring report according to the results of the response strategy generation module, including risk assessment results, early warning information and response measures;

[0048] The risk assessment results include displaying the comprehensive risk index W and risk level;

[0049] The warning information includes recording the warning level and its corresponding specific warning information;

[0050] The response includes a detailed listing of response measures taken and recommendations;

[0051] Monitoring report = risk assessment results + early warning information + response measures.

[0052] Preferably, the report output unit formats the organized monitoring report content into a readable document according to the monitoring report of the report content organization unit, and outputs it into a specified format including PDF and Word;

[0053] Specifically, it includes formatting the organized content according to preset templates; generating the final report document to ensure that each part of the information is clear and readable; and outputting the report, which supports saving in multiple formats for easy sharing and archiving.

[0054] An industrial control network intelligent security monitoring and early warning method comprises the following steps:

[0055] Step 1: The data collection module collects network traffic R, user behavior data U and device status data S in real time, where user behavior data U includes login records, operation logs, access frequency and session duration; device status data S includes temperature data, vibration level, current, voltage and fault code;

[0056] Step 2: The data preprocessing module cleans and standardizes the network traffic R, user behavior data U and device status data S collected by the data collection module to obtain the standardized network traffic R B , Standardized user behavior data U B and standardized equipment status data S B ;

[0057] Step 3: The risk assessment module obtains the standardized network traffic R from the data preprocessing module. B, Standardized user behavior data U B and standardized equipment status data S B , use the multi-dimensional evaluation formula to calculate the comprehensive risk index W;

[0058] Step 4: The multi-level early warning mechanism module classifies early warnings according to the comprehensive risk index W. The early warning levels are divided into three levels: low risk, medium risk and high risk;

[0059] Step 5: The response strategy generation module generates corresponding response strategies according to the warning level. Low risk levels continue to be monitored, medium risk levels notify administrators, and high risk levels require emergency measures to be initiated;

[0060] Step 6: The report generation module generates a monitoring report based on the results of the response strategy generation module, including risk assessment results, warning information and response measures.

[0061] The present invention provides an industrial control network intelligent security monitoring and early warning method and system, which has the following beneficial effects:

[0062] (1) When the system is running, through the data acquisition module, the system can collect multi-dimensional data such as network traffic, user behavior and device status in real time to ensure the timeliness and comprehensiveness of monitoring. This helps to quickly identify potential security threats and improve overall security protection capabilities. The data preprocessing module cleans and standardizes the collected data, improves data quality and consistency, provides a reliable basis for subsequent risk assessment, and reduces misjudgments caused by inconsistent data. The risk assessment module calculates the comprehensive risk index through a multi-dimensional assessment formula, which can more accurately identify different types of security threats and provide a scientific basis for subsequent early warning mechanisms. This precise assessment capability significantly improves the response speed and effectiveness of the system.

[0063] The multi-level early warning mechanism module divides risks into three levels: low, medium, and high according to the comprehensive risk index, and can implement targeted response strategies. This flexible response method can better meet the security needs under different threat levels and improve the system's resilience. The response strategy generation module formulates corresponding measures according to the early warning level, effectively reducing the reaction time in high-risk situations, launching emergency measures in a timely manner, and reducing potential losses.

[0064] (2) The multi-level early warning mechanism module clearly divides risks into three levels: low, medium, and high through the early warning level determination unit, providing a simple and clear risk identification system. This classification method helps to quickly respond to different risk levels and ensure that managers can take appropriate measures at the first time.

[0065] The response strategy generated according to the comprehensive risk index W can take corresponding measures for different risk levels. When the risk is low, it is recommended to continue monitoring, when the risk is medium, the administrator is notified immediately, and when the risk is high, emergency measures are initiated. This hierarchical response mechanism improves the flexibility and efficiency of the system and avoids unnecessary intervention.

[0066] In the case of medium-risk situations, administrators are notified through multiple channels, including email, dashboard alerts, and instant messages, ensuring that information can be conveyed quickly. This multi-channel information delivery method reduces communication delays, ensures that relevant personnel can respond in a timely manner, and improves the overall response speed.

[0067] In high-risk situations, the emergency response plan activated includes specific steps such as on-site inspection, team drills and equipment isolation. This systematic approach ensures the comprehensiveness and effectiveness of risk management and reduces potential losses.

[0068] (3) Capture data through network interfaces and sensors, and record relevant parameters such as traffic, login status, temperature, vibration, etc. The data is stored in raw format for subsequent processing. Noise and outliers are removed during the cleaning process, and statistical methods are used for standardization to ensure data consistency and comparability. Generate standardized network traffic RB, user behavior data UB, and device status data SB. Calculate the comprehensive risk index W, use the preprocessed standardized data, apply a multi-dimensional evaluation formula, comprehensively consider the contribution of different data to the risk, and output the comprehensive risk index W, which ranges from 0 to 1, indicating the size of the potential risk.

[0069] The comprehensive risk index W is graded. The warning level threshold is set to determine the interval of the comprehensive risk index W, and the low risk, medium risk or high risk level is output to provide a basis for subsequent response strategies. Based on the warning level, the corresponding strategy is formulated. Low risk is monitored, medium risk is notified to the administrator, and high risk is initiated to ensure timely response to potential threats.

[0070] (4) The response measures section lists in detail the specific measures and suggestions taken, providing managers with clear action guidelines. This level of detail ensures that each department has a clear action plan when facing risks, improving response efficiency and accuracy.

[0071] The report output unit supports formatting monitoring reports into multiple formats such as PDF and Word, which facilitates document sharing and archiving. This flexibility improves the usability of reports, facilitates transmission and use between different platforms, and supports a wider range of communication. By formatting the report content according to the preset template, the uniformity and professionalism of the final report document are ensured. The standardized document format enhances the authority and readability of the report, allowing all parties to quickly understand the report content. Through systematic report generation, it is convenient to compare and analyze historical data later, providing a basis for improving monitoring and response strategies. This long-term data accumulation and analysis helps to continuously optimize the safety performance of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] Figure 1 This is a schematic diagram of a block diagram of an industrial control network intelligent safety monitoring and early warning system of the present invention;

[0073] Figure 2 The present invention is a schematic diagram of the steps of an industrial control network intelligent security monitoring and early warning method. DETAILED DESCRIPTION

[0074] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0075] Example 1

[0076] The present invention provides an industrial control network intelligent security monitoring and early warning method and system, please refer to Figure 1 , including data acquisition module, data preprocessing module, risk assessment module, multi-level early warning mechanism module, response strategy generation module and report generation module;

[0077] The data acquisition module collects network traffic R, user behavior data U and device status data S in real time, wherein the user behavior data U includes login records, operation logs, access frequency and session duration; the device status data S includes temperature data, vibration level, current, voltage and fault code;

[0078] The data preprocessing module cleans and standardizes the network traffic R, user behavior data U and device status data S collected by the data collection module to obtain the standardized network traffic R B , Standardized user behavior data U B and standardized equipment status data S B ;

[0079] The risk assessment module obtains the standardized network traffic R according to the data preprocessing module. B , Standardized user behavior data U B and standardized equipment status data S B , use the multi-dimensional evaluation formula to calculate the comprehensive risk index W;

[0080] The multi-level early warning mechanism module performs early warning classification according to the comprehensive risk index W, and the early warning level is divided into three levels: low risk, medium risk and high risk;

[0081] The response strategy generation module generates a corresponding response strategy according to the warning level. Low risk levels continue to be monitored, medium risk levels notify administrators, and high risk levels require emergency measures to be initiated;

[0082] The report generation module generates a monitoring report according to the result of the response strategy generation module, including risk assessment results, early warning information and response measures.

[0083] In this embodiment, through the data acquisition module, the system can collect multi-dimensional data such as network traffic, user behavior and device status in real time to ensure the timeliness and comprehensiveness of monitoring. This helps to quickly identify potential security threats and improve overall security protection capabilities. The data preprocessing module cleans and standardizes the collected data, improves data quality and consistency, provides a reliable basis for subsequent risk assessment, and reduces misjudgments caused by inconsistent data. The risk assessment module calculates the comprehensive risk index through a multi-dimensional assessment formula, which can more accurately identify different types of security threats and provide a scientific basis for subsequent early warning mechanisms. This precise assessment capability significantly improves the response speed and effectiveness of the system.

[0084] The multi-level early warning mechanism module divides risks into three levels: low, medium, and high according to the comprehensive risk index, and can implement targeted response strategies. This flexible response method can better meet the security needs under different threat levels and improve the system's resilience. The response strategy generation module formulates corresponding measures according to the early warning level, effectively reducing the reaction time in high-risk situations, launching emergency measures in a timely manner, and reducing potential losses.

[0085] Example 2

[0086] This embodiment is explained in Example 1, please refer to Figure 1 ,Specifically: the data collection module includes a network traffic collection unit, a user behavior collection unit and a device status collection unit;

[0087] The network traffic collection unit captures data packets in real time through a network interface and records network traffic R, including inbound and outbound traffic, bandwidth utilization, and latency;

[0088] The user behavior collection unit records the user's login record, operation log, access frequency and session duration, and generates user behavior data U;

[0089] The device status acquisition unit collects the temperature data, vibration level, current and voltage of the device through the temperature sensor, vibration sensor, current sensor and voltage sensor, and reads the fault code in real time through the self-diagnosis function of the device to form the device status data S;

[0090] The data preprocessing module includes a data cleaning unit and a data standardization unit;

[0091] The data cleaning unit cleans the network traffic R, user behavior data U and device status data S collected from the data acquisition module, removes noise and abnormal values, and obtains the network cleaned traffic R C , user behavior cleaning data U C and equipment status cleaning data S C ;

[0092] The data standardization unit cleans the network flow R C , user behavior cleaning data U C and equipment status cleaning data S C Perform standardization to obtain standardized network traffic R B , Standardized user behavior data U B and standardized equipment status data S B ;

[0093] The normalized network traffic R B , Standardized user behavior data U B and standardized equipment status data S B , obtained by the following formula:

[0094]

[0095] In the formula, μ R , μ U and μ S , represent the mean of network traffic, user behavior data and device status data, σ R , σ U and σ S Represent the standard deviation of network traffic, user behavior data, and device status data respectively.

[0096] The risk assessment module includes a risk index calculation unit, which calculates the risk index based on the standardized network traffic R B , Standardized user behavior data U B and standardized equipment status data S B Calculate the comprehensive risk index W;

[0097] The comprehensive risk index W is obtained by the following formula:

[0098]

[0099] In the formula, ω 1 ,ω 2 and ω 3 Represent the weight coefficients of network traffic, user behavior data and device status data respectively, and satisfy ω 1 +ω 2 +ω 3 =1,maxR B Represents the normalized network traffic R B The peak value of

[0100] The comprehensive risk index W ranges from 0 to 1.

[0101] In this embodiment, the data collection module is subdivided into a network flow collection unit, a user behavior collection unit, and a device status collection unit, making data collection more efficient and accurate. Each unit focuses on a specific data type, which helps to improve the flexibility and adaptability of the overall monitoring system. The network flow collection unit captures data packets in real time to ensure that the in and out of network traffic, bandwidth utilization, and latency can be recorded in real time. This real-time monitoring capability enables the system to quickly respond to potential network security threats and reduce the risk of security incidents.

[0102] The user behavior collection unit can record a variety of user activities, such as login records and session duration, providing a rich source of data for subsequent risk assessment. By analyzing user behavior, abnormal operations can be identified and the security of the system can be enhanced.

[0103] The equipment status acquisition unit monitors the equipment operating status in real time through a variety of sensors, including key indicators such as temperature, vibration, and current. This real-time status monitoring can help detect equipment failures in a timely manner, reduce losses caused by unexpected downtime, and improve production efficiency. The introduction of the data preprocessing module effectively removes noise and outliers through the data cleaning unit to ensure the accuracy of subsequent data analysis. This cleaning process improves the reliability of the data and helps avoid misjudgments and false alarms. The data standardization unit standardizes the cleaned data to ensure comparability between different data sources. This processing method helps to unify the data format and improve the scientific nature of data analysis.

[0104] The risk index calculation unit in the risk assessment module calculates the comprehensive risk index W, combined with standardized data and weight coefficients, to make risk assessment more accurate. This multi-dimensional assessment can effectively identify potential risks and provide support for decision-making. The comprehensive risk index W ranges from 0 to 1, providing a clear quantitative indicator for risk management, allowing managers to intuitively understand the risk level and take corresponding management measures. This quantitative method improves the efficiency and effectiveness of safety management.

[0105] Example 3

[0106] This embodiment is explained in Example 2. Please refer to Figure 1 ,Specifically: the multi-level warning mechanism module includes a warning level determination unit and a warning information generation unit;

[0107] The warning level determination unit determines the risk level according to the comprehensive risk index W and generates a corresponding warning level; the warning level is divided into three levels:

[0108] Low risk: comprehensive risk index W < 0.3;

[0109] Medium risk: 0.3≤comprehensive risk index W<0.7;

[0110] High risk: 0.7≤comprehensive risk index W<1;

[0111] The warning information generation unit generates corresponding warning information and response measures according to the risk level determined by the comprehensive risk index W:

[0112] Low risk: continued monitoring is recommended, no immediate intervention is required;

[0113] Medium risk: Send a warning notification and suggest checking related equipment;

[0114] High risk: Send an urgent response notification, requiring immediate action.

[0115] The response strategy generation module includes a strategy judgment unit and a strategy execution unit;

[0116] The policy judgment unit receives risk level information, including low risk, medium risk and high risk;

[0117] Low-risk output strategy: "Continue monitoring", and continue to monitor system data and regularly update risk assessment reports;

[0118] Medium-risk output strategy: "Notify administrators", which includes sending emails to notify relevant administrators, including risk information and recommendations; pop-up alerts on the system dashboard to display the current risk status; and sending instant messages through internal communication tools to ensure rapid communication of information;

[0119] High-risk output strategy: "Initiate emergency measures", which specifically includes immediately calling the emergency response plan and conducting on-site inspections; launching team emergency drills to ensure that team members understand their respective responsibilities; shutting down affected equipment or systems and isolating them; collecting and recording relevant data on the incident as a basis for subsequent analysis.

[0120] The policy execution unit performs specific operations according to the policy judgment unit to ensure that risks are effectively managed;

[0121] When the output strategy is "Continue Monitoring": it enters the continuous monitoring state to ensure that data collection is constantly updated;

[0122] When the output policy is "Notify Administrator": the administrator is notified by email and the dashboard pops up an alert;

[0123] When the output strategy is "Initiate emergency measures": the emergency response plan starts the preset emergency response process, assigns tasks to various departments, and isolates and shuts down the affected equipment to ensure that it does not affect the normal operation of other systems.

[0124] In this embodiment, a clear early warning mechanism: The multi-level early warning mechanism module clearly divides risks into three levels: low, medium, and high through the early warning level determination unit, providing a simple and clear risk identification system. This classification method helps to quickly respond to different risk levels and ensure that managers can take appropriate measures at the first time.

[0125] The response strategy generated according to the comprehensive risk index W can take corresponding measures for different risk levels. When the risk is low, it is recommended to continue monitoring, when the risk is medium, the administrator is notified immediately, and when the risk is high, emergency measures are initiated. This hierarchical response mechanism improves the flexibility and efficiency of the system and avoids unnecessary intervention.

[0126] In the case of medium-risk situations, administrators are notified through multiple channels, including email, dashboard alerts, and instant messages, ensuring that information can be conveyed quickly. This multi-channel information delivery method reduces communication delays, ensures that relevant personnel can respond in a timely manner, and improves the overall response speed.

[0127] In high-risk situations, the emergency response plan activated includes specific steps such as on-site inspection, team drills and equipment isolation. This systematic approach ensures the comprehensiveness and effectiveness of risk management and reduces potential losses.

[0128] For low-risk situations, the system will enter a continuous monitoring state to ensure real-time data updates. This continuous monitoring helps to promptly detect potential safety hazards and prevent further escalation of risks. High-risk emergency measures emphasize the collection and recording of relevant data on incidents to provide a basis for subsequent analysis. This data-driven approach helps to continuously optimize risk assessment and management strategies and improve the long-term security of the system. In high-risk emergency measures, team emergency drills are initiated to ensure that each member clearly understands their respective responsibilities, thereby improving the team's collaborative efficiency and response capabilities. This training and preparation can enhance the overall safety culture of the organization and improve its ability to handle emergencies.

[0129] Example 4

[0130] This embodiment is explained in Example 3, please refer to Figure 1 ,Specifically: the report generation module includes a report content arrangement unit and a report output unit;

[0131] The report content collating unit collates the content of the monitoring report according to the results of the response strategy generation module, including risk assessment results, early warning information and response measures;

[0132] The risk assessment results include displaying the comprehensive risk index W and risk level;

[0133] The warning information includes recording the warning level and its corresponding specific warning information;

[0134] The response includes a detailed listing of response measures taken and recommendations;

[0135] Monitoring report = risk assessment results + early warning information + response measures.

[0136] The report output unit formats the organized monitoring report content into a readable document according to the monitoring report of the report content organization unit, and outputs it into a specified format including PDF and Word;

[0137] Specifically, it includes formatting the organized content according to preset templates; generating the final report document to ensure that each part of the information is clear and readable; and outputting the report, which supports saving in multiple formats for easy sharing and archiving.

[0138] In this embodiment, the report content arrangement unit provides a systematic reporting framework by integrating risk assessment results, warning information and response measures into a monitoring report. This structured approach makes the information clearer and helps relevant personnel quickly obtain key data and decision-making basis.

[0139] The display of the comprehensive risk index W and risk level in the risk assessment results can intuitively reflect the current safety status and help managers better understand potential risks. This visual information supports more accurate decision-making and reduces the possibility of misjudgment. The early warning information module records the early warning level and its corresponding specific content to ensure that all early warning information is traceable. This transparency can enhance the team's trust and collaboration in the risk management process and provide a valuable data basis for subsequent analysis and summary.

[0140] The response measures section lists in detail the specific measures and suggestions taken, providing managers with clear action guidelines. This level of detail ensures that each department has a clear action plan when facing risks, improving response efficiency and accuracy.

[0141] The report output unit supports formatting monitoring reports into multiple formats such as PDF and Word, which facilitates document sharing and archiving. This flexibility improves the usability of reports, facilitates transmission and use between different platforms, and supports a wider range of communication. By formatting the report content according to the preset template, the uniformity and professionalism of the final report document are ensured. The standardized document format enhances the authority and readability of the report, allowing all parties to quickly understand the report content. Through systematic report generation, it is convenient to compare and analyze historical data later, providing a basis for improving monitoring and response strategies. This long-term data accumulation and analysis helps to continuously optimize the safety performance of the system.

[0142] Example 5

[0143] An intelligent security monitoring and early warning method for industrial control networks, please refer to Figure 2 , specifically: including the following steps:

[0144] Step 1: The data collection module collects network traffic R, user behavior data U and device status data S in real time, where user behavior data U includes login records, operation logs, access frequency and session duration; device status data S includes temperature data, vibration level, current, voltage and fault code;

[0145] Step 2: The data preprocessing module cleans and standardizes the network traffic R, user behavior data U and device status data S collected by the data collection module to obtain the standardized network traffic R B , Standardized user behavior data U B and standardized equipment status data S B ;

[0146] Step 3: The risk assessment module obtains the standardized network traffic R from the data preprocessing module. B , Standardized user behavior data U B and standardized equipment status data S B, use the multi-dimensional evaluation formula to calculate the comprehensive risk index W;

[0147] Step 4: The multi-level early warning mechanism module classifies early warnings according to the comprehensive risk index W. The early warning levels are divided into three levels: low risk, medium risk and high risk;

[0148] Step 5: The response strategy generation module generates corresponding response strategies according to the warning level. Low risk levels continue to be monitored, medium risk levels notify administrators, and high risk levels require emergency measures to be initiated;

[0149] Step 6: The report generation module generates a monitoring report based on the results of the response strategy generation module, including risk assessment results, warning information and response measures.

[0150] In this embodiment, data is captured through network interfaces and sensors, and relevant parameters such as traffic, login status, temperature, vibration, etc. are recorded. The data is stored in raw format for subsequent processing. Noise and outliers are removed during the cleaning process, and statistical methods are used for standardization to ensure data consistency and comparability. Standardized network traffic RB, user behavior data UB, and device status data SB are generated.

[0151] Calculate the comprehensive risk index W, use the preprocessed standardized data, apply the multi-dimensional evaluation formula, comprehensively consider the contribution of different data to the risk, and output the comprehensive risk index W, which ranges from 0 to 1, indicating the size of the potential risk.

[0152] The comprehensive risk index W is graded. The warning level threshold is set to determine the interval of the comprehensive risk index W, and the low risk, medium risk or high risk level is output to provide a basis for subsequent response strategies. Based on the warning level, the corresponding strategy is formulated. Low risk is monitored, medium risk is notified to the administrator, and high risk is initiated to ensure timely response to potential threats.

[0153] The system can acquire and process data in real time to ensure timely detection and response to security threats;

[0154] Each module is seamlessly connected, data flows smoothly, and a complete monitoring and response system is formed;

[0155] Improved efficiency and reduced need for human intervention through automated data collection, processing and report generation;

[0156] The response strategy can be flexibly adjusted according to different risk levels to adapt to various security incidents;

[0157] The generated monitoring reports provide clear information presentation, facilitating managers’ decision-making and subsequent analysis;

[0158] The modular design enables the system to be expanded and upgraded as needed to adapt to the ever-changing security environment.

[0159] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An industrial control network intelligent security monitoring and early warning system, characterized by: It includes data acquisition module, data preprocessing module, risk assessment module, multi-level early warning mechanism module, response strategy generation module and report generation module; The data collection module collects network traffic R, user behavior data U and device status data S in real time, wherein the user behavior data U includes login records, operation logs, access frequency and session duration; Equipment status data S includes temperature data, vibration level, current, voltage and fault code; The data preprocessing module cleans and standardizes the network traffic R, user behavior data U and device status data S collected by the data collection module to obtain the standardized network traffic R B , Standardized user behavior data U B and standardized equipment status data S B ; The risk assessment module obtains the standardized network traffic R according to the data preprocessing module. B , Standardized user behavior data U B and standardized equipment status data S B , use the multi-dimensional evaluation formula to calculate the comprehensive risk index W; The multi-level early warning mechanism module performs early warning classification according to the comprehensive risk index W, and the early warning level is divided into three levels: low risk, medium risk and high risk; The response strategy generation module generates a corresponding response strategy according to the warning level. Low risk levels continue to be monitored, medium risk levels notify administrators, and high risk levels require emergency measures to be initiated; The report generation module generates a monitoring report according to the result of the response strategy generation module, including risk assessment results, early warning information and response measures.

2. The industrial control network intelligent security monitoring and early warning system according to claim 1 is characterized by: The data collection module includes a network traffic collection unit, a user behavior collection unit and a device status collection unit; The network traffic collection unit captures data packets in real time through a network interface and records network traffic R, including inbound and outbound traffic, bandwidth utilization, and latency; The user behavior collection unit records the user's login record, operation log, access frequency and session duration, and generates user behavior data U; The device status acquisition unit collects the temperature data, vibration level, current and voltage of the device through temperature sensors, vibration sensors, current sensors and voltage sensors, and reads the fault code in real time through the self-diagnosis function of the device to form the device status data S.

3. The industrial control network intelligent security monitoring and early warning system according to claim 1 is characterized by: The data preprocessing module includes a data cleaning unit and a data standardization unit; The data cleaning unit cleans the network traffic R, user behavior data U and device status data S collected from the data acquisition module, removes noise and abnormal values, and obtains the network cleaned traffic R C , user behavior cleaning data U C and equipment status cleaning data S C ; The data standardization unit cleans the network flow R C , user behavior cleaning data U C and equipment status cleaning data S C Perform standardization to obtain standardized network traffic R B , Standardized user behavior data U B and standardized equipment status data S B ; The normalized network traffic R B , Standardized user behavior data U B and standardized equipment status data S B , obtained by the following formula: In the formula, μ R , μ U and μ S , represent the mean of network traffic, user behavior data and device status data, σ R , σ U and σ S Represent the standard deviation of network traffic, user behavior data, and device status data respectively.

4. The industrial control network intelligent security monitoring and early warning system according to claim 1 is characterized by: The risk assessment module includes a risk index calculation unit, which calculates the risk index based on the standardized network traffic R B , Standardized user behavior data U B and standardized equipment status data S B Calculate the comprehensive risk index W; The comprehensive risk index W is obtained by the following formula: Where ω1, ω2, and ω3 represent the weight coefficients of network traffic, user behavior data, and device status data, respectively, and satisfy ω1+ω2+ω3=1, maxR B Represents the normalized network traffic R B The peak value of The comprehensive risk index W ranges from 0 to 1.

5. The industrial control network intelligent security monitoring and early warning system according to claim 1 is characterized by: The multi-level warning mechanism module includes a warning level determination unit and a warning information generation unit; The warning level determination unit determines the risk level according to the comprehensive risk index W and generates a corresponding warning level; There are three levels of warning: Low risk: comprehensive risk index W < 0.3; Medium risk: 0.3≤comprehensive risk index W<0.7; High risk: 0.7≤comprehensive risk index W<1; The warning information generation unit generates corresponding warning information and response measures according to the risk level determined by the comprehensive risk index W: Low risk: continued monitoring is recommended, no immediate intervention is required; Medium risk: Send a warning notification and suggest checking related equipment; High risk: Send an urgent response notification, requiring immediate action.

6. The industrial control network intelligent security monitoring and early warning system according to claim 1 is characterized by: The response strategy generation module includes a strategy judgment unit and a strategy execution unit; The policy judgment unit receives risk level information, including low risk, medium risk and high risk; Low-risk output strategy: "Continue monitoring", and continue to monitor system data and regularly update risk assessment reports; Medium-risk output strategy: "Notify administrator", which specifically includes sending emails to notify relevant administrators, including risk information and recommendations; Pop-up alerts on the system dashboard to display current risk status; send instant messages through internal communication tools to ensure rapid communication of information; High-risk output strategy: "Initiate emergency measures", which includes immediately calling the emergency response plan and conducting on-site inspections; launching team emergency drills to ensure that team members understand their respective responsibilities; shutting down affected equipment or systems and isolating them; Collect and record relevant data of the incident as a basis for subsequent analysis.

7. The industrial control network intelligent security monitoring and early warning system according to claim 6 is characterized by: The policy execution unit performs specific operations according to the policy judgment unit to ensure that risks are effectively managed; When the output strategy is "Continue Monitoring": it enters the continuous monitoring state to ensure that data collection is constantly updated; When the output policy is "Notify Administrator": the administrator is notified by email and the dashboard pops up an alert; When the output strategy is "Initiate emergency measures": the emergency response plan starts the preset emergency response process, assigns tasks to various departments, and isolates and shuts down the affected equipment to ensure that the normal operation of other systems is not affected.

8. The industrial control network intelligent security monitoring and early warning system according to claim 1 is characterized by: The report generation module includes a report content arrangement unit and a report output unit; The report content collating unit collates the content of the monitoring report according to the results of the response strategy generation module, including risk assessment results, early warning information and response measures; The risk assessment results include displaying the comprehensive risk index W and risk level; The warning information includes recording the warning level and its corresponding specific warning information; The response includes a detailed listing of response measures taken and recommendations; Monitoring report = risk assessment results + early warning information + response measures.

9. The industrial control network intelligent security monitoring and early warning system according to claim 8 is characterized by: The report output unit formats the organized monitoring report content into a readable document according to the monitoring report of the report content organization unit, and outputs it into a specified format including PDF and Word; Specifically, it includes formatting the organized content according to preset templates; generating the final report document to ensure that each part of the information is clear and readable; and outputting the report, which supports saving in multiple formats for easy sharing and archiving.

10. An industrial control network intelligent security monitoring and early warning system, comprising an industrial control network intelligent security monitoring and early warning method according to any one of claims 1 to 9, characterized in that: The following steps are involved: Step 1: The data collection module collects network traffic R, user behavior data U and device status data S in real time, where user behavior data U includes login records, operation logs, access frequency and session duration; device status data S includes temperature data, vibration level, current, voltage and fault code; Step 2: The data preprocessing module cleans and standardizes the network traffic R, user behavior data U and device status data S collected by the data collection module to obtain the standardized network traffic R B , Standardized user behavior data U B and standardized equipment status data S B ; Step 3: The risk assessment module obtains the standardized network traffic R from the data preprocessing module. B , Standardized user behavior data U B and standardized equipment status data S B , use the multi-dimensional evaluation formula to calculate the comprehensive risk index W; Step 4: The multi-level early warning mechanism module classifies early warnings according to the comprehensive risk index W. The early warning levels are divided into three levels: low risk, medium risk and high risk; Step 5: The response strategy generation module generates corresponding response strategies according to the warning level. Low risk levels continue to be monitored, medium risk levels notify administrators, and high risk levels require emergency measures to be initiated; Step 6: The report generation module generates a monitoring report based on the results of the response strategy generation module, including risk assessment results, warning information and response measures.

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