An industrial host network security operation and maintenance monitoring system

Abstract

The present invention discloses an industrial host network security operation and maintenance monitoring system, which relates to the field of network security technology. The industrial host network security operation and maintenance monitoring system comprises a data acquisition module, the data acquisition module is connected to a data preprocessing module, the data preprocessing module is connected to a dynamic behavior modeling module, the dynamic behavior modeling module is connected to an intelligent correlation analysis module, the intelligent correlation analysis module is connected to a predictive defense module, the predictive defense module is connected to a self-learning defense strategy module, the self-learning defense strategy module is connected to a distributed monitoring and response module, and the distributed monitoring and response module is connected to an intelligent alarm classification module. The present invention significantly improves the efficiency and system security of industrial host network security operation and maintenance monitoring through multi-dimensional comprehensive monitoring, dynamic behavior modeling, intelligent correlation analysis, predictive defense, self-learning and self-adaptation, and multi-team collaborative management.

Description

Technical Field

[0001] The present invention relates to the technical field of network security, and in particular to an industrial host network security operation and maintenance monitoring system. Background Art

[0002] Industrial hosts refer to computer hosts or servers used in industrial control systems. They are usually used to run key industrial applications such as industrial automation software, monitoring systems, supervisory control and data acquisition systems (SCADA), and programmable logic controllers (PLCs). Industrial hosts play a core role in the industrial production process, responsible for tasks such as data processing, control logic execution, and system management. They usually require high reliability and high security, because any failure or security issue may directly affect the production process and even cause serious safety accidents. It is necessary to conduct network security operation and maintenance monitoring of industrial hosts for reasons including ensuring production safety, ensuring the normal operation of production facilities, preventing data leakage, protecting sensitive industrial data, improving response speed, quickly discovering and responding to security incidents, reducing the exposure time of security vulnerabilities, and reducing losses.

[0003] The existing methods for industrial host network security operation and maintenance monitoring include using traditional security equipment (such as firewalls, intrusion detection systems (IDS), intrusion prevention systems (IPS)) for basic network monitoring and protection, collecting and analyzing system logs through log management tools (such as ELK Stack, Splunk) to discover abnormal behavior, using endpoint protection software (such as EDR, AV) to protect industrial hosts from malware attacks, using tools such as NetFlow and sFlow to monitor network traffic to discover abnormal traffic and potential network attacks, and regular manual inspections by operation and maintenance personnel to check system status and security settings. In the existing technology, traditional security equipment relies on pre-defined rules and signatures, and has limited defense capabilities against new threats and unknown attacks; existing monitoring tools are usually independent of each other, making it difficult to achieve comprehensive analysis and association of multi-source data; manual inspections are time-consuming and labor-intensive and prone to omissions, and cannot detect and respond to security incidents in a timely manner; log analysis and network traffic monitoring are mainly based on historical data, making it difficult to achieve real-time threat detection and response; existing monitoring tools are difficult to dynamically adjust monitoring and defense strategies according to actual conditions. Summary of the invention

[0004] In view of the defects in the prior art, the present invention provides an industrial host network security operation and maintenance monitoring system, including a data acquisition module, the data acquisition module is connected to a data preprocessing module, the data preprocessing module is connected to a dynamic behavior modeling module, the dynamic behavior modeling module is connected to an intelligent correlation analysis module, the intelligent correlation analysis module is connected to a predictive defense module, the predictive defense module is connected to a self-learning defense strategy module, the self-learning defense strategy module is connected to a distributed monitoring and response module, the distributed monitoring and response module is connected to an intelligent alarm classification module, the intelligent alarm classification module is connected to a multi-team collaborative management module, and the multi-team collaborative management module is connected to a user feedback loop module;

[0005] The data acquisition module is used to collect data from multiple sources such as host, network, physical environment and operation log. The data preprocessing module is used to clean, convert and standardize the collected raw data to ensure the consistency and quality of the data. The dynamic behavior modeling module is used to use machine learning algorithms to establish and update dynamic behavior models, detect activities that deviate from normal behavior, and trigger alarms.

[0006] Preferably, the data acquisition module includes a host data acquisition submodule, a network data acquisition submodule, a physical environment data acquisition submodule and an operation log acquisition submodule. The host data acquisition submodule collects the host's CPU, memory, disk usage, process and port status data through SNMP, Syslog and WMI protocols. The network data acquisition submodule collects network traffic, protocol and connection status data through NetFlow and sFlow protocols. The physical environment data acquisition submodule collects temperature, humidity and vibration physical environment data through sensors. The operation log acquisition submodule collects system operation logs and application logs through log management tools (such as ELK Stack).

[0007] Preferably, the intelligent association analysis module includes a multi-source data fusion submodule and a threat identification submodule. The multi-source data fusion submodule is used to align and merge data from different sources into a comprehensive data set, and the threat identification submodule is used to mine association rules between data through an association rule learning algorithm Apriori algorithm to identify potential security threats.

[0008] Preferably, the predictive defense module includes a time series analysis submodule and a predictive measures submodule. The time series analysis submodule is used to analyze historical alarm data and attack pattern data to predict possible future attack behaviors. The predictive measures submodule is used to generate and implement predictive measures based on the prediction results, such as closing certain ports and isolating certain hosts.

[0009] Preferably, the self-learning defense strategy module includes a user feedback loop submodule and a model optimization submodule. The user feedback loop submodule is used to collect user feedback information through forms and APIs, and the model optimization submodule is used to adjust monitoring and defense strategies and optimize models based on user feedback information and historical data analysis results.

[0010] Preferably, the distributed monitoring and response module includes a node deployment submodule and a preliminary response submodule. The node deployment submodule is used to deploy monitoring and response containers on key nodes and configure monitoring and response tasks for each node. The preliminary response submodule is used to define preliminary response rules for each node. When a node detects abnormal behavior, the preliminary response rules are executed and the response results are reported to the central management platform.

[0011] Preferably, the intelligent alarm classification module includes a threat assessment submodule and a policy matching submodule. The threat assessment submodule is used to define and execute threat assessment through rules to determine the severity and scope of impact of the alarm. The policy matching submodule is used to match corresponding response strategies from the policy library according to different alarm levels and execute the matching response strategies.

[0012] Preferably, the multi-team collaborative management module includes a task assignment submodule, a workflow management submodule and a cross-departmental collaboration submodule. The task assignment submodule is used to generate tasks according to the alarm level and type, and assign them to the corresponding security team or automation module. The workflow management submodule is used to manage the efficient flow of tasks and conduct cross-team communication and collaboration through a collaboration platform. The cross-departmental collaboration submodule is used to start the corresponding collaboration process when cross-departmental collaboration is required to ensure that each department can respond in a timely manner.

[0013] The beneficial effects of the present invention are embodied in:

[0014] The multi-dimensional comprehensive monitoring function not only covers the monitoring of hosts and networks, but also combines physical environment data and operation logs to form a comprehensive data view, thereby improving the accuracy of threat detection. Secondly, the dynamic behavior model established by the machine learning algorithm can capture the normal behavior pattern of the host and detect behaviors that deviate from the normal pattern to achieve more accurate anomaly detection. Intelligent association analysis uses multi-source data fusion and association rule learning technology to discover potential compound threats and improve the system's threat identification ability. Predictive defense uses time series analysis and behavior prediction algorithms to identify possible attack behaviors in advance, take predictive defense measures, and improve the system's active defense capabilities. Self-learning and self-adaptation Through user feedback and historical data analysis, the system can automatically optimize monitoring and defense strategies to ensure continuous improvement and adaptability of the system. Multi-team collaborative management supports collaborative work between different security teams and departments. Through task allocation and workflow management, it ensures that each alarm can be handled in a timely and effective manner. Users participate in establishing a user feedback mechanism and a training and exchange platform to improve the safety awareness and skills of operation and maintenance personnel, forming an ecological environment for continuous learning and improvement, and can more efficiently and intelligently carry out network security operation and maintenance monitoring of industrial hosts, significantly improving the security and operation and maintenance efficiency of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following is a brief introduction to the drawings required for the specific embodiments or the description of the prior art. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn according to the actual scale.

[0016] Figure 1 This is a schematic diagram of the system framework. DETAILED DESCRIPTION

[0017] The following embodiments of the technical solution of the present invention are described in detail in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and are therefore only used as examples, and cannot be used to limit the protection scope of the present invention.

[0018] It should be noted that, unless otherwise specified, the technical terms or scientific terms used in this application should have the common meanings understood by those skilled in the art to which the invention belongs.

[0019] like Figure 1As shown, an embodiment of the present invention provides an industrial host network security operation and maintenance monitoring system, including a data acquisition module, the data acquisition module is connected to a data preprocessing module, the data preprocessing module is connected to a dynamic behavior modeling module, the dynamic behavior modeling module is connected to an intelligent correlation analysis module, the intelligent correlation analysis module is connected to a predictive defense module, the predictive defense module is connected to a self-learning defense strategy module, the self-learning defense strategy module is connected to a distributed monitoring and response module, the distributed monitoring and response module is connected to an intelligent alarm classification module, the intelligent alarm classification module is connected to a multi-team collaborative management module, and the multi-team collaborative management module is connected to a user feedback loop module;

[0020] The data acquisition module is used to collect data from multiple sources such as hosts, networks, physical environments, and operation logs. The data preprocessing module is used to clean, convert, and standardize the collected raw data to ensure data consistency and quality. The dynamic behavior modeling module is used to use machine learning algorithms to establish and update dynamic behavior models, detect activities that deviate from normal behavior, and trigger alarms.

[0021] The data collection module includes a host data collection submodule, a network data collection submodule, a physical environment data collection submodule and an operation log collection submodule. The host data collection submodule collects the host's CPU, memory, disk usage, process and port status data through SNMP, Syslog and WMI protocols. The network data collection submodule collects network traffic, protocol and connection status data through NetFlow and sFlow protocols. The physical environment data collection submodule collects temperature, humidity and vibration physical environment data through sensors. The operation log collection submodule collects system operation logs and application logs through log management tools (such as ELK Stack).

[0022] The intelligent association analysis module includes a multi-source data fusion submodule and a threat identification submodule. The multi-source data fusion submodule is used to align and merge data from different sources into a comprehensive data set. The threat identification submodule is used to mine association rules between data through the association rule learning algorithm Apriori algorithm to identify potential security threats.

[0023] The predictive defense module includes a time series analysis submodule and a predictive measures submodule. The time series analysis submodule is used to analyze historical alarm data and attack pattern data to predict possible future attack behaviors. The predictive measures submodule is used to generate and implement predictive measures based on the prediction results, such as closing certain ports and isolating certain hosts.

[0024] The self-learning defense strategy module includes a user feedback loop submodule and a model optimization submodule. The user feedback loop submodule is used to collect user feedback information through forms and APIs. The model optimization submodule is used to adjust monitoring and defense strategies and optimize models based on user feedback information and historical data analysis results.

[0025] The distributed monitoring and response module includes a node deployment submodule and a preliminary response submodule. The node deployment submodule is used to deploy monitoring and response containers on key nodes and configure monitoring and response tasks for each node. The preliminary response submodule is used to define the preliminary response rules for each node. When a node detects abnormal behavior, it executes the preliminary response rules and reports the response results to the central management platform.

[0026] The intelligent alarm classification module includes a threat assessment submodule and a policy matching submodule. The threat assessment submodule is used to define and execute threat assessment through rules to determine the severity and impact scope of the alarm. The policy matching submodule is used to match the corresponding response strategy from the policy library according to different alarm levels and execute the matching response strategy.

[0027] The multi-team collaborative management module includes a task allocation submodule, a workflow management submodule and a cross-department collaboration submodule. The task allocation submodule is used to generate tasks according to the alarm level and type, and assign them to the corresponding security team or automation module. The workflow management submodule is used to manage the efficient flow of tasks and conduct cross-team communication and collaboration through the collaboration platform. The cross-department collaboration submodule is used to start the corresponding collaboration process when cross-department collaboration is required to ensure that each department can respond in a timely manner.

[0028] The user feedback loop module includes a feedback channel submodule and a feedback processing submodule. The feedback channel submodule is used to provide multiple feedback channels, such as online forms and instant messaging. The feedback processing submodule is used to process user feedback information through data cleaning and feature extraction techniques, and adjust monitoring and defense strategies based on the extracted features.

[0029] Specifically: Host data collection uses SNMP, Syslog, and WMI protocols to collect the host's CPU, memory, disk usage, process, and port status. Network data collection uses NetFlow and sFlow protocols to collect network traffic, protocols, and connection status. Physical environment data collection uses sensors to collect temperature, humidity, and vibration physical environment data. Operation log collection uses the log management tool ELK Stack to collect system operation logs and application logs.

[0030] Install and configure SNMP, Syslog, WMI, and NetFlow tools on each monitoring node. Deploy temperature, humidity, and vibration sensors at key locations. Configure the ELK Stack log management tool to ensure real-time collection and storage of operation logs. Transmit the collected data to the central data processing platform via a secure transmission channel HTTPS.

[0031] Use the data cleaning tool Pandas to remove invalid and erroneous data. Convert data in different formats into a unified format. Standardize the data to ensure consistency and comparability.

[0032] Remove invalid and erroneous data, such as duplicate records and missing values. Convert data in different formats, such as logs and sensor data, into a unified format. Standardize the data, such as scaling and normalization. Store the preprocessed data in a central database such as MySQL or MongoDB for subsequent analysis.

[0033] Use the unsupervised learning algorithms K-means and GMM to build the initial behavior model. Use the online learning algorithm Mini-Batch K-means to update the behavior model in real time. Use the anomaly detection algorithms Isolation Forest and LOF to detect activities that deviate from normal behavior.

[0034] Get historical data from the central database. Use unsupervised learning algorithms to train the initial behavior model. Save the trained model to the model library. Use stream processing frameworks such as Apache Kafka and Apache Storm to process real-time data. Use online learning algorithms to update the behavior model in real time. Manage different versions of the model to ensure the continuity and traceability of the model.

[0035] Use anomaly detection algorithms to detect abnormal behavior in real-time data. When abnormal behavior is detected, an alarm is triggered and detailed information is recorded. The alarm information is saved in the alarm library for subsequent processing.

[0036] Use data fusion technology and multi-source data fusion algorithms to fuse data from different sources. Use association rule learning Apriori algorithm to discover the association relationship between data. Align data from different sources to the same timeline. Merge the aligned data into a comprehensive data set. Use association rule learning algorithms to mine association rules between data. Identify potential security threats based on the mined association rules.

[0037] Use time series analysis algorithms ARIMA and LSTM to predict possible future attack behaviors. Use automated scripts and policy management tools to implement predictive measures.

[0038] Obtain historical alarm data and attack pattern data from the central database. Use time series analysis algorithms to train prediction models. Save the trained prediction models to the model library. Use the prediction models to predict possible future attack behaviors. Generate predictive measures based on the prediction results, such as closing certain ports, isolating certain hosts, etc. Implement predictive measures through automated scripts and policy management tools.

[0039] Use the user feedback system form and API to collect user feedback. Use the reinforcement learning algorithm Q-Learning to optimize defense strategies.

[0040] Provide multiple feedback channels, such as online forms and instant messaging. Collect user feedback information through forms and APIs. Preprocess the collected feedback information and extract useful features. Use reinforcement learning algorithms to evaluate the effectiveness of the current defense strategy. Adjust the defense strategy and optimize the model based on the evaluation results.

[0041] Use containerization technologies such as Docker and Kubernetes to deploy monitoring nodes. Use automated response tools such as Ansible and Puppet to implement initial responses. Deploy monitoring and response containers on key nodes. Configure monitoring and response tasks for each node.

[0042] Define the initial response rules for each node, such as isolating the host, closing the network connection, etc. When the node detects abnormal behavior, execute the initial response rules. Report the response results to the central management platform.

[0043] Use a rule-based threat assessment model to assess the severity and impact of alerts. Match different response strategies based on alert levels. Define assessment rules based on the type, severity, and impact of threats. Evaluate each alert to determine its alert level. Build a response strategy library for different alert levels. Match the corresponding response strategy based on the assessment results. Execute the matched response strategy, such as sending alert notifications, initiating defense measures, etc.

[0044] Use task management tools such as Jira and Trello to assign tasks. Use workflow management tools such as Confluence and Slack to manage the flow of tasks. Generate tasks based on alert levels and types. Assign tasks to the corresponding security teams or automation modules. Ensure efficient flow of tasks through workflow management tools. Use collaboration platforms such as Slack and Confluence for cross-team communication and collaboration.

[0045] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and specification of the present invention.

Claims

1. An industrial host network security operation and maintenance monitoring system, including a data acquisition module, characterized in that: The data acquisition module is connected to a data preprocessing module, the data preprocessing module is connected to a dynamic behavior modeling module, the dynamic behavior modeling module is connected to an intelligent association analysis module, the intelligent association analysis module is connected to a predictive defense module, the predictive defense module is connected to a self-learning defense strategy module, the self-learning defense strategy module is connected to a distributed monitoring and response module, the distributed monitoring and response module is connected to an intelligent alarm classification module, the intelligent alarm classification module is connected to a multi-team collaborative management module, and the multi-team collaborative management module is connected to a user feedback loop module; The data acquisition module is used to collect data from multiple sources such as host, network, physical environment and operation log. The data preprocessing module is used to clean, convert and standardize the collected raw data. The dynamic behavior modeling module is used to use machine learning algorithms to establish and update dynamic behavior models, detect activities that deviate from normal behavior, and trigger alarms.

2. The industrial host network security operation and maintenance monitoring system according to claim 1 is characterized by: The data acquisition module includes a host data acquisition submodule, a network data acquisition submodule, a physical environment data acquisition submodule and an operation log acquisition submodule. The host data acquisition submodule collects the host's CPU, memory, disk usage, process and port status data through SNMP, Syslog and WMI protocols. The network data acquisition submodule collects network traffic, protocol and connection status data through NetFlow and sFlow protocols. The physical environment data acquisition submodule collects temperature, humidity and vibration physical environment data through sensors.

3. The industrial host network security operation and maintenance monitoring system according to claim 1 is characterized by: The intelligent association analysis module includes a multi-source data fusion submodule and a threat identification submodule. The multi-source data fusion submodule is used to align and merge data from different sources into a comprehensive data set, and the threat identification submodule is used to mine association rules between data through the association rule learning algorithm Apriori algorithm to identify potential security threats.

4. The industrial host network security operation and maintenance monitoring system according to claim 1 is characterized by: The predictive defense module includes a time series analysis submodule and a prejudgment measure submodule. The time series analysis submodule is used to analyze historical alarm data and attack pattern data to predict possible future attack behaviors. The prejudgment measure submodule is used to generate and implement prejudgment measures based on the prediction results.

5. The industrial host network security operation and maintenance monitoring system according to claim 1 is characterized by: The self-learning defense strategy module includes a user feedback loop submodule and a model optimization submodule. The user feedback loop submodule is used to collect user feedback information through forms and APIs, and the model optimization submodule is used to adjust monitoring and defense strategies and optimize models based on user feedback information and historical data analysis results.

6. The industrial host network security operation and maintenance monitoring system according to claim 1 is characterized by: The distributed monitoring and response module includes a node deployment submodule and a preliminary response submodule. The node deployment submodule is used to deploy monitoring and response containers on key nodes and configure monitoring and response tasks for each node. The preliminary response submodule is used to define preliminary response rules for each node. When a node detects abnormal behavior, the preliminary response rules are executed and the response results are reported to the central management platform.

7. The industrial host network security operation and maintenance monitoring system according to claim 1 is characterized by: The intelligent alarm classification module includes a threat assessment submodule and a strategy matching submodule. The threat assessment submodule is used to define and execute threat assessment through rules to determine the severity and impact scope of the alarm. The strategy matching submodule is used to match corresponding response strategies from the strategy library according to different alarm levels and execute the matched response strategies.

8. The industrial host network security operation and maintenance monitoring system according to claim 1 is characterized by: The multi-team collaborative management module includes a task allocation submodule, a workflow management submodule and a cross-department collaboration submodule. The task allocation submodule is used to generate tasks according to the alarm level and type, and assign them to the corresponding security team or automation module. The workflow management submodule is used to manage the efficient flow of tasks and conduct cross-team communication and collaboration through a collaboration platform. The cross-department collaboration submodule is used to start the corresponding collaboration process when cross-department collaboration is required to ensure that each department can respond in a timely manner.

Images