Industrial control system intrusion detection system based on AI

Abstract

The invention provides an industrial control system intrusion detection system based on AI, and belongs to the technical field of industrial control system safety. Comprising a data acquisition module, a data preprocessing module, an AI model training module, an intrusion detection module, a response module and a user interaction and management interface, wherein the data acquisition module is responsible for acquiring network flow, equipment state and operation log data from each node of an industrial control system; the data preprocessing module is used for carrying out preprocessing operations of cleaning, normalization and feature extraction on the acquired data; the AI model training module uses the preprocessed data to train a machine learning or deep learning model; the intrusion detection module analyzes the current system state in real time, and performs anomaly detection and intrusion identification by using a trained AI model; the method has the real-time performance, the accuracy, the self-adaptability and the automatic response capability, and the safety of the industrial control system can be effectively improved.

Description

Technical Field

[0001] The present invention provides an AI-based industrial control system intrusion detection system, which belongs to the technical field of industrial control system security. Background Art

[0002] With the rapid development of Industry 4.0 and intelligent manufacturing, the security of industrial control systems (ICS) has become increasingly important. Traditional intrusion detection systems (IDS) often have difficulty effectively identifying and defending against new attacks in complex industrial network environments, and their accuracy is low and their real-time performance is poor, which cannot effectively improve the security of industrial control systems.

[0003] Therefore, in view of this, the existing structure is studied and improved, and an AI-based industrial control system intrusion detection system is proposed to solve the above-mentioned problems. Summary of the invention

[0004] The technical problem to be solved by the present invention is an AI-based industrial control system intrusion detection system, which has real-time, accuracy, adaptability and automatic response capabilities, and can effectively improve the security of industrial control systems.

[0005] In order to solve the above problems, the technical solution proposed in the present invention is: an AI-based industrial control system intrusion detection system, including a data acquisition module, a data preprocessing module, an AI model training module, an intrusion detection module, a response module, and a user interaction and management interface.

[0006] The data acquisition module is responsible for collecting network traffic, device status, and operation log data from each node of the industrial control system;

[0007] The data preprocessing module performs preprocessing operations of cleaning, normalizing, and feature extraction on the collected data;

[0008] The AI ​​model training module uses the preprocessed data to train the machine learning or deep learning model;

[0009] The intrusion detection module uses real-time analysis of the current system status and uses trained AI models for anomaly detection and intrusion identification;

[0010] When the response module detects an intrusion, it automatically triggers corresponding defense measures, including isolating infected devices and sending alarms;

[0011] The user interaction and management interface includes a visual monitoring platform and reporting and analysis tools.

[0012] Furthermore, the data acquisition module collects network traffic, device status, and operation log data in real time through sensors and monitoring software deployed in the industrial control system.

[0013] Furthermore, the data preprocessing module needs to cooperate with the anomaly detection algorithm to automatically identify and process abnormal or missing data, improve data quality, and adopt automated feature selection and extraction technology, and feature selection based on genetic algorithms to improve the efficiency and accuracy of model training.

[0014] Furthermore, the AI ​​model training module uses the preprocessed data to train the AI ​​model, improves the accuracy and generalization ability of the model through cross-validation and hyperparameter tuning methods, and uses the online learning mechanism to realize online learning and updating of the model, so that it can adapt to new threats and attack patterns in real time.

[0015] Furthermore, the intrusion detection module inputs the real-time collected data into the trained AI model to perform anomaly detection and intrusion identification, introduces user and entity behavior analysis technology, identifies abnormal activities by learning normal behavior patterns, integrates external threat intelligence, enhances the system's threat detection capabilities, and achieves effective response to known and unknown threats.

[0016] Furthermore, when the intrusion detection module detects an intrusion, the system can not only automatically isolate the infected device, but also try to automatically repair or restore the device to a safe state. According to the threat level and system status, the security policy and access control rules can be dynamically adjusted to achieve more flexible security management.

[0017] Furthermore, the response module automatically triggers corresponding defense measures based on the detection results. When an intrusion is detected, the system can not only automatically isolate the infected device, but also try to automatically repair or restore the device to a safe state, and dynamically adjust security policies and access control rules according to the threat level and system status to achieve more flexible security management.

[0018] Furthermore, the visual monitoring platform provides an intuitive monitoring interface that displays system status, threat alarms, and response actions, making it easier for operation and maintenance personnel to quickly understand and handle security incidents.

[0019] Furthermore, the reporting and analysis tool generates detailed security reports and analysis charts to help users evaluate the security status of the system and formulate corresponding security strategies.

[0020] Due to the adoption of the above technical solution, the beneficial effects of the AI-based industrial control system intrusion detection system of the present invention are as follows:

[0021] 1. The present invention can monitor the status of industrial control systems in real time, detect and respond to intrusion behaviors in a timely manner, and has real-time performance.

[0022] 2. The present invention can effectively identify complex intrusion behaviors through AI technology, reduce false positives and false negatives, and has accuracy.

[0023] 3. The present invention can self-update and optimize according to new data and attack patterns through the AI ​​model, thereby improving the system's defense capabilities and having strong adaptability.

[0024] 4. The present invention can automatically trigger defense measures, reduce manual intervention and improve response speed. DETAILED DESCRIPTION

[0025] The following will be described clearly and completely in conjunction with the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0026] The present invention provides an AI-based industrial control system intrusion detection system, which includes a data acquisition module, a data preprocessing module, an AI model training module, an intrusion detection module, a response module, and a user interaction and management interface.

[0027] The data acquisition module is responsible for collecting network traffic, device status, and operation log data from each node of the industrial control system;

[0028] The data preprocessing module performs preprocessing operations of cleaning, normalizing, and feature extraction on the collected data;

[0029] The AI ​​model training module uses the preprocessed data to train the machine learning or deep learning model;

[0030] The intrusion detection module uses real-time analysis of the current system status and uses trained AI models for anomaly detection and intrusion identification;

[0031] When the response module detects an intrusion, it automatically triggers corresponding defense measures, including isolating infected devices and sending alarms;

[0032] The user interaction and management interface includes a visual monitoring platform and reporting and analysis tools.

[0033] The data acquisition module collects network traffic, device status, and operation log data in real time through sensors and monitoring software deployed in the industrial control system.

[0034] The data preprocessing module needs to cooperate with the anomaly detection algorithm to automatically identify and process abnormal or missing data, improve data quality, and adopt automated feature selection and extraction technology, based on genetic algorithm feature selection, to improve the efficiency and accuracy of model training.

[0035] The AI ​​model training module uses the preprocessed data to train the AI ​​model, improves the accuracy and generalization ability of the model through cross-validation and hyperparameter tuning methods, and uses the online learning mechanism to realize online learning and updating of the model, so that it can adapt to new threats and attack patterns in real time.

[0036] The intrusion detection module inputs the real-time collected data into the trained AI model to perform anomaly detection and intrusion identification, introduces user and entity behavior analysis technology, identifies abnormal activities by learning normal behavior patterns, integrates external threat intelligence, enhances the system's threat detection capabilities, and achieves effective response to known and unknown threats.

[0037] When the intrusion detection module detects an intrusion, the system can not only automatically isolate the infected device, but also try to automatically repair or restore the device to a safe state. According to the threat level and system status, the security policy and access control rules are dynamically adjusted to achieve more flexible security management.

[0038] The response module automatically triggers corresponding defense measures based on the detection results. When an intrusion is detected, the system can not only automatically isolate the infected device, but also try to automatically repair or restore the device to a safe state. It can also dynamically adjust security policies and access control rules based on the threat level and system status to achieve more flexible security management.

[0039] The visual monitoring platform provides an intuitive monitoring interface that displays system status, threat alarms, and response actions, making it easy for operation and maintenance personnel to quickly understand and handle security incidents.

[0040] The reporting and analysis tool generates detailed security reports and analysis charts to help users evaluate the security status of the system and formulate corresponding security strategies.

[0041] To sum up, the specific steps are as follows:

[0042] S1. Determine the network structure and key nodes of the industrial control system, install sensors and monitoring software, ensure the comprehensiveness and real-time nature of data collection, deploy an intrusion detection system on a server or dedicated device, configure the data collection module, connect sensors and monitoring software; and set data collection parameters, such as sampling frequency, data storage path, etc., and configure access rights and initial settings for the user interaction and management interface.

[0043] S2. Start the data collection module to collect network traffic, device status, operation log and other data from each node in real time, monitor the data collection status, and ensure the integrity and real-time nature of the data.

[0044] S3. Run the data preprocessing module to clean, normalize, and extract features of the collected data. Use the anomaly detection algorithm to automatically identify and process abnormal or missing data to improve data quality. Use automated feature selection and extraction technology to optimize the efficiency and accuracy of model training.

[0045] S4. Use preprocessed data to train AI models, select appropriate machine learning or deep learning algorithms, and improve the accuracy and generalization ability of the models through cross-validation and hyperparameter tuning. Enable online learning mechanisms to enable the models to adapt to new threats and attack patterns in real time, regularly evaluate model performance, and update and optimize when necessary.

[0046] S5. Input the real-time collected data into the trained AI model for anomaly detection and intrusion identification, introduce user and entity behavior analysis technology, identify abnormal activities, and integrate external threat intelligence; when an intrusion is detected, automatically trigger the isolation of infected devices, repair or restore the device to a safe state, and dynamically adjust security policies and access control rules according to the threat level and system status.

[0047] S6. Log in to the user interaction and management interface, use the visual monitoring platform, view the system status, threat alarms and response actions, quickly understand and handle security incidents, use reporting and analysis tools to generate detailed security reports and analysis charts, evaluate the system security status, formulate corresponding security strategies and improvement measures, and regularly check the system operation status to ensure that each module is working properly, maintain sensors and monitoring software, and update drivers and firmware.

[0048] The present invention and its implementation methods are described above, and such description is not restrictive. In short, if a person skilled in the art is inspired by it and, without departing from the purpose of the invention, designs structures and embodiments similar to the technical solution without creativity, they should all fall within the protection scope of the present invention.

Claims

1. An AI-based industrial control system intrusion detection system, characterized in that: Including data acquisition module, data preprocessing module, AI model training module, intrusion detection module, response module, user interaction and management interface, The data acquisition module is responsible for collecting network traffic, device status, and operation log data from each node of the industrial control system; The data preprocessing module performs preprocessing operations of cleaning, normalizing, and feature extraction on the collected data; The AI ​​model training module uses the preprocessed data to train the machine learning or deep learning model; The intrusion detection module uses real-time analysis of the current system status and uses trained AI models for anomaly detection and intrusion identification; When the response module detects an intrusion, it automatically triggers corresponding defense measures, including isolating infected devices and sending alarms; The user interaction and management interface includes a visual monitoring platform and reporting and analysis tools.

2. The AI-based industrial control system intrusion detection system according to claim 1 is characterized in that: The data acquisition module collects network traffic, device status, and operation log data in real time through sensors and monitoring software deployed in the industrial control system.

3. The AI-based industrial control system intrusion detection system according to claim 1 is characterized in that: The data preprocessing module needs to cooperate with the anomaly detection algorithm to automatically identify and process abnormal or missing data, improve data quality, and adopt automated feature selection and extraction technology, based on genetic algorithm feature selection, to improve the efficiency and accuracy of model training.

4. The AI-based industrial control system intrusion detection system according to claim 1, characterized in that: The AI ​​model training module uses the preprocessed data to train the AI ​​model, improves the accuracy and generalization ability of the model through cross-validation and hyperparameter tuning methods, and uses the online learning mechanism to realize online learning and updating of the model, so that it can adapt to new threats and attack patterns in real time.

5. The AI-based industrial control system intrusion detection system according to claim 1 is characterized in that: The intrusion detection module inputs the real-time collected data into the trained AI model to perform anomaly detection and intrusion identification, introduces user and entity behavior analysis technology, identifies abnormal activities by learning normal behavior patterns, integrates external threat intelligence, enhances the system's threat detection capabilities, and achieves effective response to known and unknown threats.

6. The AI-based industrial control system intrusion detection system according to claim 1, characterized in that: When the intrusion detection module detects an intrusion, the system can not only automatically isolate the infected device, but also try to automatically repair or restore the device to a safe state. According to the threat level and system status, the security policy and access control rules are dynamically adjusted to achieve more flexible security management.

7. The AI-based industrial control system intrusion detection system according to claim 1, characterized in that: The response module automatically triggers corresponding defense measures based on the detection results. When an intrusion is detected, the system can not only automatically isolate the infected device, but also try to automatically repair or restore the device to a safe state. It can also dynamically adjust security policies and access control rules based on the threat level and system status to achieve more flexible security management.

8. The AI-based industrial control system intrusion detection system according to claim 1, characterized in that: The visual monitoring platform provides an intuitive monitoring interface that displays system status, threat alarms, and response actions, making it easy for operation and maintenance personnel to quickly understand and handle security incidents.

9. The AI-based industrial control system intrusion detection system according to claim 1, characterized in that: The reporting and analysis tool generates detailed security reports and analysis charts to help users evaluate the security status of the system and formulate corresponding security strategies.