Industrial network security event response platform

Abstract

The invention relates to the technical field of industrial network security, in particular to an industrial network security event response platform. Comprising an event detection module, a response execution module, a security policy generation module, an intelligent analysis engine, an adaptive learning system, a user interaction interface, a historical event database, a security rule base, an authentication module and a data acquisition and monitoring module. The comprehensive and intelligent industrial network security event response platform provided by the invention realizes rapid detection, intelligent analysis and efficient response of the industrial network security event by integrating multiple advanced technologies, so as to significantly improve the security protection level of the industrial network.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial network security, and in particular to an industrial network security incident response platform. Background Art

[0002] With the rapid development of industrial automation and informatization, the complexity and functionality of industrial control systems (ICS) and operational technology (OT) networks are constantly increasing, and the cybersecurity threats they face are also increasing. Industrial networks not only need to be protected from traditional Internet attacks, such as denial of service attacks (DoS), distributed denial of service attacks (DDoS), SQL injection attacks, cross-site scripting (XSS) attacks, man-in-the-middle attacks (MITM), malware propagation and unauthorized access, but also need to deal with new attacks specific to industrial networks, such as attacks on specific protocols, tampering and destruction of physical devices, etc. Once an industrial network is attacked, it will not only directly affect production efficiency and product quality, but may also cause major safety accidents, threaten the safety of personnel and environmental pollution, and bring immeasurable economic losses and negative social impacts.

[0003] Traditional network security solutions have obvious shortcomings in dealing with new and complex industrial network security threats. For example, traditional intrusion detection systems are usually based on static rules and have difficulty identifying changing attack patterns. At the same time, these solutions often lack targeted support for protocols and systems unique to industrial networks. In addition, industrial networks usually involve a large number of physical devices and complex production processes, so when dealing with network security incidents, a more cautious and efficient response mechanism is needed to minimize the impact on normal business. Summary of the invention

[0004] The present invention proposes a comprehensive and intelligent industrial network security incident response platform, which integrates multiple advanced technologies to achieve rapid detection, intelligent analysis and efficient response to industrial network security incidents, so as to significantly improve the security protection level of industrial networks.

[0005] The technical solution adopted by the present invention is: an industrial network security incident response platform, including an event detection module, a response execution module, a security policy generation module, an intelligent analysis engine, an adaptive learning system, a user interaction interface, a historical event database, a security rule base, an authentication module, and a data acquisition and monitoring module.

[0006] The event detection module is used to monitor the data traffic in the industrial network in real time, detect and identify potential network security threats and abnormal behaviors, and send the detection results to the response execution module and the intelligent analysis engine;

[0007] The response execution module takes predefined response measures to detected threats and anomalies based on the information received from the event detection module, including isolating infected devices, blocking malicious traffic, and restoring tampered system states;

[0008] The security policy generation module generates and adjusts security policies based on historical event data and current security situation to adapt to changes in the network environment;

[0009] The intelligent analysis engine uses machine learning algorithms to analyze the root causes, development trends, and possible impact scope of network security incidents to provide decision support for emergency response;

[0010] The adaptive learning system improves the platform's ability to identify and respond to unknown attacks by continuously learning new security threat patterns and defense measures;

[0011] The user interaction interface provides a friendly operation interface for security managers, including but not limited to event display, policy configuration, and system monitoring functions;

[0012] The historical event database records detailed information of all security events, including event type, occurrence time, impact scope, processing results, etc., for subsequent analysis and learning;

[0013] The security rule base stores a series of predefined security rules and policies to provide a basis for event detection and response execution;

[0014] The authentication module authenticates users who access the platform to ensure that only authorized personnel can operate the platform;

[0015] The data acquisition and monitoring module is responsible for collecting security logs and other relevant data of each node in the industrial network, providing raw data support for event detection and intelligent analysis;

[0016] Through close collaboration among various modules, the platform achieves rapid detection, analysis and response to industrial network security incidents, significantly improving the security protection level of industrial networks.

[0017] As a further improvement of the present invention, the event detection module can identify at least the following types of threats: denial of service attack (DoS), distributed denial of service attack (DDoS), SQL injection attack, cross-site scripting (XSS) attack, man-in-the-middle (MITM) attack, malware propagation, and unauthorized access.

[0018] As a further improvement of the present invention, the response execution module supports a multi-level response mechanism, automatically selecting a corresponding response strategy according to the severity of the security incident, including automatic recording and alarming of minor threats and triggering system recovery and isolation operations for serious threats.

[0019] As a further improvement of the present invention, the security policy generation module can dynamically adjust security rules according to the network traffic analysis results to minimize the impact on normal business.

[0020] As a further improvement of the present invention, the intelligent analysis engine combines supervised learning and unsupervised learning techniques, and can not only automatically classify known threats, but also identify unknown security threats.

[0021] As a further improvement of the present invention, the adaptive learning system utilizes a reinforcement learning method to simulate real attack scenarios, optimize the response strategy of the platform, and enhance the defense effect.

[0022] As a further improvement of the present invention, the user interaction interface supports multi-language switching and can meet the needs of users in different regions around the world.

[0023] As a further improvement of the present invention, the historical event database adopts distributed storage technology to ensure the security and high availability of data.

[0024] As a further improvement of the present invention, the security rule base is regularly updated to include the latest security threat features and defense strategies.

[0025] As a further improvement of the present invention, the authentication module supports multi-factor authentication, including fingerprint recognition, facial recognition, smart card, and one-time verification code, to ensure the accuracy of the user's identity.

[0026] Beneficial effects of the present invention: (1) Improved security and reliability of industrial networks: The present invention integrates a variety of advanced security technologies to enable the platform to quickly detect and respond to various known and unknown network security threats, especially specific attacks on industrial networks and operational technology (OT) systems. The combination of the intelligent analysis engine and the adaptive learning system ensures that the platform can continuously optimize its threat identification and response mechanism and dynamically adjust security policies, thereby minimizing the impact of security incidents on normal business and ensuring the safe and stable operation of the industrial production environment.

[0027] (2) Enhanced system flexibility and scalability: The security incident response platform designed by the present invention has a highly modular design. Each functional module (such as event detection module, response execution module, intelligent analysis engine, etc.) is independent of each other but closely cooperates with each other. This not only facilitates system maintenance and upgrades, but also enables the platform to be flexibly configured according to the specific needs of different industrial environments. For example, through customized security policy generation, the platform can better adapt to the security challenges of specific industries and support the rapid development and technological innovation of the industry.

[0028] (3) Improved user experience and operational convenience: The user interaction interface provided by the platform of the present invention is not only intuitive and user-friendly, but also supports multi-language switching, which greatly facilitates the use of users around the world. In addition, the multi-factor authentication technology adopted by the authentication module, such as fingerprint recognition and facial recognition, not only improves the security of the system, but also simplifies the user's login process and improves the efficiency of daily operations. The high availability and security design of the historical event database ensures that users can access complete and accurate historical security data at any time, providing a solid foundation for subsequent security analysis and decision-making. DETAILED DESCRIPTION

[0029] In order to make the technical problems, technical solutions and beneficial effects to be solved by the present application more clearly understood, the present application is further described in detail below in conjunction with the embodiments. It should be understood that the embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0030] The present invention provides an industrial network security event response platform, including an event detection module, a response execution module, a security policy generation module, an intelligent analysis engine, an adaptive learning system, a user interaction interface, a historical event database, a security rule base, an authentication module, and a data acquisition and monitoring module.

[0031] The event detection module in the present invention is used to monitor the data traffic in the industrial network in real time, detect and identify potential network security threats and abnormal behaviors, and send the detection results to the response execution module and the intelligent analysis engine. The event detection module can identify at least the following types of threats: denial of service attack (DoS), distributed denial of service attack (DDoS), SQL injection attack, cross-site scripting (XSS) attack, man-in-the-middle (MITM) attack, malware propagation, unauthorized access;

[0032] The response execution module in the present invention takes predefined response measures for detected threats and anomalies based on the information received from the event detection module, including isolating infected devices, blocking malicious traffic, and restoring tampered system states. The response execution module supports a multi-level response mechanism and automatically selects corresponding response strategies according to the severity of security events, including automatic recording and alarming of minor threats and triggering system recovery and isolation operations for serious threats;

[0033] The security policy generation module in the present invention generates and adjusts security policies based on historical event data and current security trends to adapt to changes in the network environment. The security policy generation module can dynamically adjust security rules based on network traffic analysis results to minimize the impact on normal business;

[0034] The intelligent analysis engine in the present invention uses machine learning algorithms to analyze the root causes, development trends and possible impact scope of network security incidents to provide decision support for emergency response. The intelligent analysis engine combines supervised learning and unsupervised learning technologies, which can not only automatically classify known threats, but also identify unknown security threats;

[0035] The adaptive learning system in the present invention improves the platform's ability to identify and respond to unknown attacks by continuously learning new security threat patterns and defense methods. The adaptive learning system uses reinforcement learning methods to simulate real attack scenarios, optimize the platform's response strategy, and improve defense effects.

[0036] The user interaction interface of the present invention provides a friendly operation interface for security managers, including but not limited to event display, policy configuration, and system monitoring functions. The user interaction interface supports multi-language switching and can meet the needs of users in different regions around the world;

[0037] The historical event database in the present invention records detailed information of all security events, including event type, occurrence time, impact scope, processing results, etc., for subsequent analysis and learning. The historical event database adopts distributed storage technology to ensure data security and high availability;

[0038] The security rule base in the present invention stores a series of predefined security rules and strategies to provide a basis for event detection and response execution. The security rule base is regularly updated to include the latest security threat features and defense strategies;

[0039] The authentication module in the present invention authenticates the users who access the platform to ensure that only authorized personnel can operate the platform. The authentication module supports multi-factor authentication, including fingerprint recognition, facial recognition, smart card, and one-time verification code to ensure the accuracy of user identity;

[0040] The data acquisition and monitoring module in the present invention is responsible for collecting security logs and other relevant data of each node in the industrial network, and providing raw data support for event detection and intelligent analysis.

[0041] The platform of the present invention realizes rapid detection, analysis and response to industrial network security incidents through close collaboration between various modules, and significantly improves the security protection level of industrial networks.

[0042] Embodiment 1:

[0043] This embodiment demonstrates how the response execution module can effectively manage network security events of different severity through a multi-level response mechanism. Assume that in an industrial network security incident response platform of a large chemical plant, the event detection module identifies two different security threat events: one is an internal employee accidentally clicking on an email containing a malicious link, and the other is an external attacker launching a DDoS attack on the factory's industrial control system.

[0044] (I) Minor Threatening Incidents

[0045] Event description: An internal employee clicked on an email containing a malicious link, causing the employee's operating terminal to be infected.

[0046] Response steps:

[0047] (1) Event detection module: When monitoring network traffic in real time, it detects abnormal traffic and identifies malware propagation events.

[0048] (2) Response execution module: According to the predefined response strategy, the event is automatically recorded and an alarm notification is generated on the user interface.

[0049] (3) Security policy generation module: Analyzes the event type and impact scope, and recommends that security managers take further measures, such as updating the terminal's security protection software.

[0050] (4) Authentication module: ensures that only authorized security management team members can view and process alarm information.

[0051] (5) User interaction interface: The security manager can view detailed alarm information through the user interaction interface and manually confirm the processing measures.

[0052] (6) Data collection and monitoring module: records the event handling process and results for subsequent analysis and improvement of security strategies.

[0053] (II) Serious Threatening Incidents

[0054] Description of the incident: External attackers launched a DDoS attack on the factory's industrial control system, causing key control equipment to fail to work properly.

[0055] Response steps:

[0056] (1) Event detection module: When monitoring network traffic in real time, it detects abnormal traffic and identifies DDoS attack events.

[0057] (2) Response execution module: According to the predefined severe threat response strategy, the following actions are immediately triggered: ① Isolate infected devices: isolate the devices under DDoS attack from the network to prevent the attack from spreading; ② Traffic: block the malicious traffic of external attackers through network firewalls and traffic control devices; ③ System recovery: start the backup control system to restore the normal operation of key equipment.

[0058] (3) Intelligent Analysis Engine: Uses machine learning algorithms to analyze the root causes, development trends, and possible impact range of attacks, providing decision support for subsequent defense measures.

[0059] (4) Adaptive learning system: records the characteristics of the attack, optimizes the platform's response strategy through reinforcement learning methods, and improves the efficiency of identifying and responding to similar attacks.

[0060] (5) Authentication module: Ensures that only authorized security management team members can operate key response measures.

[0061] (6) User interaction interface: The security manager can view detailed attack information and the platform's response operations through the user interaction interface and manually confirm whether further human intervention is required.

[0062] (7) Historical event database: records detailed information about the attack, including event type, occurrence time, impact scope, and processing results, for subsequent analysis and learning.

[0063] (8) Data collection and monitoring module: Continue to monitor network traffic and device status to ensure that there are no new threats after the system resumes normal operation.

[0064] Embodiment 2:

[0065] This embodiment demonstrates how the intelligent analysis engine can use machine learning technology to conduct in-depth analysis of network security events, thereby providing more accurate response suggestions and decision support. Assume that in an industrial network of a smart grid, the event detection module detects a series of abnormal traffic, but cannot immediately determine its nature.

[0066] (I) Abnormal traffic detection

[0067] Event description: A series of abnormal traffic was detected in the industrial network of the smart grid, which may be a new unknown threat.

[0068] Analysis steps:

[0069] (1) Event detection module: When monitoring network traffic in real time, it detects a series of abnormal traffic and sends the traffic data to the intelligent analysis engine.

[0070] (2) Intelligent analysis engine: ①Supervised learning: Use known threat data sets to classify abnormal traffic and determine whether it belongs to known threats. ②Unsupervised learning: Perform cluster analysis on unknown traffic data to discover potential unknown threat patterns.

[0071] (3) Analysis results: ① Known threats: If the abnormal traffic belongs to a known threat, the intelligent analysis engine will immediately notify the response execution module to take the corresponding predefined response measures. ② Unknown threats: If the abnormal traffic belongs to an unknown threat, the intelligent analysis engine will generate a detailed analysis report, including the possible root causes, development trends and impact scope of the threat, to provide decision support for the security management team.

[0072] 2. Threat Root Analysis

[0073] Event description: After analysis by the intelligent analysis engine, it is confirmed that the abnormal traffic belongs to an unknown threat and further root cause analysis is required.

[0074] Analysis steps:

[0075] (1) Intelligent analysis engine: Uses unsupervised learning algorithms to conduct in-depth analysis of abnormal traffic data and discover common characteristics and behavior patterns of traffic.

[0076] (2) Historical event database: The intelligent analysis engine queries historical event data to find the processing methods and results of similar events and provide reference for current events.

[0077] (3) Adaptive learning system: simulates real attack scenarios, generates possible attack models, and further verifies the nature of abnormal traffic.

[0078] (4) Analysis results: ① Determine the root cause of the threat: The intelligent analysis engine ultimately determines that the abnormal traffic is caused by a device in the internal network being infected with malware. ② Recommended response measures: The intelligent analysis engine recommends that the security management team immediately isolate the device and conduct a comprehensive security audit and repair.

[0079] (III) Implementation of response measures

[0080] Response steps:

[0081] (1) Response execution module: Automatically isolate infected devices based on the recommendations of the intelligent analysis engine.

[0082] (2) Authentication module: Ensures that only authorized security management team members can perform isolation and remediation measures.

[0083] (3) User interface: The security management team uses the user interface to view detailed analysis reports and recommended measures, and manually confirm and perform further security audits and remediation operations.

[0084] (4) Data collection and monitoring module: records all operations and results during the isolation and repair process for subsequent analysis and improvement of security policies.

[0085] (5) Historical event database: The detailed information of the event is recorded in the historical event database, providing valuable reference materials for future security analysis and learning.

[0086] Through the above embodiments, it can be seen that the industrial network security incident response platform of the present invention can quickly and accurately respond to various known and unknown network security threats through the combination of a multi-level response mechanism and an intelligent analysis engine, thereby significantly improving the security protection level of the industrial network.

[0087] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An industrial network security incident response platform, characterized in that: It includes event detection module, response execution module, security policy generation module, intelligent analysis engine, adaptive learning system, user interaction interface, historical event database, security rule base, authentication module, data acquisition and monitoring module. The event detection module is used to monitor the data traffic in the industrial network in real time, detect and identify potential network security threats and abnormal behaviors, and send the detection results to the response execution module and the intelligent analysis engine; The response execution module takes predefined response measures to detected threats and anomalies based on the information received from the event detection module, including isolating infected devices, blocking malicious traffic, and restoring tampered system states; The security policy generation module generates and adjusts security policies based on historical event data and current security situation to adapt to changes in the network environment; The intelligent analysis engine uses machine learning algorithms to analyze the root causes, development trends, and possible impact scope of network security incidents to provide decision support for emergency response; The adaptive learning system improves the platform's ability to identify and respond to unknown attacks by continuously learning new security threat patterns and defense measures; The user interaction interface provides a friendly operation interface for security managers, including but not limited to event display, policy configuration, and system monitoring functions; The historical event database records detailed information of all security events, including event type, occurrence time, impact scope, processing results, etc., for subsequent analysis and learning; The security rule base stores a series of predefined security rules and policies to provide a basis for event detection and response execution; The authentication module authenticates users who access the platform to ensure that only authorized personnel can operate the platform; The data acquisition and monitoring module is responsible for collecting security logs and other relevant data of each node in the industrial network, providing raw data support for event detection and intelligent analysis; Through close collaboration among various modules, the platform achieves rapid detection, analysis and response to industrial network security incidents, significantly improving the security protection level of industrial networks.

2. An industrial network security incident response platform according to claim 1, characterized in that: The event detection module can identify at least the following types of threats: denial of service attack (DoS), distributed denial of service attack (DDoS), SQL injection attack, cross-site scripting (XSS) attack, man-in-the-middle (MITM) attack, malware propagation, and unauthorized access.

3. An industrial network security incident response platform according to claim 1, characterized in that: The response execution module supports a multi-level response mechanism and automatically selects a corresponding response strategy according to the severity of the security incident, including automatic recording and alarming of minor threats and triggering system recovery and isolation operations for serious threats.

4. The industrial network security incident response platform according to claim 1, characterized in that: The security policy generation module can dynamically adjust security rules according to the network traffic analysis results to minimize the impact on normal business.

5. The industrial network security incident response platform according to claim 1, characterized in that: The intelligent analysis engine combines supervised learning and unsupervised learning techniques, which can not only automatically classify known threats but also identify unknown security threats.

6. The industrial network security incident response platform according to claim 1, characterized in that: The adaptive learning system utilizes reinforcement learning methods to simulate real attack scenarios, optimize the platform's response strategy, and improve defense effectiveness.

7. An industrial network security incident response platform according to claim 1, characterized in that: The user interaction interface supports multi-language switching and can meet the needs of users in different regions around the world.

8. The industrial network security incident response platform according to claim 1, characterized in that: The historical event database adopts distributed storage technology to ensure the security and high availability of data.

9. The industrial network security incident response platform according to claim 1, characterized in that: The security rule base is updated regularly to include the latest security threat features and defense strategies.

10. The industrial network security incident response platform according to claim 1, characterized in that: The authentication module supports multi-factor authentication, including fingerprint recognition, facial recognition, smart card, and one-time verification code to ensure the accuracy of user identity.