Defense system for complex collaborative attack in power distribution network

Abstract

The invention provides a defense system for complex cooperative attacks in a power distribution network, and relates to the technical field of power system network security. The defense system comprises a data behavior analysis unit, an attack identification early warning unit and a system response and report unit; the data behavior analysis unit comprises a data acquisition preprocessing module, a feature extraction module, a behavior baseline establishment module, an anomaly detection module and an attack source positioning module; the data acquisition preprocessing module is in one-way connection with the feature extraction module; the feature extraction module is in one-way connection with the behavior baseline establishment module; the behavior baseline establishment module is in one-way connection with the anomaly detection module; the anomaly detection module is in one-way connection with the attack source positioning module; according to the invention, real-time data of key nodes are collected through the data acquisition and preprocessing module, and deep analysis is carried out by using the feature extraction module, the behavior baseline establishment module and the anomaly detection module, so that multiple sections of abnormal behaviors and potential threats in the power distribution system can be accurately identified.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system network security, and in particular relates to a defense system against complex coordinated attacks in a distribution network. Background Art

[0002] With the continuous development and upgrading of power systems, the distribution system, as a key link in power supply, is of vital importance to the operation of the entire power grid in terms of safety and stability. However, in recent years, with the continuous evolution and complexity of network attack technology, the distribution system is facing more and more security threats. Traditional distribution system security monitoring methods mainly rely on manual inspections and simple threshold alarms, but this method cannot timely detect potential safety hazards or ongoing attacks. On the other hand, the simple threshold alarm method is not enough when facing complex and changeable attack methods. This method is not only inefficient, but also difficult to cope with complex and changeable attack methods. Although some existing automated monitoring systems can achieve a certain degree of real-time monitoring, they still have problems of low recognition rate and slow response speed when facing complex situations such as complex coordinated attacks and covert attacks. Therefore, we propose a defense system against complex coordinated attacks in distribution networks.

[0003] The technical comparison with patent CN115604032A "A method and system for detecting complex multi-step attacks on power systems" includes five aspects: data processing method, attack detection mechanism, time feature processing, system self-learning and adaptability, application scenarios and technical implementation complexity.

[0004] In terms of data processing methods, the comparative document CN115604032A pre-processes the network entity interaction behavior data through heterogeneous graph technology. The heterogeneous graph includes nodes such as users, hosts, files and websites. The connection relationship between nodes is used as an edge. At the same time, the timestamp information is processed through the Time2Vec layer, and then the features are extracted through the Heteformer layer. The present invention collects real-time data in the distribution system through a data acquisition preprocessing module, and extracts key features in the data through a feature extraction module. Based on these features, combined with the behavior baseline and anomaly detection modules, the monitoring and detection of attacks are realized.

[0005] In terms of attack detection mechanism, the comparative document CN115604032A adopts a complex multi-step attack detection module, uses the self-attention mechanism to assign different weights to different types of nodes and edges, and then captures important neighbor information through the attention mechanism to complete attack detection. The present invention models the normal behavior of the system through a behavioral baseline establishment module, monitors behavioral deviations in real time through an anomaly detection module, and uses honeypot technology to lure attackers into a virtual environment, thereby identifying and reporting the attack source.

[0006] In terms of time feature processing, the comparative document CN115604032A uses the Time2Vec layer to process the timestamp information into an embedded vector and maps it back to the time feature through a linear layer to enhance the time information representation of the node, thereby improving the detection accuracy of complex multi-step attacks. However, the present invention does not use a special time embedding technology, but mainly relies on the comparison of real-time data with the behavior baseline, and uses statistical analysis and machine learning algorithms to determine whether there is abnormal behavior.

[0007] In terms of system self-learning and adaptability, although the reference document CN115604032A adopts a deep learning model, it does not mention the self-learning function in the document, but optimizes the detection results by manually adjusting the model parameters. The system self-learning module of the present invention optimizes the detection model and response strategy by continuously learning new data, and can continuously improve the system performance according to different scenarios and new attack methods, thereby improving the flexibility and accuracy of detection.

[0008] In terms of application scenarios and technical implementation complexity, the complex multi-step attack detection system of the comparative document CN115604032A is suitable for large-scale power systems with rich network entity interaction data. Through heterogeneous graphs and deep learning models, it processes relatively complex data relationships and adapts to large and complex network environments. However, the present invention focuses on the distribution system, mainly through the collaborative work of multiple modules to achieve real-time monitoring and multi-step attack detection of key nodes in the distribution system. It has strong adaptability but relatively simple technical implementation.

[0009] Compared with the comparative document CN115604032A, the present invention has the following innovations: first, the present invention effectively lures attackers into a virtual environment through honeypot technology, thereby obtaining accurate attack source intelligence; second, the system self-learning module of the present invention can continuously optimize the model according to new data, making the system more adaptable; third, the present invention is designed for the power distribution system, and the module collaboration is more sophisticated, which can provide a fast and accurate response plan in a complex multi-step attack environment. Summary of the invention

[0010] In order to solve the above technical problems, the present invention proposes a defense system against complex coordinated attacks in distribution networks. The real-time data of key nodes are collected through the data acquisition and preprocessing module, and in-depth analysis is performed using feature extraction, behavior baseline establishment and anomaly detection modules. The system can accurately identify abnormal behaviors and potential threats in the distribution system, and solves the problem that some existing automated monitoring systems can achieve a certain degree of real-time monitoring, but still have low recognition rate and slow response speed when facing complex situations such as complex coordinated attacks and covert attacks.

[0011] To achieve the above object, the technical solution adopted by the present invention is:

[0012] A defense system for complex coordinated attacks in a distribution network, characterized by comprising a data behavior analysis unit, an attack identification and early warning unit, and a system response and reporting unit:

[0013] The data behavior analysis unit includes a data acquisition preprocessing module, a feature extraction module, a behavior baseline establishment module, an anomaly detection module and an attack source positioning module; the data acquisition preprocessing module is unidirectionally connected to the feature extraction module; the feature extraction module is unidirectionally connected to the behavior baseline establishment module; the behavior baseline establishment module is unidirectionally connected to the anomaly detection module; the anomaly detection module is unidirectionally connected to the attack source positioning module;

[0014] The attack identification and early warning unit includes an attack pattern recognition module, a multi-stage attack chain analysis module, an attack intention prediction module, a risk assessment module and an early warning mechanism module; the anomaly detection module is unidirectionally connected to the attack pattern recognition module; the attack pattern recognition module is unidirectionally connected to the multi-stage attack chain analysis module; the multi-stage attack chain analysis module is unidirectionally connected to the attack intention prediction module; the attack intention prediction module is unidirectionally connected to the risk assessment module; the risk assessment module is unidirectionally connected to the early warning mechanism module;

[0015] The system response and reporting unit includes a response strategy generation module, an automated response execution module, a reporting and auditing module, a system self-learning module and a user interface module; the attack source positioning module is unidirectionally connected to the user interface module; the early warning mechanism module is unidirectionally connected to the response strategy generation module; the response strategy generation module is unidirectionally connected to the automated response execution module; the automated response execution module is unidirectionally connected to the reporting and auditing module; the reporting and auditing module is bidirectionally connected to the system self-learning module; and the reporting and auditing module is unidirectionally connected to the user interface module.

[0016] As a further improvement of the present invention, the data collection preprocessing module, feature extraction module, behavior baseline establishment module, anomaly detection module and attack source positioning module in the data behavior analysis unit are specifically as follows;

[0017] The data acquisition preprocessing module is responsible for collecting real-time data from multiple key nodes of the power distribution system; these data include multiple indicators such as current, voltage, and temperature. After collecting the data, the module will clean and preprocess the data to eliminate noise, fill in missing values ​​or abnormal values, and may perform data standardization operations;

[0018] The feature extraction module is responsible for sending the preprocessed data to the feature extraction module. The system will extract key features that can reflect the system status from the raw data; these features may include statistical characteristics of the data and time series characteristics; the purpose of feature extraction is to convert high-dimensional raw data into a low-dimensional feature space for subsequent anomaly detection and attack identification;

[0019] The behavior baseline establishment module is responsible for building a normal operating behavior baseline based on historical data; this baseline represents the performance of the system under normal operating conditions and evaluates whether the current behavior deviates from normal standards and references;

[0020] The anomaly detection module is responsible for using the established behavior baseline. The anomaly detection module monitors abnormal behaviors in the data stream in real time through statistical analysis and machine learning algorithms. When the real-time data is significantly different from the behavior baseline, the system will consider that there is abnormal behavior and report it.

[0021] The attack source location module uses honeypot technology to simulate real or virtual systems and services, deliberately exposing some vulnerabilities and weaknesses to lure attackers into attacking; once the attacker enters the honeypot environment, all of his behaviors and operations will be recorded and analyzed, thereby providing attack intelligence and threat information to the security team.

[0022] As a further improvement of the present invention, the attack identification and early warning unit includes an attack pattern recognition module, a multi-stage attack chain analysis module, an attack intention prediction module, a risk assessment module and an early warning mechanism module, as follows;

[0023] The attack pattern recognition module is responsible for further analyzing abnormal behaviors when abnormal behaviors are detected, and identifying specific attack patterns through pattern recognition technology; this helps to distinguish normal abnormalities from real attack behaviors, and provide more accurate information for subsequent response;

[0024] The multi-segment attack chain analysis module is responsible for identifying attack behaviors, and the complex coordinated attack chain analysis module analyzes their coherence and attempts to identify complex coordinated attack chains; this helps to understand the overall structure and process of the attack, assess the complexity and impact of the attack, and provide a basis for formulating effective response strategies;

[0025] The attack intention prediction module is responsible for predicting the attacker's possible intention and next action based on the attack mode and behavior chain, so that preventive measures can be taken in advance to prevent further development of the attack;

[0026] The risk assessment module is responsible for assessing the impact of the attack on the distribution system, including direct losses and potential risks; this helps determine the priority and urgency of the response;

[0027] The early warning mechanism module is responsible for automatically triggering an early warning based on the risk assessment results and notifying relevant personnel in a timely manner; the early warning information includes the type, location and impact range of the attack so that relevant personnel can respond quickly.

[0028] As a further improvement of the present invention, in the risk assessment module, the purpose of the attack impact assessment is to quantify the damage that the attack may cause to the power distribution system, which includes direct losses L and potential risks R. Direct losses usually refer to losses that have already occurred and can be directly measured, such as equipment damage and data loss. Potential risks refer to risks that may occur in the future. These risks may not appear immediately, but may cause greater damage to the system in the future. P is the probability of the potential risk occurring, which needs to be determined through previous data collection, statistical analysis, and system self-learning model prediction. The total risk assessment value V is evaluated by the following formula;

[0029] V=L+R×P

[0030] The impact of potential risks is regulated by the probability of their occurrence. If the probability of potential risks is high, then this part of the risk will contribute more to the total risk. On the contrary, if the probability of potential risks is low, then even if the potential risk itself is large, its contribution to the total risk will be relatively small. According to the calculated total risk assessment value V, risks are divided into different levels, such as low, medium, and high, which helps to determine the priority and urgency of the response.

[0031] As a further improvement of the present invention, the response strategy generation module, the automated response execution module, the report and audit module, the system self-learning module and the user interface module in the system response and reporting unit are specifically as follows;

[0032] The response strategy generation module is responsible for generating corresponding response strategies for different attack types and risk levels after receiving early warning reminders; these strategies include isolating the affected areas, repairing damaged equipment or systems, and strengthening security protection measures;

[0033] The automated response execution module is responsible for automatically executing the generated response strategy; this can reduce the time and cost of manual intervention and improve the efficiency and accuracy of the response; at the same time, the module can also adjust the execution order and method of the strategy according to the actual situation to achieve the best response effect;

[0034] The reporting and auditing module is responsible for generating attack detection reports and audit records; these reports include detailed descriptions of events, response processes, loss assessments, and improvement suggestions, providing references for subsequent analysis and decision-making; the system self-learning module continuously optimizes detection models and response strategies through continuous learning; extracts useful information and knowledge from new data and experience, updates behavioral baselines, adjusts detection algorithm parameters, or introduces new algorithms and technologies to improve the system's detection accuracy and response speed;

[0035] The user interface module provides a friendly user interface to display real-time monitoring data, warning information and report content; it allows users to easily view the system's operating status, understand the attack situation, and execute related operations and instructions, while presenting the intrusion information operation records and analysis provided by the attack source positioning module; at the same time, the module also supports a variety of interaction methods and visualization tools to meet the needs and preferences of different users.

[0036] The present invention has the following beneficial effects:

[0037] The present invention collects real-time data of key nodes through a data acquisition and preprocessing module, and uses feature extraction, behavior baseline establishment and anomaly detection modules for in-depth analysis, which can accurately identify abnormal behaviors and potential threats in the power distribution system; compared with traditional methods, the present invention has significantly improved detection accuracy and efficiency, and can more quickly discover and respond to security events; specifically, the data acquisition and preprocessing module can ensure the accuracy and integrity of the collected data, providing a reliable basis for subsequent analysis; the feature extraction module extracts key features that can reflect the system state from these data, so that the anomaly detection module can more accurately identify potential threats; and the behavior baseline establishment module provides a reference standard for anomaly detection, making the detection process more efficient and accurate; this multi-module collaborative working mode greatly improves the efficiency and accuracy of detection, so that the present invention can discover more security threats in a shorter time, and through honeypot technology, by simulating real or virtual systems and services, deliberately exposing some vulnerabilities and weaknesses, and luring attackers to attack; once the attacker enters the honeypot environment, all of his behaviors and operations will be recorded and analyzed, thereby providing attack intelligence and threat information to the security team, and protecting the power distribution system more targetedly according to the attack.

[0038] The present invention automatically triggers an early warning according to the risk assessment results, and formulates targeted response measures through a response strategy generation module; these measures may include isolating the affected area, repairing damaged equipment or systems, etc., to minimize the impact of the attack on the distribution system; at the same time, the automated response execution module ensures that these strategies can be executed quickly and accurately, greatly improving the efficiency and accuracy of responding to attacks; the risk assessment module can quantitatively evaluate the risks according to factors such as the nature, scope of impact and urgency of the attack, and provide a basis for the formulation of response strategies; the response strategy generation module formulates specific response measures based on these assessment results, and realizes the rapid deployment and execution of these measures through the automated response execution module; this automated process greatly shortens the response time and improves the efficiency and accuracy of responding to attacks.

[0039] The present invention has a powerful system self-learning module, which can continuously optimize the detection model and response strategy through continuous learning; this means that as time goes by and data accumulates, the present invention will increasingly accurately identify security threats in the power distribution system and formulate more effective response strategies; this continuous learning capability enables the present invention to adapt to the ever-changing attack methods and environments, and maintain long-term effectiveness and reliability; specifically, the system self-learning module can learn new features and patterns from new data and feed them back to the detection model and response strategy generation module; in this way, as time goes by and data accumulates, the detection capability and response capability of the present invention will continue to improve; at the same time, this continuous learning capability also enables the present invention to adapt to the ever-changing attack methods and environments, and always maintain comprehensive protection for the power distribution system. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 A flowchart of a defense system for complex coordinated attacks in a distribution network according to the present invention;

[0041] Figure 2 It is a general implementation flow chart of the present invention;

[0042] Figure 3 This is a flow chart of the risk assessment module algorithm of the present invention. DETAILED DESCRIPTION

[0043] The present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments:

[0044] The defense system flow chart is as follows Figure 1 The overall implementation flow chart is as shown in Figure 2 As shown in the figure, the algorithm flow chart of the risk assessment module is as follows Figure 3 As shown, the present invention is a defense system for complex coordinated attacks in a distribution network, including a data behavior analysis unit, an attack identification and early warning unit, and a system response and reporting unit;

[0045] Figure 1 This is a flowchart of a defense system for complex coordinated attacks in a distribution network of the present invention, which shows the workflow of the entire defense system. The system includes three main units: a data behavior analysis unit, an attack identification and early warning unit, and a system response and reporting unit. The data behavior analysis unit is responsible for collecting data from the distribution system, extracting features, establishing a behavior baseline, and detecting abnormal behavior; the attack identification and early warning unit further analyzes abnormal behavior, identifies attack patterns, assesses risks, and triggers early warnings; the system response and reporting unit generates corresponding response strategies based on the early warning results, automatically executes and generates reports, and continuously optimizes the detection model and response strategy through the system self-learning module.

[0046] Figure 2 This is the overall implementation flow chart of the present invention, which describes the specific implementation process of the defense system. First, various data of the distribution system are collected and preprocessed through the data acquisition and preprocessing module. Then, the feature extraction and behavior baseline establishment module extracts key features from the preprocessed data and establishes a normal behavior baseline. Next, the anomaly detection and attack source location module is used to monitor abnormal behavior in real time and locate the source of the attack. The attack pattern recognition and multi-segment attack chain analysis module identifies the attack pattern in the abnormal behavior and analyzes the structure of the attack chain. After that, the attack intention prediction and risk assessment module predicts possible attack intentions based on the attack chain and performs risk assessment. Based on the evaluation results, the system generates and automatically executes a response strategy, which is then recorded and reviewed by the report and audit module. Finally, the detection model and response strategy are continuously optimized through the system self-learning and optimization module to enhance the defense capability.

[0047] Figure 3 This is the algorithm flow chart of the risk assessment module of the present invention, which shows the specific process of assessing the impact of attacks on the power distribution system. First, the direct loss assessment module quantifies the losses that have occurred and obtains the direct loss value L. Then, the potential risk assessment module combines the potential risk occurrence probability prediction module to calculate the potential risk value R and its occurrence probability P, and calculates the expected impact of the potential risk through R×P. Next, all assessment values ​​are input into the total risk assessment value calculation module to calculate the total risk assessment value V, and the formula is V=L+R×P. Finally, according to the calculated risk assessment value, the system risk is graded so that corresponding response measures and strategies can be formulated.

[0048] The data behavior analysis unit includes a data collection preprocessing module, a feature extraction module, a behavior baseline establishment module, an anomaly detection module and an attack source positioning module; the data collection preprocessing module is unidirectionally connected to the feature extraction module; the feature extraction module is unidirectionally connected to the behavior baseline establishment module; the behavior baseline establishment module is unidirectionally connected to the anomaly detection module; the anomaly detection module is unidirectionally connected to the attack source positioning module;

[0049] The data collection and preprocessing module is responsible for collecting real-time data from multiple key nodes of the power distribution system; these data include current, voltage, temperature and other indicators. After collecting the data, the module will clean and preprocess the data to eliminate noise, fill in missing values ​​or outliers, and may perform data standardization operations; the feature extraction module is responsible for sending the preprocessed data to the feature extraction module, and the system will extract key features that can reflect the system status from the raw data; these features may include statistical characteristics of the data and time series characteristics; the purpose of feature extraction is to convert high-dimensional raw data into low-dimensional feature space for subsequent anomaly detection and attack identification; the behavior baseline establishment module is responsible for based on historical data, Build a normal operating behavior baseline; this baseline represents the performance of the system under normal operating conditions and evaluates whether the current behavior deviates from normal standards and references; the anomaly detection module is responsible for utilizing the established behavior baseline. The anomaly detection module monitors abnormal behaviors in data streams in real time through statistical analysis and machine learning algorithms; when real-time data is significantly different from the behavior baseline, the system will consider that there is abnormal behavior and report it; the attack source location module uses honeypot technology to simulate real or virtual systems and services, deliberately exposing some vulnerabilities and weaknesses to lure attackers to attack; once an attacker enters the honeypot environment, all of his behaviors and operations will be recorded and analyzed, thereby providing attack intelligence and threat information to the security team.

[0050] The attack identification and early warning unit includes an attack pattern recognition module, a multi-stage attack chain analysis module, an attack intention prediction module, a risk assessment module and an early warning mechanism module; the anomaly detection module is unidirectionally connected to the attack pattern recognition module; the attack pattern recognition module is unidirectionally connected to the multi-stage attack chain analysis module; the multi-stage attack chain analysis module is unidirectionally connected to the attack intention prediction module; the attack intention prediction module is unidirectionally connected to the risk assessment module; the risk assessment module is unidirectionally connected to the early warning mechanism module.

[0051] The attack pattern recognition module is responsible for further analyzing abnormal behaviors when abnormal behaviors are detected, and identifying specific attack patterns through pattern recognition technology; this helps to distinguish normal abnormalities from real attack behaviors, and provide more accurate information for subsequent responses; the multi-segment attack chain analysis module is responsible for identifying attack behaviors, and the complex coordinated attack chain analysis module will analyze its coherence and try to identify complex coordinated attack chains; this helps to understand the overall structure and process of the attack, evaluate the complexity and impact of the attack, and provide a basis for formulating effective response strategies; the attack intention prediction module is responsible for predicting the possible intentions and next actions of the attacker based on the attack pattern and behavior chain; so that preventive measures can be taken in advance to prevent further development of the attack; the risk assessment module is responsible for assessing the impact of the attack on the distribution system, including direct losses and potential risks; this helps to determine the priority and urgency of the response; the early warning mechanism module is responsible for automatically triggering an early warning based on the risk assessment results and notifying relevant personnel in a timely manner; the early warning information includes the type, location and scope of impact of the attack so that relevant personnel can respond quickly;

[0052] In the risk assessment module, the purpose of attack impact assessment is to quantify the damage that an attack may cause to the power distribution system, which includes direct losses L and potential risks R. Direct losses usually refer to losses that have already occurred and can be directly measured, such as equipment damage and data loss. Potential risks refer to risks that may occur in the future. These risks may not appear immediately, but may cause greater damage to the system in the future. P is the probability of potential risk occurrence, which needs to be determined through previous data collection, statistical analysis, and system self-learning model prediction. We use the following formula to evaluate the total risk assessment value V;

[0053] V=L+R×P

[0054] The impact of potential risks is regulated by the probability of their occurrence. If the probability of potential risks is high, then this part of the risk will contribute more to the total risk. On the contrary, if the probability of potential risks is low, then even if the potential risk itself is large, its contribution to the total risk will be relatively small. According to the calculated total risk assessment value V, risks are divided into different levels (such as low, medium, and high), which helps to determine the priority and urgency of the response.

[0055] The system response and reporting unit includes a response strategy generation module, an automated response execution module, a reporting and auditing module, a system self-learning module and a user interface module; the attack source positioning module is unidirectionally connected to the user interface module; the early warning mechanism module is unidirectionally connected to the response strategy generation module; the response strategy generation module is unidirectionally connected to the automated response execution module; the automated response execution module is unidirectionally connected to the reporting and auditing module; the reporting and auditing module is bidirectionally connected to the system self-learning module; and the reporting and auditing module is unidirectionally connected to the user interface module.

[0056] The response strategy generation module is responsible for generating corresponding response strategies for different attack types and risk levels after receiving early warning reminders; these strategies include isolating affected areas, repairing damaged equipment or systems, and strengthening security protection measures; the automated response execution module is responsible for automatically executing the generated response strategies; this can reduce the time and cost of manual intervention and improve the efficiency and accuracy of the response; at the same time, the module can also adjust the execution order and method of the strategies according to actual conditions to achieve the best response effect; the reporting and auditing module is responsible for generating attack detection reports and audit records; these reports include detailed descriptions of the events, response processes, loss assessments, and improvement suggestions for subsequent analysis and decision-making; the system self-learning module continuously optimizes the detection model and response strategy through continuous learning; it can extract useful information and knowledge from new data and experience, update the behavioral baseline, adjust the detection algorithm parameters or introduce new algorithms and technologies to improve the detection accuracy and response speed of the system; the user interface module provides a friendly user interface to display real-time monitoring data, early warning information and report content; it allows users to easily view the system's operating status, understand the attack situation and perform related operations and instructions, and presents the intrusion information operation records and analysis provided by the attack source positioning module; at the same time, the module also supports a variety of interaction methods and visualization tools to meet the needs and preferences of different users.

[0057] A specific application of this embodiment is: real-time data is collected from multiple key nodes of the power distribution system through the data acquisition preprocessing module, including current, voltage, temperature and other indicators, and the data is cleaned and preprocessed to eliminate noise, fill missing values ​​or abnormal values, and perform data standardization operations; then, the feature extraction module inputs the preprocessed data, and the system extracts key features that can reflect the system status from the raw data, such as the statistical characteristics of the data and the time series characteristics, and converts the high-dimensional raw data into a low-dimensional feature space for subsequent anomaly detection and attack identification; next, the behavior baseline establishment module constructs a normal operating behavior baseline based on historical data to evaluate whether the current behavior deviates from the normal standard and reference ; The anomaly detection module uses the established behavioral baseline to monitor abnormal behaviors in the data stream in real time through statistical analysis and machine learning algorithms. When the real-time data is significantly different from the behavioral baseline, the system will consider that abnormal behavior exists and report it; The attack source positioning module uses honeypot technology to simulate real or virtual systems and services, deliberately expose some vulnerabilities and weaknesses, and lure attackers to attack. Once the attacker enters the honeypot environment, all of his behaviors and operations will be recorded and analyzed, thereby providing attack intelligence and threat information to the security team; When abnormal behaviors are detected, the attack pattern recognition module will further analyze these abnormal behaviors and identify specific attack patterns through pattern recognition technology, which helps to distinguish normal anomalies from real attack behaviors ; The multi-segment attack chain analysis module is responsible for identifying attack behaviors and analyzing their coherence, trying to identify complex coordinated attack chains, understand the overall structure and process of the attack, and evaluate the complexity and impact of the attack; the attack intention prediction module predicts the attacker's possible intentions and next actions based on the attack mode and behavior chain, so as to take preventive measures in advance to prevent further development of the attack; the risk assessment module evaluates the impact of the attack on the distribution system, including direct losses and potential risks, and determines the priority and urgency of the response; the early warning mechanism module automatically triggers an early warning based on the risk assessment results and notifies relevant personnel in a timely manner. The early warning information includes the type, location and scope of impact of the attack; the response strategy generation module responds to different attacks after receiving the early warning reminder. The module generates corresponding response strategies based on the attack type and risk level, such as isolating the affected area, repairing damaged equipment or systems, and strengthening security protection measures; the automated response execution module is responsible for automatically executing the generated response strategy, reducing the time and cost of manual intervention, and improving the efficiency and accuracy of the response; the reporting and auditing module generates attack detection reports and audit records, including detailed descriptions of the events, response processes, loss assessments, and improvement suggestions, providing a reference for subsequent analysis and decision-making; the system self-learning module continuously optimizes the detection model and response strategy through continuous learning, extracts useful information and knowledge from new data and experience, updates the behavioral baseline, adjusts the detection algorithm parameters, or introduces new algorithms and technologies to improve the detection accuracy and response speed of the system;The user interface module provides a friendly user interface, displays real-time monitoring data, warning information and report content, allows users to easily view the system's operating status, understand the attack situation and perform related operations and instructions, and also presents the intrusion information operation records and analysis provided by the attack source location module, supports a variety of interactive methods and visualization tools to meet the needs and preferences of different users. ;

[0058] The above description is only a preferred embodiment of the present invention and does not constitute any other form of limitation to the present invention. Any modification or equivalent change made based on the technical essence of the present invention still falls within the scope of protection required by the present invention.

Claims

1. A defense system against complex coordinated attacks in a distribution network, characterized by: It includes a data behavior analysis unit, an attack identification and warning unit, and a system response and reporting unit; its characteristics are: The data behavior analysis unit includes a data acquisition preprocessing module, a feature extraction module, a behavior baseline establishment module, an anomaly detection module and an attack source positioning module; the data acquisition preprocessing module is unidirectionally connected to the feature extraction module; the feature extraction module is unidirectionally connected to the behavior baseline establishment module; the behavior baseline establishment module is unidirectionally connected to the anomaly detection module; the anomaly detection module is unidirectionally connected to the attack source positioning module; The attack identification and early warning unit includes an attack pattern recognition module, a multi-stage attack chain analysis module, an attack intention prediction module, a risk assessment module and an early warning mechanism module; the anomaly detection module is unidirectionally connected to the attack pattern recognition module; the attack pattern recognition module is unidirectionally connected to the multi-stage attack chain analysis module; the multi-stage attack chain analysis module is unidirectionally connected to the attack intention prediction module; the attack intention prediction module is unidirectionally connected to the risk assessment module; the risk assessment module is unidirectionally connected to the early warning mechanism module; The system response and reporting unit includes a response strategy generation module, an automated response execution module, a reporting and auditing module, a system self-learning module and a user interface module; the attack source positioning module is unidirectionally connected to the user interface module; the early warning mechanism module is unidirectionally connected to the response strategy generation module; the response strategy generation module is unidirectionally connected to the automated response execution module; the automated response execution module is unidirectionally connected to the reporting and auditing module; the reporting and auditing module is bidirectionally connected to the system self-learning module; and the reporting and auditing module is unidirectionally connected to the user interface module.

2. A defense system against complex coordinated attacks in a distribution network according to claim 1, characterized in that: The data collection preprocessing module, feature extraction module, behavior baseline establishment module, anomaly detection module and attack source positioning module in the data behavior analysis unit are specifically as follows; The data acquisition preprocessing module is responsible for collecting real-time data from multiple key nodes of the power distribution system; these data include multiple indicators such as current, voltage, and temperature. After collecting the data, the module will clean and preprocess the data to eliminate noise, fill in missing values ​​or abnormal values, and may perform data standardization operations; The feature extraction module is responsible for sending the preprocessed data to the feature extraction module, and the system will extract key features that can reflect the system status from the original data; these features may include statistical characteristics of the data and time series characteristics; The purpose of feature extraction is to convert high-dimensional raw data into a low-dimensional feature space for subsequent anomaly detection and attack identification; The behavior baseline establishment module is responsible for building a normal operating behavior baseline based on historical data; This baseline represents the performance of the system under normal operating conditions, and evaluates whether the current behavior deviates from normal standards and references; the anomaly detection module is responsible for utilizing the established behavioral baseline, and the anomaly detection module monitors abnormal behavior in the data stream in real time through statistical analysis and machine learning algorithms; When real-time data differs significantly from the behavioral baseline, the system will consider it an abnormal behavior and report it accordingly. The attack source location module uses honeypot technology to simulate real or virtual systems and services, deliberately exposing some vulnerabilities and weaknesses to lure attackers into attacking; once the attacker enters the honeypot environment, all of his behaviors and operations will be recorded and analyzed, thereby providing attack intelligence and threat information to the security team.

3. A defense system against complex coordinated attacks in a distribution network according to claim 1, characterized in that: The attack identification and early warning unit includes an attack pattern recognition module, a multi-stage attack chain analysis module, an attack intention prediction module, a risk assessment module and an early warning mechanism module, as follows; The attack pattern recognition module is responsible for further analyzing abnormal behaviors when abnormal behaviors are detected, and identifying specific attack patterns through pattern recognition technology; this helps to distinguish normal abnormalities from real attack behaviors, and provide more accurate information for subsequent response; The multi-segment attack chain analysis module is responsible for identifying attack behaviors, and the complex coordinated attack chain analysis module analyzes their coherence and attempts to identify complex coordinated attack chains; this helps to understand the overall structure and process of the attack, assess the complexity and impact of the attack, and provide a basis for formulating effective response strategies; The attack intention prediction module is responsible for predicting the attacker's possible intention and next action based on the attack mode and behavior chain, so that preventive measures can be taken in advance to prevent further development of the attack; The risk assessment module is responsible for assessing the impact of the attack on the distribution system, including direct losses and potential risks; this helps determine the priority and urgency of the response; The early warning mechanism module is responsible for automatically triggering an early warning based on the risk assessment results and notifying relevant personnel in a timely manner; the early warning information includes the type, location and impact range of the attack so that relevant personnel can respond quickly.

4. A defense system against complex coordinated attacks in a distribution network according to claim 3, characterized in that: In the risk assessment module, the purpose of attack impact assessment is to quantify the damage that an attack may cause to the power distribution system, which includes direct losses L and potential risks R. Direct losses usually refer to losses that have already occurred and can be directly measured, such as equipment damage and data loss. Potential risks refer to risks that may occur in the future. These risks may not appear immediately, but may cause greater damage to the system in the future. P is the probability of potential risk occurrence, which needs to be determined through previous data collection, statistical analysis, and system self-learning model prediction. The total risk assessment value V is evaluated by the following formula; V=L+R×P The impact of potential risks is regulated by the probability of their occurrence. If the probability of potential risks is high, then this part of the risk will contribute more to the total risk. On the contrary, if the probability of potential risks is low, then even if the potential risk itself is large, its contribution to the total risk will be relatively small. According to the calculated total risk assessment value V, risks are divided into different levels, such as low, medium, and high, which helps to determine the priority and urgency of the response.

5. A defense system against complex coordinated attacks in a distribution network according to claim 1, characterized in that: The response strategy generation module, automated response execution module, report and audit module, system self-learning module and user interface module in the system response and reporting unit are specifically as follows; The response strategy generation module is responsible for generating corresponding response strategies for different attack types and risk levels after receiving early warning reminders; these strategies include isolating the affected areas, repairing damaged equipment or systems, and strengthening security protection measures; The automated response execution module is responsible for automatically executing the generated response strategy; this can reduce the time and cost of manual intervention and improve the efficiency and accuracy of the response; at the same time, the module can also adjust the execution order and method of the strategy according to the actual situation to achieve the best response effect; The reporting and auditing module is responsible for generating attack detection reports and audit records; these reports include detailed descriptions of events, response processes, loss assessments, and improvement suggestions, providing a reference for subsequent analysis and decision-making; The system self-learning module continuously optimizes the detection model and response strategy through continuous learning; extracts useful information and knowledge from new data and experience, updates the behavior baseline, adjusts the detection algorithm parameters or introduces new algorithms and technologies to improve the detection accuracy and response speed of the system; The user interface module provides a user-friendly interface to display real-time monitoring data, warning information and report content; It allows users to easily view the system's operating status, understand the attack situation, and execute related operations and instructions, while presenting the intrusion information operation records and analysis provided by the attack source positioning module; at the same time, the module also supports a variety of interactive methods and visualization tools to meet the needs and preferences of different users.

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