Power network security defense system and method

Abstract

The invention belongs to the technical field of power networks, and particularly relates to a power network security defense system and method, and the system comprises a sensing module which is used for collecting network flow, equipment states and user operation data information in a power network in real time according to various kinds of deployed security monitoring equipment, therefore, preliminary perception of network attack behaviors, abnormal operation and equipment fault security events is realized; and the transmission module is used for ensuring the confidentiality and integrity of the security monitoring data in the transmission process by adopting an encryption communication technology, constructing a security data transmission channel, preventing the data from being stolen and tampered, and meanwhile, having a redundancy backup mechanism for data transmission to ensure the reliability of the data transmission. The decision-making module can formulate a security defense strategy and an emergency response scheme according to the result of the analysis module, and effectively solves the problem of quick response when the power network faces a security event.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power network, in particular to a power network security defense system and method. BACKGROUND

[0002] With the rapid development of digitalization and intelligentization of the power industry, the power network is deeply integrated with information technology, and new technologies such as smart grid, distributed energy access, and power internet of things are widely applied. The power network has gradually transformed from a traditional closed system to an open and interconnected complex system, which has improved operational efficiency and service quality, but also faces unprecedented network security risks. The specific problems are as follows:

[0003] 1. External attack threat: In recent years, the power network has become a key target of hacker attacks. For example, attackers can gain control by hacking into the management information system of a power company and maliciously operating the automation equipment in the substation, modifying the parameters of the relay protection device, and causing widespread paralysis of the power grid. Such attacks exploit vulnerabilities in the connection between the power network and external networks, and penetrate the system through phishing, malicious software implantation, and other means, posing a serious threat to the normal operation of the power network.

[0004] 2. Internal security risks: Improper or malicious behavior by internal personnel can also cause security problems. A power company employee used an unauthorized mobile storage device on their work computer, which infected the internal network system with a virus, which then spread to the power monitoring system, causing abnormal power data collection in some areas and affecting power grid dispatching decisions. In addition, lax internal permission management and unauthorized access to sensitive data and critical control functions by some personnel also increase the risk of data leakage and misoperation.

[0005] 3. Device and system vulnerabilities: A large number of intelligent meters, distributed power controllers, and substation automation devices used in the power network have many security vulnerabilities due to insufficient security considerations during design and development. For example, some old power monitoring systems have unpatched remote code execution vulnerabilities that attackers can exploit to remotely control the monitoring system, tamper with power operation parameters, and disrupt normal power grid operation. Meanwhile, the communication protocols between devices from different manufacturers lack uniform security standards, making them vulnerable to interception and tampering of communication data.

[0006] 4. Data security risks: The power network contains a large amount of sensitive information such as user power consumption data and power grid operation data. If this data is stolen, tampered with, or leaked, it will not only infringe on user privacy but also affect the safe and stable operation of the power grid. For example, attackers can steal real-time load data from the power grid, analyze weak links in the power grid, and launch targeted attacks, leading to power grid collapse.

[0007] Based on the above, an electric power network security defense system and method are invented. Summary of the Invention

[0008] To solve the above technical problems, according to one aspect of the present invention, the present invention provides the following technical solutions:

[0009] A power network security defense system comprising:

[0010] The perception module is used to collect network traffic, device status, and user operation data in the power network in real time based on various deployed security monitoring devices, so as to achieve preliminary perception of network attack behaviors, abnormal operations, and equipment failure security events;

[0011] The transmission module uses encrypted communication technology to ensure the confidentiality and integrity of security monitoring data during transmission, and builds a secure data transmission channel to prevent data theft and tampering. It also has a redundant backup mechanism for data transmission to ensure the reliability of data transmission;

[0012] The analysis module uses machine learning technology to conduct in-depth analysis of the data collected by the perception module. It establishes attack behavior recognition models and abnormal behavior analysis models to accurately identify and warn of network attacks, internal illegal operations, and equipment failure security incidents. At the same time, it mines and analyzes historical security data, summarizes the patterns of security incidents, and provides a basis for security policy formulation.

[0013] The decision-making module is used to formulate corresponding security defense strategies and emergency response plans based on the results of the analysis module and the actual operation and security requirements of the power network;

[0014] The execution module is used to execute the security policies and emergency response plans formulated by the decision module;

[0015] The dynamic policy optimization module is used to use machine learning technology to dynamically adjust security policies based on the real-time security situation of the power network and the effectiveness of policy execution.

[0016] As a preferred solution of the power network security defense system described in the present invention, the analysis module includes:

[0017] The data integration module is used to integrate the data sensed by the perception module. It first uses data cleaning technology to remove duplicate, erroneous, and incomplete data. Then, through data conversion technology, it unifies data in different formats into a standard format that the system can recognize, ultimately forming a structured data set, laying the foundation for subsequent analysis.

[0018] Data preprocessing module, used to preprocess the integrated data and improve data quality;

[0019] The feature extraction and analysis module is configured to extract key features related to security threats from the preprocessed data by using algorithms, so as to analyze the features in depth and mine the hidden rules and potential security threat signs behind the data.

[0020] As a preferred scheme of the power network security defense system, the analysis module further comprises:

[0021] The model construction and training module is configured to construct corresponding machine learning and deep learning models according to common security threat types and attack modes of the power network, and train the models by using historical security data to continuously adjust model parameters, so that the models can accurately identify different types of security events.

[0022] The real-time security event analysis module is configured to input the real-time collected data into the trained model to perform real-time security event analysis, so that the model can determine whether there is abnormal behavior and security attack in the data according to the extracted features and learned patterns; once a suspected security event is detected, the related data is further analyzed in association, and the authenticity and severity of the event are determined in combination with context information.

[0023] The security event warning and feedback module is configured to generate warning information of corresponding levels according to the severity of the event, and timely feedback, and record the related information of the security event in the system log for subsequent security audit and event tracing.

[0024] As a preferred scheme of the power network security defense system, the decision module comprises:

[0025] The receiving and sorting module is configured to receive the security event warning information transmitted by the analysis module in real time, collect the current operation state data of the power network, and sort the information to form a complete decision basis data set.

[0026] The risk comprehensive assessment module is configured to comprehensively assess the received information, analyze the influence of the security event on the power network in combination with the business demand, security target and risk bearing capacity of the power network.

[0027] The security strategy formulation module is configured to formulate corresponding security defense strategies and emergency response schemes according to the risk assessment results, and formulate multiple alternative schemes for different types of security events, and clearly define the starting conditions and execution processes of each scheme to ensure the flexibility and effectiveness of the countermeasures.

[0028] As a preferred scheme of the power network security defense system, the decision module further comprises:

[0029] The strategy and scheme auditing module is used for strictly auditing the formulated security strategy and emergency scheme, checking the rationality of the strategy, the feasibility of the scheme and the synergy between the measures, verifying the execution effect of the scheme in the actual scene through simulation deduction, finding potential problems and timely optimizing and adjusting.

[0030] The instruction issuing and execution supervision is used for converting the security strategy and emergency scheme that pass the auditing into specific execution instructions and issuing the same.

[0031] As a preferred scheme of the power network security defense system, the execution module comprises:

[0032] The instruction receiving and checking module is used for receiving the execution instruction issued by the decision module through a secure communication channel, checking the integrity of the instruction and verifying the identity of the instruction when the instruction is received, and ensuring that the instruction is not tampered in the transmission process by using digital signature or hash checking technology.

[0033] The instruction analysis and task allocation module is used for analyzing the checked instruction, converting the same into specific operations that can be understood and executed by the security devices and systems, and allocating the analyzed task to the corresponding execution unit according to the functions and responsibilities of different security devices, so as to clearly define the execution content and sequence of each device.

[0034] As a preferred scheme of the power network security defense system, the execution module further comprises:

[0035] The security strategy execution module is used for enabling the security devices and systems to strictly execute the security strategy according to the allocated task.

[0036] The execution state real-time monitoring module is used for establishing a real-time monitoring mechanism to continuously monitor the running state of the security devices and systems and the strategy execution condition during the execution of the security strategy.

[0037] As a preferred scheme of the power network security defense system, the dynamic strategy optimization module comprises:

[0038] The data acquisition and integration module is used for first acquiring the security event analysis result of the network attack behavior data, the abnormal flow data and the device vulnerability information from the analysis module, then acquiring the execution effect data of the implemented security strategy from the execution module, collecting the real-time running data of the power network, and finally integrating the data of different sources and different formats, unifying the data standard and format, and constructing a data set for strategy optimization analysis.

[0039] A real-time situation analysis module is configured to utilize a machine learning algorithm to conduct in-depth analysis on the integrated data, construct a power network security situation model, and identify the changing trend of the current network security situation by comparing historical data and real-time data, determine the type, severity and development direction of the security threat, and comprehensively evaluate the matching degree of the current security policy and actual security demand in combination with the business demand and operation state of the power network.

[0040] A policy optimization suggestion generation module is configured to automatically generate security policy optimization suggestions by utilizing an intelligent optimization algorithm according to the real-time situation analysis result, develop diversified optimization schemes for different types of security threats, and perform feasibility analysis and potential impact evaluation on each optimization suggestion to predict the effect and risk that may be brought by the policy adjustment and provide a scientific basis for decision-making.

[0041] As a preferred scheme of the power network security defense system, the dynamic policy optimization module further comprises:

[0042] A policy decision and issuing module is configured to submit the generated policy optimization suggestions to a decision module for reference, and make the decision module audit and adjust the optimization suggestions in combination with expert experience and the actual operation state of the power network to determine the final security policy optimization scheme, and then convert the optimization scheme confirmed by the decision module into specific execution instructions and issue the execution instructions to an execution module through a secure communication channel.

[0043] An execution effect monitoring and feedback module is configured to continuously monitor the policy execution effect data fed back by the execution module during the execution of the optimized policy, evaluate the actual effect of the policy optimization by comparing the changes of network security indexes before and after the policy adjustment, collect new problems and abnormal conditions occurring during the execution process, and timely feed back the monitoring result and feedback information to the module itself as an important basis for the next round of policy optimization.

[0044] A model and policy iterative optimization module is configured to iteratively update the analysis model and optimization algorithm of the module according to the execution effect monitoring and feedback information.

[0045] A power network security defense method comprises the following specific steps:

[0046] S1: A perception module is configured to collect network flow, device state and user operation data information in the power network in real time according to various types of security monitoring devices deployed to realize preliminary perception of network attack behaviors, abnormal operations and device fault security events.

[0047] S2: By using encryption communication technology, the transmission module ensures the confidentiality and integrity of the security monitoring data during transmission, and builds a secure data transmission channel to prevent data from being stolen and tampered with, while having a redundant backup mechanism for data transmission to ensure the reliability of data transmission;

[0048] S3: By using machine learning technology, the analysis module deeply analyzes the data collected by the perception module; establishes attack behavior recognition model and abnormal behavior analysis model to realize accurate identification and early warning of network attacks, internal violation operations and device fault safety events; at the same time, the historical safety data is analyzed and summarized to provide basis for safety policy making;

[0049] S4: According to the results of the analysis module, the decision module formulates corresponding safety defense strategy and emergency response scheme combined with the actual operation situation and safety demand of power network;

[0050] S5: The execution module executes the safety strategy and emergency response scheme formulated by the decision module;

[0051] S6: The dynamic strategy optimization module uses machine learning technology to dynamically adjust the safety strategy according to the real-time safety situation of power network and the effect of strategy execution.

[0052] Compared with the prior art:

[0053] 1. Perception module and external attack threat, device vulnerability problem: The perception module deploys intrusion detection sensors, traffic monitoring probes and other devices, which can collect network traffic, device state and other data in real time. For external attack threats, it can timely discover the penetration behavior of hackers through phishing, malware implantation and other means, such as abnormal network traffic and unauthorized device access. For problems caused by device and system vulnerabilities, by monitoring device running state data, it can identify abnormal behaviors of devices caused by vulnerabilities, such as abnormal instruction operation of devices with remote code execution vulnerabilities being illegally controlled, to realize preliminary perception of safety events and kill attacks in the embryonic state.

[0054] 2. Transmission module and data security risk problem: The transmission module uses encryption communication technology and special safety data transmission channel to effectively solve the problem of data security risk. In the process of data transmission, encryption algorithm ensures data confidentiality to prevent sensitive information such as user electricity data and power grid operation data from being stolen. Redundant backup mechanism guarantees data integrity, even if data tampering occurs during transmission, it can be recovered through backup data to avoid the impact of data leakage or tampering on the safe and stable operation of power grid.

[0055] 3. Analysis module and various security issues: The analysis module uses machine learning and other technologies to deeply analyze the collected data and establish attack behavior and abnormal behavior identification models. For external attacks, it can accurately identify DDoS attacks, SQL injection attacks, and other types. For internal security risks, it can discover behaviors such as unauthorized access and illegal operations by analyzing user operation data. For device and system vulnerability issues, it can predict potential device failures caused by vulnerabilities based on device operation data changes, take preventive measures, and accurately identify and warn of various security incidents.

[0056] 4. Decision module and rapid response issues: The decision module formulates security defense strategies and emergency response plans based on the results of the analysis module, effectively addressing the rapid response issues faced by power networks in the face of security incidents. Whether it's a system anomaly caused by external attacks or a security risk caused by internal personnel misoperation, the decision module can quickly respond, dynamically adjust network access control strategies, isolate attacked devices or areas, start backup systems, avoid security incident expansion, and reduce power supply interruptions and economic losses.

[0057] 5. Execution module and strategy implementation issues: The execution module is responsible for implementing the strategies formulated by the decision module, filtering network traffic, isolating abnormal devices, and controlling user operations through security devices such as firewalls and intrusion prevention systems. For external attacks, the firewall can block attack sources; for internal unauthorized operations, the permission control function limits user access range; for device vulnerabilities, the device can be isolated in a timely manner to ensure the effective implementation of security strategies and improve the security protection capability of power networks. BRIEF DESCRIPTION OF DRAWINGS

[0058] Figure 1 is a schematic diagram of the overall framework of the present application;

[0059] Figure 2 is a schematic diagram of the analysis module framework of the present application;

[0060] Figure 3 is a schematic diagram of the decision module framework of the present application;

[0061] Figure 4 is a schematic diagram of the execution module framework of the present application;

[0062] Figure 5 is a schematic diagram of the dynamic strategy optimization module framework of the present application. DETAILED DESCRIPTION

[0063] To make the purpose, technical solutions and advantages of the present application clearer, the embodiments of the present application will be further described in detail below with reference to the drawings.

[0064] The present application provides a power network security defense system, please refer toFigures 1-5 , comprising:

[0065] a perception module for collecting network traffic, device status, and user operation data information in the power network in real time according to various types of deployed security monitoring devices such as intrusion detection sensors, traffic monitoring probes, and terminal security protection software, to realize preliminary perception of network attack behaviors, abnormal operations, and device fault safety events;

[0066] a transmission module for using encryption communication technology to ensure the confidentiality and integrity of security monitoring data during transmission, to build a secure data transmission channel to prevent data from being stolen and tampered with, and to have a redundant backup mechanism for data transmission to ensure the reliability of data transmission;

[0067] an analysis module for using machine learning technology to perform deep analysis on the data collected by the perception module, to establish attack behavior recognition models and abnormal behavior analysis models, to realize accurate identification and early warning of network attacks, internal rule violations, and device fault safety events, and to mine and analyze historical security data to summarize safety event rules and provide a basis for security policy formulation;

[0068] a decision module for formulating corresponding security defense strategies and emergency response plans according to the results of the analysis module, in combination with the actual operation and safety requirements of the power network;

[0069] an execution module for executing the security strategies and emergency response plans formulated by the decision module;

[0070] a dynamic strategy optimization module for using machine learning technology to dynamically adjust security strategies according to the real-time security situation of the power network and the effects of strategy execution; when the analysis layer detects a new attack pattern or abnormal fluctuations in network traffic, the module uses machine learning algorithms to analyze a large amount of historical data and current data to automatically generate more targeted security strategy adjustment plans; for example, when encountering an unknown type of DDoS attack, the module can quickly optimize the traffic filtering rules of the firewall and dynamically adjust the bandwidth allocation strategy to ensure the normal operation of the business while efficiently resisting attacks.

[0071] By setting the dynamic strategy optimization module, the security strategies can always be adapted to the actual network situation through continuous learning and optimization, improving the adaptive ability of the defense system and reducing the lag and limitations of manual strategy adjustment.

[0072] The analysis module comprises:

[0073] A data integration module is used to integrate the data perceived by the perception module. First, data cleaning techniques are used to remove duplicate, incorrect, and incomplete data. Then, data conversion techniques are used to unify data of different formats into a standard format that can be recognized by the system, and finally, a structured data set is formed to lay the foundation for subsequent analysis.

[0074] A data preprocessing module is used to preprocess the integrated data to improve data quality. Noise reduction algorithms are used to remove noise interference in the data to avoid affecting the accuracy of the analysis results. Normalization processing methods are used to adjust the numerical range of the data to a suitable interval to ensure the weight consistency of different types of data in subsequent analysis. In addition, feature selection and dimensionality reduction processing are also performed to eliminate redundant features, retain key features, reduce data computation, and improve analysis efficiency.

[0075] A feature extraction and analysis module is used to extract key features related to security threats based on the preprocessed data. For network traffic data, features such as traffic size, traffic peak, packet protocol type, source IP and destination IP address distribution are extracted. For device running state data, features such as device CPU usage, memory occupancy, temperature change, and abnormal log quantity are extracted. Through in-depth analysis of the features, the hidden rules and potential security threat signs behind the data can be discovered.

[0076] A model construction and training module is used to construct machine learning and deep learning models based on common security threat types and attack patterns in the power network, such as neural network-based attack behavior recognition models and decision tree-based abnormal behavior analysis models. Historical security data is used to train the models to continuously adjust the model parameters, enabling the models to accurately identify different types of security events. Cross-validation and other methods are used to evaluate the performance of the models during training to optimize the model structure and improve the generalization ability and accuracy of the models.

[0077] A real-time security event analysis module is used to input real-time collected data into the trained model for real-time security event analysis. The model can determine whether there are abnormal behaviors and security attacks in the data based on the extracted features and learned patterns. Once a suspected security event is detected, further correlation analysis of related data is performed to determine the authenticity and severity of the event in combination with contextual information.

[0078] The security event early warning and feedback module is configured to generate early warning information of a corresponding level according to the severity of the event and to timely feedback. The early warning information not only contains basic information such as the type of the security event, the time of occurrence, the range of influence, but also provides an event development trend prediction based on model analysis, thereby providing an important basis for the decision-making layer to formulate a scientific and reasonable security defense strategy and emergency response scheme. Meanwhile, the security event related information is recorded in the system log, thereby facilitating subsequent security audit and event tracing.

[0079] The decision module comprises:

[0080] The receiving and carding module is configured to receive the security event early warning information transmitted by the analysis module in real time, collect current operation state data of the power network such as power grid load, equipment operation parameters, business system operation conditions, and classify and card the information to form a complete decision basis data set;

[0081] The risk comprehensive assessment module is configured to comprehensively assess the received information in depth, analyze the influence of the security event on the power network, including the threat degree to the power supply stability, data security and business continuity, in combination with the business demand, security target and risk bearing capacity of the power network, determine the risk level, and provide a key reference for subsequent decision-making;

[0082] The security strategy formulation module is configured to formulate a corresponding security defense strategy and emergency response scheme according to the risk assessment result. For a low-risk event, a conventional defense strategy such as strengthening monitoring and limiting relevant access rights can be adopted. For a medium or high-risk event, an emergency response program needs to be started, such as isolating the attacked equipment or network area, suspending part of non-critical business to reduce the attack surface, calling a backup system to guarantee power supply, etc. Meanwhile, multiple alternative schemes are formulated for different types of security events, and the starting conditions and execution processes of each scheme are specified to ensure the flexibility and effectiveness of the countermeasures;

[0083] The strategy and scheme auditing module is configured to strictly audit the formulated security strategy and emergency scheme, check the rationality of the strategy, the feasibility of the scheme and the synergy between the measures, verify the execution effect of the scheme in the actual scene through simulation deduction, find potential problems and timely optimize and adjust;

[0084] The instruction issuing and execution supervision module is configured to convert the security strategy and emergency scheme that passes the audit into specific execution instructions and issue the same.

[0085] The execution module comprises:

[0086] The instruction receiving and checking module is configured to receive the execution instructions issued by the decision module through a secure communication channel, including network access control policy adjustment instructions, device isolation instructions, system backup start instructions, etc. When receiving the instructions, the integrity of the instructions is checked and the identity is verified, and the digital signature or hash checking technology is used to ensure that the instructions are not tampered with during transmission.

[0087] The instruction analysis and task allocation module is configured to analyze the instructions that pass the verification and convert them into specific operations that can be understood and executed by each security device and system, such as converting network access control policy instructions into firewall rule configuration parameters and converting device isolation instructions into terminal management system isolation operation instructions for specific devices. At the same time, the tasks after analysis are allocated to corresponding execution units such as firewalls, intrusion prevention systems, and terminal security management software according to the functions and responsibilities of different security devices, and the execution content and sequence of each device are determined.

[0088] The security policy execution module is configured to enable each security device and system to strictly execute the security policy according to the allocated tasks. The firewall filters network traffic according to the new access control rules, blocks access requests that do not comply with the rules, and limits external attacks and internal illegal access. The intrusion prevention system monitors network traffic in real time and actively defends against detected attack behaviors based on the latest attack feature library and detection rules, such as intercepting malicious data packets and resetting network connections. The terminal security management software performs patch updates, virus scans, and peripheral device disabling operations on terminal devices to strengthen the security protection capabilities of terminal devices. The backup system starts the data backup process according to the instructions to ensure the security and recoverability of critical data in the power network.

[0089] The execution state real-time monitoring module is configured to establish a real-time monitoring mechanism during the execution of the security policy to continuously monitor the running state and policy execution of each security device and system.

[0090] The dynamic policy optimization module includes:

[0091] The data collection and integration module is configured to first obtain the security event analysis results of network attack behavior data, abnormal traffic data, and device vulnerability information from the analysis module. Then, the execution effect data of the implemented security policy is obtained from the execution module, such as firewall interception records, intrusion prevention system protection effectiveness, and terminal device security state changes. At the same time, real-time running data of the power network is collected, including power grid load fluctuations and business system running conditions. Finally, the data from different sources and formats are integrated, the data standard and format are unified, and a data set for policy optimization analysis is constructed.

[0092] A real-time situation analysis module is configured to utilize a machine learning algorithm to conduct in-depth analysis on the integrated data, construct a power network security situation model, and identify the changing trend of the current network security situation by comparing historical data and real-time data, determine the type, severity, and development direction of security threats, and comprehensively evaluate the matching degree of the current security policy and actual security demand in combination with the business demand and operation state of the power network;

[0093] A policy optimization suggestion generation module is configured to automatically generate security policy optimization suggestions by using intelligent optimization algorithms such as reinforcement learning and genetic algorithm according to the real-time situation analysis result, develop diversified optimization schemes for different types of security threats, and perform feasibility analysis and potential impact evaluation on each optimization suggestion to predict the effects and risks that may be brought by policy adjustment and provide a scientific basis for decision-making;

[0094] A policy decision and issuance module is configured to submit the generated policy optimization suggestions to a decision module for reference, and combine expert experience and the actual operation state of the power network to enable the decision module to review and adjust the optimization suggestions, determine the final security policy optimization scheme, and then convert the optimization scheme confirmed by the decision module into specific execution instructions and issue the execution instructions to an execution module through a secure communication channel;

[0095] An execution effect monitoring and feedback module is configured to continuously monitor the policy execution effect data fed back by the execution module during the execution of the optimized policy, evaluate the actual effect of the policy optimization by comparing the changes in network security indicators before and after the policy adjustment, such as the number of successful attacks, the number of data leakage events, and the business system interruption time, collect new problems and abnormal conditions that occur during the execution process, such as device compatibility problems and business failures caused by policy conflicts, and feed back the monitoring results and feedback information to the module itself in a timely manner as an important basis for the next round of policy optimization;

[0096] A model and policy iterative optimization module is configured to update the analysis model and optimization algorithm of the module itself according to the execution effect monitoring and feedback information.

[0097] A power network security defense method includes the following specific steps:

[0098] S1: The perception module is configured to collect network flow, device state, and user operation data information in the power network in real time according to various security monitoring devices deployed, to realize preliminary perception of network attack behaviors, abnormal operations, and device fault security events;

[0099] S2: The transmission module uses encrypted communication technology to ensure the confidentiality and integrity of security monitoring data during transmission, and builds a secure data transmission channel to prevent data theft and tampering. At the same time, it has a redundant backup mechanism for data transmission to ensure the reliability of data transmission;

[0100] S3: The analysis module uses machine learning technology to conduct in-depth analysis of the data collected by the perception module. It establishes attack behavior recognition models and abnormal behavior analysis models to accurately identify and provide early warnings for network attacks, internal violations, and equipment failure security incidents. It also mines and analyzes historical security data, summarizes security incident patterns, and provides a basis for security policy formulation.

[0101] S4: Based on the results of the analysis module and the actual operation and security requirements of the power network, the decision module formulates corresponding security defense strategies and emergency response plans;

[0102] S5: Execute the security policies and emergency response plans formulated by the decision module through the execution module;

[0103] S6: Through the dynamic policy optimization module, machine learning technology is used to dynamically adjust the security policy based on the real-time security situation of the power network and the effectiveness of policy execution.

[0104] Although the present invention has been described above with reference to embodiments, various modifications may be made thereto and equivalent components may be substituted without departing from the scope of the present invention. In particular, as long as there are no structural conflicts, the various features of the embodiments disclosed herein may be combined with each other in any manner, and the omission of an exhaustive description of such combinations in this specification is solely for the sake of space and resource conservation. Therefore, the present invention is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

Claims

1. A power network security defense system, characterized in that: include: The perception module is used to collect network traffic, device status, and user operation data in the power network in real time based on various deployed security monitoring devices, so as to achieve preliminary perception of network attack behaviors, abnormal operations, and equipment failure security events; The transmission module uses encrypted communication technology to ensure the confidentiality and integrity of security monitoring data during transmission, and builds a secure data transmission channel to prevent data theft and tampering. It also has a redundant backup mechanism for data transmission to ensure the reliability of data transmission; The analysis module uses machine learning technology to conduct in-depth analysis of the data collected by the perception module. It establishes attack behavior recognition models and abnormal behavior analysis models to accurately identify and warn of network attacks, internal illegal operations, and equipment failure security incidents. At the same time, it mines and analyzes historical security data, summarizes the patterns of security incidents, and provides a basis for security policy formulation. The decision-making module is used to formulate corresponding security defense strategies and emergency response plans based on the results of the analysis module and the actual operation and security requirements of the power network; The execution module is used to execute the security policies and emergency response plans formulated by the decision module; The dynamic policy optimization module is used to use machine learning technology to dynamically adjust security policies based on the real-time security situation of the power network and the effectiveness of policy execution.

2. The power network security defense system according to claim 1, characterized in that: The analysis module includes: The data integration module is used to integrate the data sensed by the perception module. It first uses data cleaning technology to remove duplicate, erroneous, and incomplete data. Then, through data conversion technology, it unifies data in different formats into a standard format that the system can recognize, ultimately forming a structured data set, laying the foundation for subsequent analysis. Data preprocessing module, used to preprocess the integrated data and improve data quality; The feature extraction and analysis module is used to extract key features related to security threats based on preprocessed data using algorithms, so as to be able to explore the hidden patterns and potential security threat signs behind the data through in-depth analysis of the features.

3. The power network security defense system according to claim 2, characterized in that: The analysis module also includes: The model building and training module is used to build corresponding machine learning and deep learning models based on common security threat types and attack patterns in power networks. At the same time, it can also train the model using historical security data to continuously adjust the model parameters so that the model can accurately identify different types of security incidents; The real-time security event analysis module is used to input real-time collected data into the trained model for real-time security event analysis. This allows the model to determine whether there are abnormal behaviors and security attacks in the data based on the extracted features and learned patterns. Once a suspected security event is detected, further correlation analysis of the relevant data is performed, combining contextual information to determine the authenticity and severity of the event. The security incident warning and feedback module is used to generate warning information of corresponding levels according to the severity of the incident and provide timely feedback. At the same time, it will also record relevant information of the security incident in the system log to facilitate subsequent security audits and incident tracing.

4. The power network security defense system according to claim 1, characterized in that: The decision module includes: The receiving and sorting module is used to receive the security incident warning information transmitted by the analysis module in real time. At the same time, it collects the current operating status data of the power network and classifies and sorts the information to form a complete decision-making data set; The comprehensive risk assessment module is used to conduct an in-depth and comprehensive assessment of the received information. It can analyze the potential impact of security incidents on the power network based on the business needs, security goals, and risk tolerance of the power network; Develop a security policy module to formulate corresponding security defense strategies and emergency response plans based on risk assessment results; at the same time, formulate multiple alternative plans for different types of security incidents, and clarify the activation conditions and execution processes of each plan to ensure the flexibility and effectiveness of response measures.

5. The power network security defense system according to claim 4, characterized in that: The decision module also includes: The strategy and plan review module is used to strictly review the formulated security strategies and emergency response plans, checking the rationality of the strategies, the feasibility of the plans, and the synergy between the various measures. Through simulation and deduction, it verifies the effectiveness of the plans in actual scenarios, identifies potential problems, and makes timely optimization and adjustments. Instruction issuance and execution supervision are used to convert approved security policies and emergency plans into specific execution instructions and issue them.

6. The power network security defense system according to claim 1, characterized in that: The execution module includes: The instruction receiving and verification module is used to receive the execution instructions issued by the decision module through a secure communication channel. When receiving the instructions, it will perform integrity verification and identity verification on the instructions, using digital signatures or hash verification technology to ensure that the instructions have not been tampered with during transmission; The instruction parsing and task assignment module is used to parse the verified instructions and convert them into specific operations that can be understood and executed by various security devices and systems. At the same time, it will also assign the parsed tasks to the corresponding execution units according to the functions and responsibilities of different security devices, and clarify the execution content and order of each device.

7. The power network security defense system according to claim 6, characterized in that: The execution module also includes: The security policy execution module is used to ensure that each security device and system strictly implements the security policy according to the assigned tasks; The execution status real-time monitoring module is used to establish a real-time monitoring mechanism during the security policy execution process to continuously monitor the operating status and policy execution of each security device and system.

8. The power network security defense system according to claim 1, characterized in that: The dynamic strategy optimization module includes: The data collection and integration module is used to first obtain security incident analysis results such as network attack behavior data, abnormal traffic data, and device vulnerability information from the analysis module; then obtain the execution effect data of the implemented security policy from the execution module, and at the same time, collect real-time operation data of the power network; finally, integrate data from different sources and formats, unify data standards and formats, and construct a data set for policy optimization analysis; The real-time situation analysis module uses machine learning algorithms to conduct in-depth analysis of the integrated data and build a power network security situation model. By comparing historical data with real-time data, it can identify the changing trends of the current network security situation and determine the type, severity, and development direction of security threats. At the same time, it will also comprehensively evaluate the degree of match between the current security strategy and actual security needs based on the business needs and operating status of the power network. The strategy optimization suggestion generation module is used to automatically generate security strategy optimization suggestions based on real-time situation analysis results and apply intelligent optimization algorithms. It also formulates diversified optimization plans for different types of security threats. At the same time, it conducts feasibility analysis and potential impact assessment on each optimization suggestion, predicts the effects and risks that may be brought about by strategy adjustments, and provides a scientific basis for decision-making.

9. The power network security defense system according to claim 8, characterized in that: The dynamic strategy optimization module also includes: The policy decision-making and delivery module is used to submit the generated policy optimization suggestions to the decision-making module for reference. At the same time, based on expert experience and the actual operation of the power network, the decision-making module reviews and adjusts the optimization suggestions to determine the final security policy optimization plan. Then, the optimization plan confirmed by the decision-making module is converted into specific execution instructions and delivered to the execution module through a secure communication channel. The execution effect monitoring and feedback module is used to continuously monitor the policy execution effect data fed back by the execution module during the optimization policy execution process. It also evaluates the actual effect of policy optimization by comparing changes in network security indicators before and after policy adjustment. At the same time, it collects new problems and abnormal situations that arise during the execution process and promptly feeds the monitoring results and feedback information back to the module itself as an important basis for the next round of policy optimization. The model and strategy iterative optimization module is used to iteratively update its own analysis model and optimization algorithm based on execution effect monitoring and feedback information.

10. A power network security defense method, characterized in that: The specific steps are as follows: S1: Through the perception module, various security monitoring devices are deployed to collect network traffic, device status, and user operation data in the power network in real time to achieve preliminary perception of network attack behaviors, abnormal operations, and equipment failure security events; S2: The transmission module uses encrypted communication technology to ensure the confidentiality and integrity of security monitoring data during transmission, and builds a secure data transmission channel to prevent data theft and tampering. At the same time, it has a redundant backup mechanism for data transmission to ensure the reliability of data transmission; S3: The analysis module uses machine learning technology to conduct in-depth analysis of the data collected by the perception module. It establishes attack behavior recognition models and abnormal behavior analysis models to accurately identify and provide early warnings for network attacks, internal violations, and equipment failure security incidents. It also mines and analyzes historical security data, summarizes security incident patterns, and provides a basis for security policy formulation. S4: Based on the results of the analysis module and the actual operation and security requirements of the power network, the decision module formulates corresponding security defense strategies and emergency response plans; S5: Execute the security policies and emergency response plans formulated by the decision module through the execution module; S6: Through the dynamic strategy optimization module, machine learning technology is used to dynamically adjust the security strategy based on the real-time security situation of the power network and the effectiveness of strategy execution.

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