Intelligent automatic network security emergency response method and system based on AI self-learning

Abstract

The invention provides an intelligent automatic network security emergency response method and system based on AI self-learning. The system comprises a data acquisition layer, a data preprocessing layer, an AI analysis layer, a decision layer, an automatic response layer and a monitoring and feedback layer. According to the method, through fusion of a machine learning technology, automatic arrangement and a continuous optimization mechanism, full-process closed-loop management from threat detection to disposal is realized. The core advantage of the method lies in the organic integration of technical modules and the combination of self-learning and continuous optimization mechanisms, and the self-learning and continuous optimization mechanisms enable the system to dynamically adjust the model according to the treatment result through feedback loop and reinforcement learning technologies. The self-learning characteristic embodied in the research, for example, in combination with an RAG (Retrieved-Augmented Generation) technology and a vector database, can effectively alleviate the deficiency of the traditional model in the aspect of adaptability.

Description

Technical Field

[0001] The present invention relates to the field of network security technology, and in particular to an intelligent automated network security emergency response method and system based on AI self-learning. Background Art

[0002] With the rapid development of information technology, network security threats are becoming increasingly complex and changeable, and traditional manual intervention methods can no longer meet the needs of rapid response. As network attack methods become increasingly complex, traditional manual intervention methods can no longer meet the needs of rapid response. Therefore, there is an urgent need for a method that can automatically and intelligently handle network security incidents.

[0003] Artificial intelligence (AI) technology, especially machine learning and deep learning, has shown great potential in the field of network security. By analyzing massive amounts of data, AI can identify potential security threats and risks and improve network security protection capabilities. For example, AI-based network security situational awareness technology, with the help of machine learning, deep learning and other algorithms and models, can efficiently process large amounts of network data, logs and events, and identify potential security threats and risks.

[0004] However, existing AI applications are mostly focused on threat detection and early warning, lacking the ability to fully respond to complex cybersecurity incidents. In addition, as attackers introduce machine learning, deep learning and other methods, the automation, intelligence and weaponization of cyber attacks continue to increase, making it more difficult to detect attacks. Summary of the invention

[0005] In view of this, the purpose of the present invention is to provide an intelligent automated network security emergency response method and system based on AI self-learning, which realizes closed-loop management of the entire process from threat detection to disposal by integrating machine learning technology, automated orchestration, and continuous optimization mechanisms. The core advantage of this method lies in the organic integration of technical modules and the combination of self-learning and continuous optimization mechanisms. The latter enables the system to dynamically adjust its model based on the disposal results through feedback loops and reinforcement learning techniques. The self-learning characteristics embodied in the present invention, such as the combination of RAG (Retrieval-Augmented Generation) technology and vector databases, can effectively alleviate the shortcomings of traditional models in adaptability.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: an intelligent automated network security emergency response method based on AI self-learning, comprising the following steps: Step 1: Collect data from various data sources and upload them to the system for further processing; Step 2: Clean and standardize the collected data; Step 3: Conduct security analysis on the data, i.e., the AI model establishes a baseline model by learning the normal behavior patterns in historical data; when comparing real-time data with the baseline model, any deviation will be marked as a potential anomaly; Step 4: Generate a response decision based on the results analyzed in Step 3; Step 5: Execute automated response operations according to the measures generated by the decision-making layer; Step 6: Monitor the event processing flow in real time, collect feedback, retrain the AI model using the feedback data, dynamically adjust the analysis algorithm and response strategy, and generate a report.

[0007] In a preferred embodiment, in the said Step 1, the data sources include: Network traffic data: Obtain real-time network data using a traffic collector; Device logs: Collect operation logs from various devices through a device log collector; Terminal data: Obtain the running status and behavior logs of terminal devices through a proxy or monitoring software; Security event data: Real-time collect and upload security event data through intrusion detection / defense systems, web application firewalls, and security information and event management SIEM.

[0008] In a preferred embodiment, in the said Step 2, the data preprocessing includes: Denoising: Dynamically adjust parameters using the Kalman filter algorithm; Data formatting: Convert the collected data into a unified standard format; Feature extraction: Extract key information from the original data; Data standardization: Standardize the data to provide a consistent data source for AI analysis.

[0009] In a preferred embodiment, in the said Step 3, by taking key features and using standardization and encoding to process the data, a neural network algorithm is adopted to distinguish normal and abnormal traffic; then, the K-means clustering algorithm can discover outliers without labeling the data, which is suitable for detecting unknown attack patterns.

[0010] In a preferred embodiment, the said Step 3 further includes: Intrusion detection technology: Achieve network security protection by integrating supervised learning, deep learning, and protocol analysis technologies; in supervised learning, use a labeled data set to train a classification model to identify known attack patterns such as SQL injection; deep learning detects complex zero-day vulnerability attacks by analyzing the temporal features of network traffic through convolutional neural networks (CNNs) and recurrent neural networks (RNNs); protocol analysis technology is based on the modeling of the TCP / IP protocol stack to discover abnormal behaviors such as port scanning; Threat prediction technology: AI realizes the threat prediction function by integrating time series analysis, pattern matching and risk assessment technology. Based on the long short-term memory network LSTM model, it mines the periodicity of attack behavior and compares the patterns with historical attack vectors and vulnerability features to predict potential similar threats. It generates risk priority scores by integrating vulnerability assessment parameters. It builds a DDoS attack prediction model by monitoring traffic trends such as bandwidth occupancy and request type distribution, realizing the transition from passive response to active defense. Model self-learning: In the field of network security, artificial intelligence models are continuously optimized through online learning, incremental training and feature adaptation technologies; online learning technology dynamically updates model parameters based on real-time data streams to adapt to new attack variants and ensure the timeliness of detection strategies; incremental training technology regularly fine-tunes the model to retain historical knowledge and learn new data sets; feature adaptation technology dynamically adjusts the weights of network traffic features.

[0011] In a preferred embodiment, step 4 includes: automated response decision-making, alarm push mechanism, event recording and analysis, and priority sorting.

[0012] In a preferred embodiment, the automated response decision is specifically as follows: the AI-driven automated response system implements intelligent decision-making through predefined security scripts and reinforcement learning DQN agents, autonomously performs isolation, blocking, and patch repair operations for malicious IP access and terminal infection scenarios, and can optimize strategies in real time based on the security event library; the innovation direction focuses on generative AI to automatically generate customized response processes against new attacks, cross-system linkage firewalls, terminal protection tools to build end-to-end closed-loop defense, and realizes strategy self-evolution through continuous learning of historical event feedback, forming a full-chain adaptive security protection system of "detection-decision-execution-optimization"; The specific alarm push mechanism is as follows: through deep integration with the log service SLS and cloud monitoring, low-latency alarm functions are realized through email and SMS channels; through event classification and AI false alarm filtering technology; a dynamic push strategy based on an intelligent classification mechanism is introduced to dynamically adjust the push objects according to the threat level; generative AI technology is used to automatically generate an alarm summary containing contextual information of the affected system and repair suggestions, and an administrator feedback closed loop is built to continuously optimize the alarm model; Event recording and analysis include: tracing the event handling process by logging the security script; using AI technology to automatically generate analysis reports and closed-loop records to support subsequent audits and improvements; building a multimodal event archive and developing a causal chain system based on root cause analysis technology to automatically correlate cross-log attack paths; converting event data into a structured knowledge base to provide reusable response knowledge assets for intelligent decision-making; The specific priority sorting is as follows: prioritize various security incidents based on the risk value calculation formula: risk value = probability × loss amount; break the barriers between SecOps and risk management systems, use automated orchestration technology to achieve alarm noise reduction, and reduce the repeat alarm rate by more than 60%; establish a continuous monitoring mechanism and event knowledge base to shorten the average response time of high-priority incidents to within 15 minutes.

[0013] In a preferred embodiment, step 5 specifically involves automatically matching the ban period based on traffic behavior analysis, permanently banning the source of persistent DDoS attacks, and temporarily banning short-term scanning behaviors for several hours; at the same time, combined with the threat intelligence scoring system, the ban time for high-confidence malicious IPs is automatically extended.

[0014] In a preferred embodiment, in step 6, the event processing process is monitored in real time through sensors and camera equipment to ensure that each link is executed according to the predetermined strategy and to quickly identify anomalies based on the alarm threshold; key indicators such as response success rate, processing time efficiency, and false alarm rate are systematically collected, and combined with resident feedback or automatic detection to form a closed loop; the AI ​​model is retrained using feedback data to dynamically adjust the analysis algorithm and response strategy; detailed reports are automatically output according to compliance templates, covering event analysis, implementation measures, effect evaluation, and improvement suggestions, while retaining audit tracking records to meet legal requirements.

[0015] The present invention also provides an intelligent automated network security emergency response system based on AI self-learning, which adopts the intelligent automated network security emergency response method based on AI self-learning; it includes a data collection level, a data preprocessing level, an AI analysis level, a decision-making level, an automatic response level, and a monitoring and feedback level.

[0016] Compared with the existing technology, the present invention has the following beneficial effects: This method breaks through the bottlenecks of adaptability, active defense and response efficiency of traditional network security systems through the deep integration of technical modules (AI analysis, automated orchestration, dynamic strategy), continuous optimization driven by self-learning and feedback, and a full-process closed-loop management architecture, and realizes the leap from "passive emergency response" to "intelligent autonomy". This innovation provides a scalable technical paradigm for security protection in complex network environments, significantly improving the intelligence level and overall effectiveness of network security protection. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A schematic diagram of the system structure of a preferred embodiment of the present invention; Figure 2 This is a flow chart of step 3 of a preferred embodiment of the present invention; Figure 3 A schematic diagram of the process of the threat prediction technology in step 3 of the preferred embodiment of the present invention; Figure 4 This is a flow chart of step 4 of a preferred embodiment of the present invention (I); Figure 5 This is a flow chart of step 4 of a preferred embodiment of the present invention (II). DETAILED DESCRIPTION

[0018] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0019] It should be noted that the following detailed descriptions are illustrative and are intended to provide further explanation of the present application. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present application belongs.

[0020] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present application; as used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, they indicate the presence of features, steps, operations, devices, components and / or their combinations.

[0021] The present invention provides an intelligent automated network security emergency response method based on AI self-learning, which is implemented by an intelligent automated network security emergency response system based on AI self-learning. Figure 1-5 The intelligent automated network security emergency response system based on AI self-learning includes a data collection layer, a data preprocessing layer, an AI analysis layer, a decision-making layer, an automatic response layer, and a monitoring and feedback layer.

[0022] 1. Data collection level The main responsibility of the data collection layer is to collect data from various data sources and upload them to the system for further processing. Its main data sources include: Network flow data: Use flow collectors (such as NetFlow, sFlow, etc.) to obtain real-time network data.

[0023] Device logs: Collect operation logs from various devices (including firewalls, routers, switches, etc.) through device log collectors (such as Syslog, Windows event logs, etc.).

[0024] Terminal data: Obtain the operating status and behavior logs of terminal devices (such as PCs, servers, mobile devices, etc.) through agents or monitoring software.

[0025] Security event data: Security event data is collected and uploaded in real time through security devices such as IDS / IPS (intrusion detection / prevention system), WAF (Web application firewall), and SIEM (security information and event management).

[0026] Data collection can adopt a distributed architecture to ensure comprehensive coverage of all devices and traffic in the network.

[0027] 2. Data preprocessing level The data preprocessing level aims to clean and standardize the collected data to lay the foundation for subsequent analysis. Key tasks include: Denoising: Use the Kalman filter algorithm to dynamically adjust parameters to eliminate irrelevant data, redundant data, and abnormal data to ensure data quality.

[0028] Data formatting: Convert the collected data into a unified standard format (such as JSON, CSV, XML, etc.) for unified processing.

[0029] Feature extraction: Extract key information from raw data, such as the source IP, destination IP, port, protocol type, etc. of the traffic.

[0030] Data standardization: Standardize data (such as normalization and standardization) to provide a consistent data source for AI analysis.

[0031] 3. AI analysis level The AI ​​analysis layer uses machine learning (ML) and deep learning (DL) models to perform security analysis on data. The goals of this layer are: Anomaly detection: The AI ​​model builds a baseline model by learning normal behavior patterns from historical data (such as user behavior, network traffic, system calls, etc.). When real-time data is compared with the baseline model, any deviation (such as sudden increase in traffic, abnormal login pattern) will be marked as a potential anomaly.

[0032] This technology extracts key features (such as IP source, request frequency, time interval, access path, etc.), processes data through standardization and encoding, and uses algorithms such as neural networks to distinguish normal and abnormal traffic. The K-means clustering algorithm can then be used to find outliers without labeling data, making it suitable for detecting unknown attack patterns. Compared with traditional technologies, this technology has stronger adaptability and can dynamically optimize detection logic according to traffic changes, thereby reducing false positives.

[0033] Intrusion detection technology: By integrating supervised learning, deep learning, and protocol analysis technology, artificial intelligence models can achieve network security protection. In supervised learning, the classification model is trained with labeled data sets to identify known attack patterns such as SQL injection; deep learning technology, especially convolutional neural networks (CNN) and recurrent neural networks (RNN), can detect complex attacks such as zero-day vulnerabilities by analyzing the timing characteristics of network traffic; and protocol analysis technology is based on the modeling of the TCP / IP protocol stack to discover abnormal behaviors such as port scanning. Typical application examples include using sudden changes in traffic rates to identify distributed denial of service (DDoS) attacks, and detecting malicious code by combining static analysis of API call sequences with dynamic monitoring of file operations, thereby building a multi-dimensional intrusion detection system.

[0034] Threat prediction technology: Artificial intelligence achieves the function of threat prediction by integrating time series analysis, pattern matching and risk assessment technology. Based on models such as long short-term memory networks (LSTM), the periodic laws of attack behaviors can be mined (such as the temporal correlation between brute force cracking and subsequent login behaviors), and historical attack vectors and vulnerability features can be combined for pattern comparison to predict potential similar threats. In addition, by integrating vulnerability assessment parameters, risk priority scores can be generated; in specific cases, by monitoring traffic trends such as bandwidth occupancy and request type distribution, a DDoS attack prediction model can be constructed, thereby achieving a shift from passive response to active defense, improving the timeliness of threat warnings and the accuracy of response strategies.

[0035] Model self-learning: In the field of network security, artificial intelligence models are continuously optimized through online learning, incremental training, and feature adaptation. Online learning technology dynamically updates model parameters based on real-time data streams to adapt to new attack variants (such as encrypted malware) to ensure the timeliness of detection strategies; incremental training technology effectively alleviates the problem of catastrophic forgetting by regularly fine-tuning the model to retain historical knowledge and learn new data sets; feature adaptation technology dynamically adjusts the weights of network traffic features (such as new attack traffic features) to improve detection accuracy.

[0036] 4. Decision-making level The decision-making layer generates response decisions based on the results of artificial intelligence analysis. Including: Automated response decision-making: The AI-driven automated response system makes intelligent decisions through predefined security scripts and reinforcement learning DQN agents. It can autonomously perform isolation, blocking, patch repair and other operations for scenarios such as malicious IP access and terminal infection, and can optimize strategies in real time based on the security event library (for example, dynamically adjust device isolation thresholds and patch deployment frequency). Innovation focuses on using generative AI to automatically generate customized response processes to combat new attacks, build end-to-end closed-loop defenses across systems by linking multiple tools such as firewalls and terminal protection, and achieve self-evolution of strategies through continuous learning of historical event feedback, forming a full-chain adaptive security protection system of "detection-decision-execution-optimization".

[0037] Alarm push mechanism: The AI ​​alarm push system proposed in this study realizes low-latency alarm functions through multiple channels such as email and SMS through deep integration with the log service (SLS) and cloud monitoring. The system ensures the accurate delivery of alarm information through event classification and AI false alarm filtering technology. In terms of innovation, the system introduces a dynamic push strategy based on an intelligent classification mechanism, which dynamically adjusts the push objects according to the threat level (for example, high-risk events are automatically pushed to the management level). In addition, the system uses generative AI technology to automatically generate alarm summaries containing contextual information such as affected systems and repair suggestions, and builds an administrator feedback loop to continuously optimize the alarm model. The system has formed an alarm governance system of "precise classification-semantic enhancement-adaptive iteration", which effectively reduces the redundant notification rate and improves the efficiency of emergency response.

[0038] Event recording and analysis: Standardized event recording is the core foundation of audit and improvement work, and must include key elements such as event causes, disposal measures and results. By executing the log records of security scripts, the event handling process can be traced. The system uses AI technology to automatically generate analysis reports and closed-loop records to support subsequent audits and improvements. In addition, by building a multimodal event archive (integrating operation logs, interface screenshots and raw network traffic data), a causal chain system based on root cause analysis technology was developed to achieve automatic association of cross-log attack paths. The system can also convert event data into a structured knowledge base (such as an attack pattern library, a vulnerability feature library) to provide reusable response knowledge assets for intelligent decision-making.

[0039] Prioritization: Prioritize various security incidents based on the risk value calculation formula (risk value = probability × loss amount) to ensure that high-risk incidents are accurately sorted. At the same time, it is necessary to break the barriers between SecOps and risk management systems, use automated orchestration technology to reduce alarm noise, and reduce the repeat alarm rate by more than 60%. Establish a continuous monitoring mechanism and event knowledge base to shorten the average response time for high-priority incidents to within 15 minutes. This hierarchical governance model can not only avoid 80% of resources being consumed on low-risk alarms, but also ensure that in crisis scenarios such as data leaks, the emergency response process can immediately trigger pre-approved processing plans, ultimately reducing the average impact time of major network security incidents on the business by 45%.

[0040] 5. Automatic response layer The automatic response layer performs automatic response operations based on the measures generated by the decision-making layer. Including: Based on traffic behavior analysis (such as request frequency and protocol anomalies), the blocking period is automatically matched, and persistent DDoS attack sources are permanently blocked, while short-term scanning behaviors are temporarily blocked for several hours. At the same time, combined with the threat intelligence scoring system, the blocking time of high-confidence malicious IPs is automatically extended. Dynamic blocking strategy optimization dynamically adjusts the blocking duration based on traffic patterns (request frequency, protocol type), such as permanently blocking persistent DDoS attack sources, and only blocking short-term scanning behaviors for a few hours, and automatically extending the blocking period in combination with threat intelligence scores (such as high-confidence malicious IP libraries); multi-dimensional blocking synergizes application layer (such as HTTP request filtering) and network layer (IP / port blocking) measures, such as injecting rules into WAF and blocking IPs in response to SQL injection attacks to achieve three-dimensional defense; blocking strategy self-learning trains AI models through feedback such as false blocking logs, optimizes blocking thresholds to reduce accidental injuries, and supports dynamic adjustment of blocking conditions (such as duration, type). In addition, the system also includes an automated response mechanism: infected hosts are automatically isolated to prevent the spread of attacks, patches are automatically pushed or protection is enabled after vulnerabilities are discovered, and traffic filtering (such as feature recognition, rate limiting) is enabled to block malicious traffic based on the attack type. The system improves overall defense effectiveness through dynamism, synergy, and adaptability, while taking into account both accuracy and flexibility.

[0041] 6. Monitoring and feedback layer Improve overall response efficiency through real-time tracking, data integration and intelligent iteration. Specific functions include: process monitoring (real-time monitoring of event processing processes through sensors, cameras and other equipment to ensure that each link is executed according to the predetermined strategy, and quickly identify anomalies based on alarm thresholds); feedback collection (systematic collection of key indicators such as response success rate, processing time, false alarm rate, etc., and combined with resident feedback or automatic detection to form a closed loop); model optimization (using feedback data to retrain AI models, dynamically adjust analysis algorithms and response strategies, such as optimizing decisions through historical database comparison); report generation (automatically output detailed reports according to compliance templates, covering event analysis, implementation measures, effect evaluation and improvement suggestions, while retaining audit tracking records to meet legal requirements). This layer realizes the self-evolution of the response system and the continuous enhancement of risk prevention and control capabilities through the "monitoring-feedback-optimization" cycle mechanism.

[0042] This method breaks through the bottlenecks of adaptability, active defense and response efficiency of traditional network security systems through the deep integration of technical modules (AI analysis, automated orchestration, dynamic strategies), continuous optimization driven by self-learning and feedback, and a full-process closed-loop management architecture, and achieves a leap from "passive emergency response" to "intelligent autonomy". This innovation provides a scalable technical paradigm for security protection in complex network environments, significantly improving the intelligence level and overall effectiveness of network security protection.

Claims

1. An intelligent automated network security emergency response method based on AI self-learning, characterized in that: The following steps are involved: Step 1: Collect data from various data sources and upload them to the system for further processing; Step 2: Clean and standardize the collected data; Step 3: Perform security analysis on the data, that is, the AI ​​model builds a baseline model by learning normal behavior patterns in historical data; when real-time data is compared with the baseline model, any deviation will be marked as a potential anomaly; Step 4: Generate a response decision based on the results of the analysis in step 3; Step 5: Execute automated response operations based on the measures generated by the decision-making layer; Step 6: Monitor the event handling process in real time, collect feedback, use feedback data to retrain the AI ​​model, dynamically adjust the analysis algorithm and response strategy, and produce reports.

2. According to claim 1, an intelligent automated network security emergency response method based on AI self-learning is characterized in that: In step 1, the data source includes: Network traffic data: Use traffic collectors to obtain real-time network data; Device log: collect operation logs from various devices through the device log collector; Terminal data: Obtain the operating status and behavior logs of terminal devices through agents or monitoring software; Security event data: Security event data is collected and uploaded in real time through intrusion detection / prevention systems, web application firewalls, and security information and event management (SIEM).

3. According to claim 1, an intelligent automated network security emergency response method based on AI self-learning is characterized in that: The data preprocessing in step 2 includes: denoising: dynamically adjusting parameters using the Kalman filter algorithm; data formatting: converting the collected data into a unified standard format; feature extraction: extracting key information from the raw data; data standardization: standardizing the data to provide a consistent data source for AI analysis.

4. According to claim 1, an intelligent automated network security emergency response method based on AI self-learning is characterized in that: In step 3, by taking key features and applying standardization and encoding to process data, a neural network algorithm is used to distinguish normal and abnormal traffic; then, the K-means clustering algorithm is used to find outliers without labeling data, which is suitable for detecting unknown attack patterns.

5. According to claim 4, an intelligent automated network security emergency response method based on AI self-learning is characterized in that: The step 3 also includes: Intrusion detection technology: Network security protection is achieved by integrating supervised learning, deep learning and protocol analysis technology. In supervised learning, the classification model is trained with labeled data sets to identify known attack patterns such as SQL injection. Deep learning uses convolutional neural networks (CNN) and recurrent neural networks (RNN) to analyze the timing characteristics of network traffic to detect complex attacks with zero-day vulnerabilities. Protocol analysis technology is based on modeling of the TCP / IP protocol stack to detect abnormal behaviors such as port scanning. Threat prediction technology: AI realizes the threat prediction function by integrating time series analysis, pattern matching and risk assessment technology. Based on the long short-term memory network LSTM model, it mines the periodicity of attack behavior and compares the patterns with historical attack vectors and vulnerability features to predict potential similar threats. It generates risk priority scores by integrating vulnerability assessment parameters. It builds a DDoS attack prediction model by monitoring traffic trends such as bandwidth occupancy and request type distribution, realizing the transition from passive response to active defense. Model self-learning: In the field of network security, artificial intelligence models are continuously optimized through online learning, incremental training and feature adaptation technologies; online learning technology dynamically updates model parameters based on real-time data streams to adapt to new attack variants and ensure the timeliness of detection strategies; incremental training technology regularly fine-tunes the model to retain historical knowledge and learn new data sets; feature adaptation technology dynamically adjusts the weights of network traffic features.

6. According to claim 1, an intelligent automated network security emergency response method based on AI self-learning is characterized in that: The step 4 includes: automated response decision, alarm push mechanism, event recording and analysis, and priority sorting.

7. The intelligent automated network security emergency response method based on AI self-learning according to claim 6 is characterized in that: The automated response decision is specifically: the AI-driven automated response system implements intelligent decision-making through predefined security scripts and reinforcement learning DQN agents, autonomously performs isolation, blocking, and patch repair operations for malicious IP access and terminal infection scenarios, and can optimize strategies in real time based on the security event library; The innovation direction focuses on using generative artificial intelligence to automatically generate customized response processes to combat new attacks, linking firewalls and terminal protection tools across systems to build end-to-end closed-loop defense, and achieving strategy self-evolution through continuous learning of historical event feedback, forming a full-chain adaptive security protection system of "detection-decision-execution-optimization"; The specific alarm push mechanism is as follows: through deep integration with the log service SLS and cloud monitoring, low-latency alarm functions are realized through email and SMS channels; through event classification and AI false alarm filtering technology; a dynamic push strategy based on an intelligent classification mechanism is introduced to dynamically adjust the push objects according to the threat level; generative AI technology is used to automatically generate an alarm summary containing contextual information of the affected system and repair suggestions, and an administrator feedback closed loop is built to continuously optimize the alarm model; Event recording and analysis include: tracing the event handling process by logging the security script; using AI technology to automatically generate analysis reports and closed-loop records to support subsequent audits and improvements; building a multimodal event archive and developing a causal chain system based on root cause analysis technology to automatically correlate cross-log attack paths; Convert event data into a structured knowledge base to provide reusable response knowledge assets for intelligent decision-making; Priority sorting is as follows: Based on the risk value calculation formula: risk value = probability × loss amount, various security events are prioritized; the gap between SecOps and risk management systems is broken, and alarm noise reduction is achieved with the help of automated orchestration technology, reducing the repeated alarm rate by more than 60%; Establish a continuous monitoring mechanism and event knowledge base to reduce the average response time for high-priority incidents to within 15 minutes.

8. The intelligent automated network security emergency response method based on AI self-learning according to claim 1 is characterized in that: The step 5 specifically involves automatically matching the ban cycle based on traffic behavior analysis, permanently banning the source of persistent DDoS attacks, and temporarily banning short-term scanning behaviors for several hours; at the same time, combined with the threat intelligence scoring system, the ban time for high-confidence malicious IPs is automatically extended.

9. The intelligent automated network security emergency response method based on AI self-learning according to claim 1 is characterized in that: In step 6, the event handling process is monitored in real time through sensors and camera equipment to ensure that each link is executed according to the predetermined strategy and anomalies are quickly identified based on the alarm threshold; key indicators such as response success rate, processing time, and false alarm rate are systematically collected, and combined with resident feedback or automatic detection to form a closed loop; the AI ​​model is retrained using feedback data, and the analysis algorithm and response strategy are dynamically adjusted; detailed reports are automatically output according to compliance templates, covering event analysis, implementation measures, effect evaluation, and improvement suggestions, while retaining audit tracking records to meet legal requirements.

10. An intelligent automated network security emergency response system based on AI self-learning, characterized in that: An intelligent automated network security emergency response method based on AI self-learning as described in any one of claims 1 to 9 is adopted; It includes data collection level, data preprocessing level, AI analysis level, decision-making level, automatic response level, and monitoring and feedback level.

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