Adaptive network security early warning system and method based on deep learning

Abstract

The invention provides a self-adaptive network security early warning system and method based on deep learning, and aims to continuously update a threat recognition model through an advanced autonomous learning mechanism to adapt to a rapidly changing network environment. The system establishes a dynamic behavior baseline by integrating multi-source data including network traffic, system logs, user behaviors and the like, and realizes real-time monitoring of network behaviors and accurate identification of abnormal behaviors; once potential threats are detected, the system can automatically trigger preset response measures, such as suspicious traffic isolation and access request blocking, so that actions can be taken quickly before damage caused by the threats; according to the design of the system, the dependence on manual intervention is reduced, the real-time performance and effectiveness of early warning are improved, and the recognition accuracy of unknown attacks is remarkably improved; through the automatic and intelligent early warning mechanism, more reliable and intelligent protection is provided for network space security, and high efficiency and accuracy of network security management are ensured.

Description

Technical Field

[0001] The present invention relates to the field of network security technology, and in particular to an adaptive network security early warning system and method based on deep learning. Background Art

[0002] The present invention relates to the field of network security, and in particular to technologies related to network security early warning systems. In the current information age, the Internet has become a necessary basic environment to ensure the normal operation of society and people's work, study and life, and the importance of network security has become increasingly prominent. The diversification and complexity of network attack methods, such as malware, botnets, denial of service attacks, etc., pose a severe challenge to network security. Traditional network security early warning systems mostly rely on static signature matching and pre-defined rules. These systems perform well when detecting known attack patterns, but are often unable to cope with new attacks and unknown threats.

[0003] With the development of big data and artificial intelligence technology, the field of network security has begun to explore the use of these advanced technologies to improve the performance of early warning systems. As an important branch of artificial intelligence, deep learning has significant advantages in image recognition, natural language processing, and data analysis and processing. However, the application of deep learning technology to network security early warning, especially in real-time monitoring, abnormal behavior detection, and unknown threat prediction, still faces a series of technical challenges such as large data volume, variable behavior patterns, and high real-time requirements.

[0004] There are still some other problems in the actual application of the existing network security early warning system, such as: limited ability to analyze network traffic and log data, it is difficult to quickly and accurately identify abnormal behavior from massive data; insufficient ability to learn and adapt to user behavior, unable to adjust to the best early warning state in time; the user interface of the early warning system is not friendly enough, it is difficult to provide intuitive and easy-to-understand early warning information for network security managers. In addition, the existing system needs to be improved in terms of response speed and automation, and often requires manual intervention to take defensive measures, which may lead to untimely response in the face of rapidly changing network attacks.

[0005] In view of the above problems, the present invention aims to propose an adaptive network security early warning system and method based on deep learning. The system can continuously optimize the threat identification model through a self-learning mechanism, use real-time data for intelligent analysis, quickly and accurately identify abnormal behaviors and unknown threats, and automatically execute response measures, thereby significantly improving the efficiency and effectiveness of network security early warning. Through the application of deep learning technology, the present invention is expected to achieve major technological breakthroughs in the field of network security early warning and provide more reliable and intelligent protection for cyberspace security.

[0006] Technical Solution

[0007] The technical solution of the present invention is to propose an adaptive network security early warning system and method based on deep learning, aiming to improve the intelligence and automation level of network security protection. The following is a specific technical solution:

[0008] The data acquisition module plays a vital role. This module is responsible for real-time monitoring and collecting multi-source data in the network, including: Network traffic data: Capture data packets transmitted in the network, including but not limited to TCP / IP data packets, which carry detailed information of network communications, such as source address, destination address, port number and transmission content. System log: Extract log files from network devices such as servers, routers, firewalls, etc. These logs record key information such as the operating status of the equipment, security events and system warnings. User behavior data: Monitor and record user activities in the network, such as visited web pages, executed commands, file uploads and downloads, etc., to identify user behavior patterns and potential abnormal behaviors. API call information: Monitor the calls of application program interfaces, including the frequency, parameters and return results of calls, to detect possible API abuse or abnormal access patterns. The data acquisition module realizes real-time capture of these data through sensors and agents deployed at key nodes of the network, such as network sniffers and log servers. In addition, the module also has data preprocessing functions, which can preliminarily screen and filter data to reduce the burden of subsequent processing. Through these methods, the data collection module provides the system with a comprehensive, real-time view of the network security situation, providing a solid data foundation for further security analysis and early warning.

[0009] The preprocessing module, after the data acquisition module collects the raw data, the data will be transferred to the preprocessing module for in-depth processing to improve the availability and accuracy of the data. The workflow of this module specifically includes the following key steps: Remove invalid records, duplicate entries and outliers in the data. This step is to eliminate noise and error information that may be caused by network device failure or configuration errors to ensure the cleanliness and reliability of the data. Convert data from different sources and formats into a unified format. Since network devices and applications may use different log formats and data structures, the preprocessing module is responsible for standardizing these data so that they can be processed consistently by the system. Adjust the dimensions and numerical range of the data so that they are in a consistent metric. For example, convert log levels of different magnitudes (such as from 1 to 10) into a standardized severity score (such as 0 to 1). Integrate information from different data sources to create a comprehensive network view. This includes associating data points from different devices and users to build a comprehensive, multi-dimensional network security data model. Extract key features from the raw data, which can effectively represent the basic attributes and behavior patterns of the data, and provide input for subsequent behavior analysis and anomaly detection. The design of the preprocessing module aims to improve the quality and consistency of data through these steps, and provide an accurate, clear and actionable data foundation for subsequent modules such as behavior analysis, anomaly detection and threat identification. Through these meticulous preprocessing work, the system can more effectively analyze and warn of security threats.

[0010] After the behavior analysis module and the data collection module collect the raw data, the data will be transferred to the preprocessing module for in-depth processing to improve the availability and accuracy of the data. The workflow of this module specifically includes the following key steps: Data cleaning: remove invalid records, duplicate entries and outliers in the data. This step is to eliminate noise and erroneous information that may be caused by network device failure or configuration errors to ensure the cleanliness and reliability of the data. Data formatting: convert data from different sources and formats into a unified format. Since network devices and applications may use different log formats and data structures, the preprocessing module is responsible for standardizing the data so that it can be processed consistently by the system. Normalization: adjust the dimension and numerical range of the data so that it is in a consistent metric. For example, convert log levels of different magnitudes (such as from 1 to 10) into a standardized severity score (such as 0 to 1). Data fusion: integrate information from different data sources to create a comprehensive network view. This includes associating data points from different devices and users to build a comprehensive, multi-dimensional network security data model. Feature extraction: Extract key features from raw data. These features can effectively represent the basic attributes and behavior patterns of the data, and provide input for subsequent behavior analysis and anomaly detection. The design of the preprocessing module aims to improve the quality and consistency of data through these steps, and provide an accurate, clear, and actionable data foundation for subsequent modules such as behavior analysis, anomaly detection, and threat identification. Through these meticulous preprocessing work, the system can more effectively analyze and warn security threats.

[0011] The anomaly detection module is a key component responsible for real-time analysis and identification of abnormal behaviors in the network. The working principle and operation process of this module are as follows: Real-time data stream access: The anomaly detection module receives data streams from the preprocessing module. These data streams are cleaned, formatted and normalized, representing the current real-time activities in the network. Comparison with the normal behavior model: This module uses the normal behavior model obtained from the behavior analysis module as a benchmark. This model is established based on historical data and defines the expected normal user behavior and system operation in the network. Application of real-time stream analysis technology: Advanced real-time stream analysis technologies such as complex event processing (CEP) and real-time data analysis algorithms are used to continuously monitor and analyze data streams. These technologies can quickly process and analyze data streams to find patterns that are inconsistent with normal behavior models. Abnormal behavior identification: By setting a series of thresholds and rules, the module can identify abnormal behaviors that deviate from the normal behavior baseline. These anomalies may include unexpected data access patterns, unusual network traffic peaks, or behaviors that match known attack patterns. Multi-dimensional feature analysis: The anomaly detection module not only focuses on a single behavior, but also comprehensively considers the characteristics of multiple dimensions, such as the frequency, duration, source and target of the behavior, to comprehensively assess the risk level of the behavior. Contextual association analysis: The module also considers the context in which the behavior occurs, such as the behavior pattern in a specific time period, the historical behavior of a specific user, etc., to improve the accuracy of anomaly detection and reduce false positives. Once an abnormal behavior is detected, the module generates an anomaly report and triggers an alarm. The report contains detailed information about the abnormal behavior, possible impacts and recommended initial response measures. The design goal of the anomaly detection module is to provide an efficient, sensitive and accurate anomaly monitoring mechanism that can quickly identify potential security threats in various complex network environments, and provide a solid foundation for the system's early warning decision-making and response execution. Through this real-time anomaly detection, the system can achieve early detection and timely response to network threats, thereby improving the overall network security protection capabilities.

[0012] The threat identification module is responsible for in-depth analysis and risk assessment of abnormal behaviors. The following is the detailed workflow of this module: Abnormal behavior input: Receive abnormal behavior data from the anomaly detection module, which has been marked as a possible security risk and requires further analysis and confirmation. Attack pattern database matching: The threat identification module accesses a comprehensive attack pattern database, which contains various known network attack patterns, malicious behavior characteristics and security signatures. The module uses this information as a reference to match and analyze abnormal behaviors. Signature matching technology: Using signature matching technology, the characteristics of abnormal behaviors are compared with attack signatures in the database. These signatures may represent specific malware behaviors, network attack techniques or other suspicious activities. Behavioral feature analysis: In addition to signature matching, the module also uses behavioral analysis technology to evaluate the characteristics of abnormal behaviors, such as frequency, duration, and scope of impact, to determine whether they conform to known attack patterns. Machine learning classification: Apply machine learning algorithms to classify abnormal behaviors. These algorithms can identify and learn new attack patterns and even predict potential categories of unknown attacks. Context association: Further consider the context in which the abnormal behavior occurs, such as user roles, asset importance, network environment, etc., to assess the risk level and possible impact of the behavior. Risk assessment: Based on the matching results and behavior analysis, the module conducts risk assessment on the potential threats of abnormal behaviors and determines the severity and urgency of the threats. Threat identification result output: Finally, the module generates a detailed threat report, including the identified attack types, risk levels, affected assets, and recommended response measures. Feedback mechanism: The threat identification module also has a feedback mechanism to feed back the identification results and accuracy to the self-learning engine in order to continuously optimize the attack pattern database and identification algorithm. The threat identification module is designed to accurately identify and classify network threats through the comprehensive use of multiple analysis techniques and database resources, provide a scientific basis for early warning decisions, and ensure the efficiency and accuracy of the network security early warning system. Through the in-depth analysis of this module, the system can quickly respond to various complex security threats and protect the security and stability of the network environment.

[0013] After receiving the output of the threat identification module, the early warning decision module starts its core function, that is, to conduct a comprehensive assessment of potential security threats and generate corresponding early warning signals accordingly. The following is the specific workflow of this module:

[0014] The module first receives the threat identification results, including a detailed description of the abnormal behavior, the matched attack mode, the risk level and the potential impact. A customized risk assessment algorithm is used to quantify the input threat information. The algorithm considers multiple dimensions, such as the severity of the threat, the potential capabilities of the attacker, the value of the affected assets, etc. Based on the risk assessment results, the module determines the urgency of the threat. This includes an assessment of the speed of threat development and the rapidity of the damage that may be suffered by the network system. The actual impact that the threat may cause is assessed, including potential consequences such as interruption of business operations, data loss, financial loss or reputation damage. Based on the risk and urgency assessment results, the module sets the warning level, such as low, medium, and high, and the corresponding color coding (for example, green, yellow, red). Create a warning signal, including the warning level, threat description, possible impact, recommended response measures and action guide. According to the warning level and the organization's notification process, develop an appropriate notification strategy to ensure that relevant information can be communicated to the responsible management or automated system in a timely manner. The warning decision and the generated warning signal are recorded in the system log to provide data support for future security audits and analysis. The output of the early warning decision module is fed back to the self-learning engine and behavior analysis module to adjust and optimize the model and strategy according to the actual threats. The early warning decision module is a key link in the network security early warning system. It ensures the timeliness and accuracy of the early warning through scientific risk assessment and decision-making. The efficient operation of this module plays a vital role in quickly responding to network security threats and reducing potential damage.

[0015] The response execution module, after receiving the warning signal from the warning decision module, quickly takes a series of automated security response measures. The following is the specific operation process of the module: the module first receives the warning signal from the warning decision module, including the warning level, threat details and recommended response actions. According to the warning level, the response execution module queries the preset security policy database and matches the corresponding automated response measures. These policies may include isolating malicious IP addresses, blocking traffic from specific ports, strengthening network monitoring, etc. Once the response measures are determined, the module automatically triggers the corresponding security operations. For example, if malicious traffic from a certain IP address is detected, the module will automatically update the firewall rules to block all traffic from the IP. For IP addresses or entities identified as malicious, the response execution module will execute isolation measures to prevent them from further interacting with the network, thereby curbing the spread of potential threats. The module can adjust network traffic, such as limiting or blocking the transmission of suspicious data packets, to prevent the spread of malware or data leakage. For network areas or systems involved in the warning, the response execution module can enhance monitoring efforts, such as increasing the level of detail in logging, or starting in-depth data packet analysis. When necessary, the module can dispatch additional security resources, such as increasing the analysis capabilities of the intrusion detection system, or deploying additional security probes to collect more information. While automatically responding, the module will also notify the network security team to ensure that relevant personnel are aware of the current threat situation and the measures taken. The response execution module collects the execution results of the response measures and feeds them back to the early warning decision module and the self-learning engine to adjust the early warning and response strategies in real time. The execution details of all response measures will be recorded and stored for subsequent security audits and analysis to ensure the transparency and traceability of the response measures. The design goal of the response execution module is to achieve fast, accurate and effective security response to minimize the impact of security incidents on network systems. Through the automated response process, the module significantly improves the speed and efficiency of handling network security threats.

[0016] The self-learning engine is an advanced machine learning component that uses deep learning technology to optimize threat identification and behavior analysis models by continuously learning from the system's warning and response results. The engine uses an iterative optimization algorithm, and its calculation formula can be expressed as follows according to the gradient descent method (GD): Among them, θ t represents the model parameters at time t, α is the learning rate, is about the parameter θ t The gradient of the loss function J. The loss function J is used to evaluate the difference between the model prediction and the actual result, and the model parameters are updated through the back propagation algorithm to minimize this difference.

[0017] The self-learning engine achieves self-optimization and learning through the following steps: The engine collects data generated during the early warning decision-making and response execution process, including false positives, missed negatives, and correctly predicted results. Features that help improve the model, such as abnormal behavior patterns, attack characteristics, and response effects, are extracted from the collected data. The model is trained using the extracted features and deep learning architectures (such as convolutional neural networks (CNN) or recurrent neural networks (RNN)), and the model parameters are updated through the above optimization algorithms. The performance of the model is evaluated regularly, and the prediction ability of the model is measured using indicators such as accuracy, recall, and F1 score. The trained model is deployed to the threat identification and behavior analysis modules, replacing the old model to improve the recognition accuracy. The engine continuously monitors new data and feedback during system operation, and continuously iterates the learning process to adapt to the ever-changing network threat environment. Through this self-learning and self-optimization capability, the self-learning engine significantly improves the system's ability to predict and defend against unknown threats, ensuring that the network security early warning system continues to evolve and improve over time.

[0018] The human-computer interaction interface, as an interactive bridge between the system and the administrator, provides a series of functions to enhance the convenience of operation and user experience. The interface uses graphical display to enable administrators to monitor dynamic charts of network traffic and user behavior in real time, real-time updates of system logs, and warning information represented by color coding and icons. In addition, the interface integrates data visualization tools to convert complex data sets into easy-to-understand graphs and trend lines to help administrators quickly identify patterns and anomalies. The system status monitoring section provides an overview of the operating status of network devices and security components, including system health indicators, resource usage, and configuration status. The analysis and reporting module regularly generates detailed security analysis reports, including threat statistics, warning history, and response effect evaluation, to provide decision support for administrators. The human-computer interaction interface also has a command input function, allowing administrators to manually trigger specific response measures based on real-time monitoring results or preset policies, such as adjusting monitoring sensitivity, updating threat identification models, or executing specific security scripts. The feedback mechanism allows administrators to evaluate and annotate the system's warning and response effects, and these feedback information will be used by the self-learning engine to optimize the system's warning accuracy and response efficiency. Through this human-computer interaction interface, administrators can manage and respond to network security incidents more intuitively and flexibly, improving the system's operability and user interaction experience, and ensuring the efficiency and accuracy of network security management.

[0019] The method of the present invention comprises the following steps:

[0020] Step 101: Start the data acquisition module to collect network data in real time.

[0021] Step 102: The data is cleaned and formatted through a pre-processing module.

[0022] Step 103: Use the behavior analysis module to establish and update the normal behavior model.

[0023] Step 104: The anomaly detection module monitors and identifies abnormal behaviors in real time.

[0024] Step 105: The threat identification module analyzes and matches potential threats.

[0025] Step 106: The warning decision module generates a warning signal.

[0026] Step 107: The response execution module takes action according to the warning signal.

[0027] Step 108: The self-learning engine optimizes system performance based on the feedback.

[0028] Step 109: Monitor and operate the system through the human-computer interaction interface.

[0029] The technical solution of the present invention realizes a highly automated and intelligent network security early warning system through the collaborative work of the above modules and steps, effectively improving the ability to identify, warn and respond to complex network threats.

[0030] By adopting the above technical solution, the present invention can bring the following three key technical effects:

[0031] 1. Significantly improve the accuracy and response speed of early warning: This invention integrates deep learning algorithms and real-time stream analysis technology to enable the system to accurately identify abnormal behaviors and potential threats from massive data. Compared with traditional methods, this AI-based analysis can detect and respond to new attack patterns faster, thus significantly improving the accuracy and response speed of the early warning system.

[0032] 2. Achieve highly automated network security management: Through the automated response execution module and self-learning engine, the system of the present invention can independently execute security policies and quickly take defensive measures without human intervention. This automated mechanism not only improves processing efficiency, but also reduces the workload of network security managers, allowing them to focus on more complex security issues.

[0033] 3. Continuous optimization and adaptive learning: The system has the ability to self-learn and self-optimize, and can continuously adjust its behavior analysis and threat identification models based on new threat intelligence and early warning feedback. This continuous learning and adaptation mechanism ensures that the system can maintain a high degree of effectiveness and foresight in the face of evolving cyber attack methods.

[0034] These three technical effects together constitute the core advantage of the present invention, providing a more advanced, intelligent and automated early warning solution for the field of network security. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 A schematic diagram of the system of the present invention;

[0036] Figure 2 A diagram showing a method of using the system of the present invention. DETAILED DESCRIPTION

[0037] The following will be combined with the attached embodiment of the present invention Figure 1 and Figure 2 , the technical solutions in the embodiments of the present invention are clearly and completely described. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0038] In the network security early warning system of the present invention, each step of the implementation method is highly automated and accurate through carefully designed parameters and processes. The following is a detailed description of the specific implementation method, including key parameters:

[0039] Data collection module: Parameter settings: Configure the network sniffer to capture all TCP / IP packets, and set the listening port range to 1-65535. Set the trigger threshold of the API monitoring tool to 100 calls per second. Collect network traffic, system logs, user behavior data, and API call information in real time to ensure the comprehensiveness and real-time nature of the data.

[0040] Preprocessing module: Data cleaning rules include removing duplicate records and invalid data, formatting templates define a unified data structure, and normalization algorithms convert data values ​​to the [0,1] interval. A cleaned, formatted, and normalized data set is generated to provide standardized input for subsequent analysis.

[0041] Behavior analysis module: Use historical data to train the normal behavior model, set the number of iterations of the clustering algorithm to 100, and set the convergence threshold to 0.01. Establish and dynamically update the normal behavior model for comparison of the behavior baseline.

[0042] Anomaly detection module: The parameters of the real-time stream analysis technology include the time window size set to 5 seconds and the threshold of the anomaly scoring mechanism set to 3σ (3 standard deviations). It identifies behaviors that are inconsistent with the normal behavior model and outputs abnormal behavior reports.

[0043] Threat Identification Module: The update cycle of the attack pattern database is set to once a week, and the sensitivity of the signature matching algorithm is set to high to ensure rapid identification of emerging threats. Abnormal behaviors are matched with known attack patterns to identify and classify potential threats.

[0044] Early warning decision module: The parameters of the risk assessment algorithm include the threat severity weight set to 0.7, the impact range coefficient set to 0.3, and the warning level is divided into three levels: low, medium, and high. According to the threat assessment results, an early warning signal is generated, including the warning level and recommended measures.

[0045] Response execution module: Automated response measures include setting the trigger threshold of IP isolation rules to 5 consecutive abnormal behavior detections and the traffic blocking threshold to 1MB of abnormal traffic per second. Automatically execute preset response measures to reduce the potential damage of threats to the system.

[0046] Self-learning engine: The architecture of the deep learning model includes 3 hidden layers, 100 neurons in each layer, the learning rate is set to 0.01, and the number of training iterations is set to 1000. Through continuous learning, the threat identification and behavior analysis models are optimized to improve the system's ability to predict unknown threats.

[0047] Human-computer interaction interface: Data visualization display parameters include real-time data update frequency set to once per second, and early warning information prompt display mode set to pop-up window and sound dual reminder. Provide real-time data visualization display, early warning information prompt, system status monitoring and user command input functions.

[0048] System deployment and testing: Select a network environment to deploy the system, set test scenarios including simulated DDoS attacks and SQL injections, and evaluate the response time and false alarm rate. The system runs stably in the actual network environment, accurately warns and responds to security threats, with a false alarm rate of less than 5%.

[0049] Through these detailed implementation steps and parameter settings, the network security early warning system of the present invention can realize full process automation from data collection to response execution, thereby improving the response speed and accuracy to network security threats.

[0050] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. The above embodiments and descriptions are only preferred examples of the present invention and are not intended to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. An adaptive network security early warning system and method based on deep learning, characterized in that: include: A data collection module that collects network traffic, system logs, user behavior data, and application program interface (API) call information in real time; A preprocessing module to clean, format and normalize the collected data; A behavior analysis module that builds a normal access behavior model based on historical data and dynamically updates the model through a machine learning algorithm; An anomaly detection module to identify deviations and abnormal behavior by comparing real-time data with models of normal behavior; A threat identification module that analyzes abnormal behavior and matches it with known attack patterns to identify potential security threats; An early warning decision module, which is used to evaluate the threat level and generate corresponding early warning signals based on the threat identification results; A response execution module, which is used to automatically execute security response measures based on early warning signals or after user confirmation, such as isolating malicious IPs, blocking malicious traffic, or strengthening monitoring; A self-learning engine that learns from warning and response results to continuously optimize the accuracy of threat identification and behavior analysis; a human-computer interaction interface that displays warning information, system status, and analysis reports to system administrators and receives instructions from administrators; The network security early warning system of the present invention realizes efficient collaborative work through a series of well-designed modules. The data acquisition module is used to capture network traffic, system logs, user behavior data and API call information in real time, providing a comprehensive raw data source for the system; these raw data are cleaned, formatted and normalized by the preprocessing module to ensure data consistency and availability; the behavior analysis module establishes a normal behavior model based on the processed data, and dynamically updates it through a machine learning algorithm to adapt to changes in user behavior; the anomaly detection module quickly identifies deviations and abnormal behaviors by comparing real-time data with the normal behavior model; The threat identification module analyzes these abnormal behaviors and matches them with known attack patterns to identify potential security threats; the early warning decision module evaluates the threat level and generates corresponding early warning signals based on the threat identification results; After receiving the warning signal, the response execution module automatically or after user confirmation executes security response measures, such as isolating malicious IPs, blocking malicious traffic or strengthening monitoring; the self-learning engine learns from the warning and response results, continuously optimizes the accuracy of threat identification and behavior analysis, and improves the system's predictive ability; the human-computer interaction interface displays warning information, system status and analysis reports to the system administrator, and receives the administrator's instructions to achieve transparent management of the system and user-friendly operation; these modules form a complete network security warning and response closed-loop system through close logical connections and data flow interactions.

2. An early warning method using the network security early warning system as claimed in claim 1, characterized in that: The following steps are involved: Step 101: Collect multi-source data in the network in real time through a data acquisition module; Step 102: Using a preprocessing module to clean, format and normalize the collected data; Step 103: Establish a normal behavior model based on historical data through a behavior analysis module, and dynamically update the model; Step 104: using an anomaly detection module to compare the real-time data with the normal behavior model to identify deviations and abnormal behaviors; Step 105: Analyze abnormal behaviors and match them with known attack patterns through a threat identification module to identify potential security threats; Step 106: using the early warning decision module to evaluate the threat level and generate a corresponding early warning signal according to the threat identification result; Step 107: Execute safety response measures automatically or after user confirmation according to the warning signal through the response execution module; Step 108: Use the self-learning engine to learn from the warning and response results to continuously optimize the accuracy of threat identification and behavior analysis; Step 109: Display warning information, system status and analysis reports to the system administrator through the human-computer interaction interface, and receive instructions from the administrator.

3. The early warning method according to claim 2, characterized in that: The behavior analysis module in step 103 uses cluster analysis and outlier detection algorithms to identify and adapt to changes in user behavior patterns.

4. The early warning method according to claim 2, characterized in that: The anomaly detection module in step 104 uses real-time stream analysis technology to quickly identify behaviors that are significantly different from normal behavior models.

5. The early warning method according to claim 2, characterized in that: The threat identification module in step 105 includes an attack pattern database for storing and updating known attack patterns and signatures.

6. The early warning method according to claim 2, characterized in that: The warning decision module in step 106 uses a risk assessment algorithm to determine the urgency of the warning and the necessary response measures.

7. The early warning method according to claim 2, characterized in that: The response execution module in step 107 can be integrated with existing network security devices and systems to achieve coordinated response.

8. The early warning method according to claim 2, characterized in that: The self-learning engine in step 108 uses deep learning technology to continuously improve the system's predictive capabilities and response strategies.

9. The early warning method according to claim 2, characterized in that: The human-computer interaction interface in step 109 provides real-time data visualization, warning history records, and user configuration options to enhance the operability and transparency of the system.

10. A computer program product, characterized in that It includes a computer program stored on a non-transitory computer-readable storage medium, wherein the computer program includes program instructions. When the program instructions are executed by a computer, the steps of the network security early warning method as described in claim 2 can be implemented.

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