Network flow analysis system based on machine learning

Abstract

The invention provides a network flow analysis system based on machine learning, and belongs to the technical field of network flow analysis. The network flow analysis module comprises a data acquisition and preprocessing module, a feature extraction module, an anomaly detection module, a deep analysis module, a comprehensive analysis and learning module, a trapping and tracing module, a real-time response and display module and a system optimization and promotion module; the data acquisition and preprocessing module is responsible for acquiring original traffic data from a network and carrying out necessary preprocessing operation, the feature extraction module extracts key feature information, the anomaly detection module analyzes extracted features and identifies an abnormal behavior mode, the deep analysis module carries out deep analysis on traffic marked as abnormal, and the abnormal behavior mode is identified. The comprehensive analysis and learning module comprehensively analyzes the results of all the modules to improve the accuracy and reliability of the system, and the trapping and tracing module is responsible for trapping and tracing the detected network threats.

Description

Technical Field

[0001] The present invention relates to the technical field of network traffic analysis, and in particular to a network traffic analysis system based on machine learning. Background Art

[0002] With the rapid development and widespread application of the Internet, network traffic has shown explosive growth and increasingly complex characteristics. Traditional network traffic analysis methods have gradually become incapable of dealing with massive, high-dimensional and diverse traffic data. In the current network environment, various applications and services are emerging in an endless stream, and the types and behavior patterns of network traffic have become extremely rich and varied. Analysis methods based on rules and features are difficult to accurately identify and classify new types of network traffic, and cannot effectively predict the development trend of traffic.

[0003] Since traditional network traffic analysis systems lack intelligent learning and adaptive capabilities, they may not only misjudge normal network behaviors as threats, resulting in a high false alarm rate and wasting time and resources of the security team, but also may not be able to accurately detect disguised or obfuscated network threats. Attackers can use a variety of technical means to circumvent rule-based detection systems, which greatly limits the effectiveness of traditional systems in practical applications. To address the above problems, a network traffic analysis system based on machine learning is proposed. Summary of the invention

[0004] The present invention provides a network traffic analysis system based on machine learning, aiming to solve the problem that the traditional system in the prior art lacks intelligent learning and adaptive capabilities, which may misjudge normal network behaviors as threats and lead to a high false alarm rate.

[0005] The present invention is implemented as follows: comprising a network traffic analysis module, the network traffic analysis module comprising a data acquisition and preprocessing module, a feature extraction module, an anomaly detection module, a deep analysis module, a comprehensive analysis and learning module, a trapping and tracing module, a real-time response and display module and a system optimization and improvement module;

[0006] The data collection and preprocessing module is responsible for collecting raw traffic data from the network and performing necessary preprocessing operations;

[0007] The feature extraction module is responsible for analyzing network traffic data, extracting key feature information, and using these features for subsequent anomaly detection and in-depth analysis;

[0008] The anomaly detection module is responsible for analyzing the extracted features using machine learning algorithms to identify abnormal behavior patterns;

[0009] The deep analysis module is responsible for performing deep analysis on the traffic marked as abnormal to determine whether it is a real network threat;

[0010] The comprehensive analysis and learning module is responsible for comprehensively analyzing the results of each module to improve the accuracy and reliability of the system;

[0011] The trapping and tracing module is responsible for trapping and tracing the detected network threats;

[0012] The real-time response and display module is responsible for displaying the system's detection results to the user in real time, and providing corresponding measures to block malicious connections and send alarm notifications;

[0013] The system optimization and improvement module is responsible for optimizing and improving the performance of the entire system.

[0014] Preferably, the data acquisition and preprocessing module includes a data packet capture submodule and a data preprocessing submodule, the data packet capture submodule collects traffic data from various nodes and devices in the network, including data packets and traffic logs: uses a high-performance network interface card (NIC) and WinPcap data packet capture driver, sets a capture filter, only captures the required data packet types and protocols, installs a specific Linux operating system to support the data packet capture tool, and the data preprocessing submodule cleans, filters and converts the collected data to remove invalid data, providing clean and unified format data for subsequent analysis: uses the Hadoop data processing framework, applies data cleaning algorithms, removes duplicate data, fills missing values ​​and corrects erroneous data, and uses regular expressions and string processing functions to perform data filtering and format conversion.

[0015] Preferably, the feature extraction module includes a traditional feature extraction submodule, a deep learning feature automatic extraction submodule and an enhanced feature extraction submodule. The traditional feature extraction submodule extracts common source address and destination address network traffic features: by writing specific code logic, parsing the header and payload of the data packet, and extracting common features. The deep learning feature automatic extraction submodule uses a deep learning model to automatically learn and extract features: construct a deep learning model, use a convolutional neural network (CNN) to learn the original traffic data, and automatically extract high-level features. The enhanced feature extraction submodule further mines and strengthens valuable features: using feature engineering technology, feature combination, feature transformation and feature selection, to mine and strengthen valuable features.

[0016] Preferably, the anomaly detection module includes a statistical analysis submodule and a deep learning anomaly detection submodule. The statistical analysis submodule calculates the statistical indicators of the traffic based on the established normal network behavior baseline model, compares the current traffic, and discovers abnormal behavior patterns: calculates the statistical indicators of the mean, variance, standard deviation, skewness and kurtosis of the traffic data, establishes a statistical model of normal network behavior, and discovers anomalies by comparing the statistical indicators of the current traffic data with the model. The deep learning anomaly detection submodule uses deep learning technology to detect complex abnormal patterns: constructs a deep learning model, uses an autoencoder (AE) to learn normal traffic data, and then identifies anomalies by detecting deviations between new data and the learned normal pattern.

[0017] Preferably, the deep analysis module includes a deep packet detection submodule, an encrypted traffic analysis submodule and an intelligent sandbox module. The deep packet detection submodule is responsible for deeply analyzing the content of the data packet, including the payload part, to detect whether there is hidden malicious code and disguised information: using deep packet detection technology, protocol parsing library and rule engine, deeply analyzing the payload content of the data packet; the encrypted traffic analysis submodule is responsible for using specific algorithms and technologies to analyze the encrypted traffic and identify potential signs of threats: using cryptographic analysis technology and machine learning algorithms, analyzing the metadata (packet size and time interval) of the encrypted traffic, and trying to identify patterns and anomalies in the encrypted traffic; the intelligent sandbox module is responsible for running suspicious traffic or files in an isolated environment, observing their behavior, and determining whether there is a threat: building a virtual isolation environment, using a virtual machine, running suspicious traffic or files in it, and using monitoring tools to observe their behavior.

[0018] Preferably, the comprehensive analysis and learning module includes a threat intelligence integration submodule, a machine learning submodule and a dynamic rule generation submodule. The threat intelligence integration submodule obtains external threat intelligence data and performs correlation analysis with local traffic data: connects with the threat intelligence provider through an API interface to obtain the latest threat intelligence data, and uses data fusion technology to correlate and integrate external threat intelligence with local traffic data. The machine learning submodule uses a deep learning algorithm and a convolutional neural network (CNN) to train and learn traffic data to establish a prediction and classification model: constructs a deep learning model and trains the model using training data. The dynamic rule generation submodule dynamically generates new detection rules based on the results of machine learning and real-time analysis: generates new detection rules using a rule generation algorithm based on the output results of the machine learning submodule and real-time traffic analysis data.

[0019] Preferably, the trapping and tracing module includes a trapping submodule and a tracing analysis submodule. The trapping submodule is responsible for attracting attackers and collecting their behavior data to provide the system with more threat characteristics and clues: setting up false services or resources in the network to attract attackers' access, and using monitoring tools to record attackers' behavior and interaction data. The tracing analysis submodule is used to trace the source, propagation path and scope of influence of the threat after it is detected: using network tracking technology and data analysis methods to analyze the source, propagation path and scope of influence of the threat.

[0020] Preferably, the real-time response and display module includes a real-time response submodule and a result display and alarm submodule, wherein the real-time response submodule is responsible for displaying the system's detection results to the user in real time, and the result display and alarm submodule is used to present the analysis results to security personnel in a visual manner and to issue an alarm in time when a serious threat is found: using data visualization tools and an alarm system, the analysis results are displayed to security personnel in the form of intuitive charts and reports, and an alarm is issued in time when a serious threat is found.

[0021] Preferably, the real-time response submodule includes a threat detection submodule, a response decision submodule and an execution submodule. The threat detection submodule is responsible for real-time monitoring of threats in traffic: using real-time monitoring tools to quickly analyze real-time traffic and detect threats. The response decision submodule is used to formulate response strategies based on the detection results: based on predefined strategies and rules, response decisions are formulated according to the results of the threat detection submodule. The execution submodule executes response operations, intercepts and alerts them: and executes response operations through interfaces with network devices and security systems.

[0022] Preferably, the system optimization and improvement module includes an integrated automated machine learning submodule, a deployed behavior analysis and anomaly detection module submodule, a real-time learning and adaptation mechanism submodule, and an enhanced model interpretability and transparency submodule. The integrated automated machine learning submodule is used to realize automated feature selection, model selection and hyperparameter optimization processes: using automated machine learning tools and frameworks, feature selection, model selection and hyperparameter optimization are automatically performed through Auto-sk l earn; the deployed behavior analysis and anomaly detection module submodule is used to continuously monitor and analyze network behavior and detect anomalies in a timely manner: using behavior analysis tools and anomaly detection algorithms, continuously monitoring and analyzing network behavior; the real-time learning and adaptation mechanism submodule enables the system to update models and rules in real time and adapt to new threats: by setting a scheduled update mechanism and an online learning algorithm, the system can receive new data and update models and rules in real time; the enhanced model interpretability and transparency submodule is used to improve the comprehensibility of the model, which is convenient for security personnel to make decisions: using model interpretation technology, the interpretability of the model is improved through feature importance analysis, which is convenient for security personnel to make decisions.

[0023] The network traffic analysis system based on machine learning provided by the present invention, when the system is used, first, a data packet capture submodule captures network traffic data through a network interface card, and then passes the captured data to a data preprocessing submodule for cleaning, screening and format conversion to obtain preprocessed data, a feature extraction module receives the preprocessed data, a traditional feature extraction submodule extracts common features, a deep learning feature automatic extraction submodule automatically learns high-level features, an enhanced feature extraction submodule strengthens and mines features, an anomaly detection module obtains data output by the feature extraction module, a statistical analysis submodule calculates statistical indicators and compares them with a baseline model, a deep learning anomaly detection submodule detects anomaly patterns through a deep learning model, a deep analysis module receives data, a deep packet detection submodule analyzes data packet payloads, and an encrypted traffic analysis submodule analyzes Encrypted traffic, the intelligent sandbox module isolates and analyzes suspicious traffic and files, the threat intelligence integration submodule of the comprehensive analysis and learning module obtains and integrates threat intelligence, the machine learning submodule performs model training and learning, the dynamic rule generation submodule formulates new rules based on the results, the trapping submodule sets traps and collects attacker data, the source tracing analysis submodule traces and analyzes threats, the threat detection submodule detects threats in real time, the response decision submodule formulates strategies, the execution submodule executes response operations, the result display and alarm submodule displays results and issues alarms, and finally the integrated automated machine learning submodule automatically optimizes the model, the deployment behavior analysis and anomaly detection module submodule continuously monitors network behavior, and the real-time learning and adaptation mechanism submodule is updated in real time, and the model explanation and transparency enhancement submodule improves model explainability;

[0024] The system can dynamically generate new detection rules based on real-time analysis and machine learning results to respond to ever-changing network threats in a timely manner. At the same time, it can be associated and integrated with external threat intelligence data, enriching the information sources for analysis and improving the ability to discover new and potential threats. The introduction of intelligent sandbox technology to isolate and analyze suspicious traffic and files increases the depth and comprehensiveness of detection. By setting traps and tracing the source of threats, it provides a more in-depth analysis method for preventing and responding to threats. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following is a brief introduction to the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this technical field, other drawings can be obtained based on these drawings without creative work. As shown in the accompanying drawings, the above and other purposes, features and advantages of the present invention will be clearer. The same reference numerals in all the drawings indicate the same parts. The drawings are not deliberately scaled to the actual size, and the focus is on illustrating the main purpose of the present invention.

[0026] Figure 1 It is a schematic diagram of the process structure of the network traffic analysis system based on machine learning provided by the present invention;

[0027] Figure 2 It is a structural schematic diagram of the real-time response and display module of the network traffic analysis system based on machine learning provided by the present invention. DETAILED DESCRIPTION

[0028] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0029] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in the technical field without creative work are within the scope of protection of the present invention.

[0030] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0031] Furthermore, the terms “first”, “second”, etc. are merely used for distinguishing descriptions and should not be understood as indicating or implying relative importance.

[0032] For example, see Figure 1-2 .

[0033] This embodiment provides a network traffic analysis system based on machine learning, including a network traffic analysis module, which includes a data collection and preprocessing module, a feature extraction module, an anomaly detection module, a deep analysis module, a comprehensive analysis and learning module, a trapping and tracing module, a real-time response and display module, and a system optimization and improvement module;

[0034] The data collection and preprocessing module is responsible for collecting raw traffic data from the network and performing necessary preprocessing operations;

[0035] The feature extraction module is responsible for analyzing network traffic data, extracting key feature information, and using these features for subsequent anomaly detection and in-depth analysis;

[0036] The anomaly detection module is responsible for analyzing the extracted features using machine learning algorithms to identify abnormal behavior patterns;

[0037] The deep analysis module is responsible for conducting in-depth analysis on traffic marked as abnormal to determine whether it is a real network threat;

[0038] The comprehensive analysis and learning module is responsible for comprehensive analysis of the results of each module to improve the accuracy and reliability of the system;

[0039] The trapping and tracing module is responsible for trapping and tracing the detected network threats;

[0040] The real-time response and display module is responsible for displaying the system's detection results to users in real time and providing corresponding measures to block malicious connections and send alarm notifications;

[0041] The system optimization and improvement module is responsible for optimizing and improving the performance of the entire system.

[0042] The data collection and preprocessing module includes a packet capture submodule and a data preprocessing submodule. The packet capture submodule collects traffic data from various nodes and devices in the network, including packets and traffic logs: using a high-performance network interface card (NIC) and WinPcap packet capture driver, by setting capture filters, only the required packet types and protocols are captured, and a specific Linux operating system is installed to support the packet capture tool. The data preprocessing submodule cleans, filters and converts the collected data, removes invalid data, and provides clean and unified format data for subsequent analysis: using the Hadoop data processing framework, applying data cleaning algorithms, removing duplicate data, filling missing values ​​and correcting erroneous data, and using regular expressions and string processing functions for data filtering and format conversion;

[0043] First, the packet capture submodule captures network traffic data through the network interface card, and then passes the captured data to the data preprocessing submodule for cleaning, filtering and format conversion to obtain preprocessed data.

[0044] The feature extraction module includes a traditional feature extraction submodule, a deep learning feature automatic extraction submodule and an enhanced feature extraction submodule. The traditional feature extraction submodule extracts common source address and destination address network traffic features: by writing specific code logic, parsing the header and payload of the data packet, and extracting common features. The deep learning feature automatic extraction submodule uses a deep learning model to automatically learn and extract features: building a deep learning model, using a convolutional neural network (CNN) to learn the original traffic data, and automatically extracting high-level features. The enhanced feature extraction submodule further mines and strengthens valuable features: using feature engineering technology, feature combination, feature transformation and feature selection, to mine and strengthen valuable features;

[0045] The feature extraction module receives the preprocessed data, the traditional feature extraction submodule extracts common features, the deep learning feature automatic extraction submodule automatically learns high-level features, and the enhanced feature extraction submodule strengthens and mines the features.

[0046] The anomaly detection module includes a statistical analysis submodule and a deep learning anomaly detection submodule. The statistical analysis submodule calculates the statistical indicators of traffic based on the established normal network behavior baseline model, compares the current traffic, and discovers abnormal behavior patterns: calculates the statistical indicators of the mean, variance, standard deviation, skewness and kurtosis of the traffic data, establishes a statistical model of normal network behavior, and discovers anomalies by comparing the statistical indicators of the current traffic data with the model. The deep learning anomaly detection submodule uses deep learning technology to detect complex abnormal patterns: builds a deep learning model, uses an autoencoder (AE) to learn normal traffic data, and then identifies anomalies by detecting the deviation of new data from the learned normal pattern;

[0047] The data output by the feature extraction module is obtained, the statistical analysis submodule calculates statistical indicators and compares them with the baseline model, and the deep learning anomaly detection submodule detects abnormal patterns through the deep learning model.

[0048] The deep analysis module includes a deep packet inspection submodule, an encrypted traffic analysis submodule and an intelligent sandbox module. The deep packet inspection submodule is responsible for deeply analyzing the contents of the data packet, including the payload part, to detect whether there is hidden malicious code and disguised information: using deep packet inspection technology, protocol parsing library and rule engine to deeply analyze the payload content of the data packet. The encrypted traffic analysis submodule is responsible for using specific algorithms and technologies to analyze the encrypted traffic and identify potential signs of threats: using cryptographic analysis technology and machine learning algorithms to analyze the metadata (packet size and time interval) of the encrypted traffic, and try to identify patterns and anomalies in the encrypted traffic. The intelligent sandbox module is responsible for running suspicious traffic or files in an isolated environment, observing their behavior, and determining whether there is a threat: building a virtual isolated environment, using a virtual machine, running suspicious traffic or files in it, and using monitoring tools to observe their behavior;

[0049] After receiving data, the deep packet inspection submodule analyzes the data packet payload, the encrypted traffic analysis submodule analyzes the encrypted traffic, and the intelligent sandbox module isolates and analyzes suspicious traffic and files.

[0050] The comprehensive analysis and learning module includes a threat intelligence integration submodule, a machine learning submodule, and a dynamic rule generation submodule. The threat intelligence integration submodule obtains external threat intelligence data and performs correlation analysis with local traffic data: it connects with the threat intelligence provider through the API interface to obtain the latest threat intelligence data, and uses data fusion technology to correlate and integrate external threat intelligence with local traffic data. The machine learning submodule uses deep learning algorithms and convolutional neural networks (CNNs) to train and learn traffic data and establish prediction and classification models: it builds a deep learning model and uses training data to train the model. The dynamic rule generation submodule dynamically generates new detection rules based on the results of machine learning and real-time analysis: it uses the rule generation algorithm to generate new detection rules based on the output results of the machine learning submodule and real-time traffic analysis data;

[0051] The threat intelligence integration submodule acquires and integrates threat intelligence, the machine learning submodule performs model training and learning, and the dynamic rule generation submodule formulates new rules based on the results.

[0052] The trapping and tracing module includes the trapping submodule and the tracing analysis submodule. The trapping submodule is responsible for attracting attackers and collecting their behavior data to provide the system with more threat characteristics and clues: setting up fake services or resources in the network to attract attackers' access, and using monitoring tools to record attackers' behavior and interaction data. The tracing analysis submodule is used to trace the source, propagation path, and impact range of the threat after it is detected: using network tracking technology and data analysis methods to analyze the source, propagation path, and impact range of the threat;

[0053] The trapping submodule sets traps and collects attacker data, while the source analysis submodule traces and analyzes threats.

[0054] The real-time response and display module includes a real-time response submodule and a result display and alarm submodule. The real-time response submodule is responsible for displaying the system's detection results to users in real time. The result display and alarm submodule is used to present the analysis results to security personnel in a visual manner and issue an alarm in time when a serious threat is found: using data visualization tools and alarm systems, the analysis results are presented to security personnel in the form of intuitive charts and reports, and alarms are issued in time when a serious threat is found. The real-time response submodule includes a threat detection submodule, a response decision submodule and an execution submodule. The threat detection submodule is responsible for real-time monitoring of threats in traffic: using real-time monitoring tools to quickly analyze real-time traffic and detect threats. The response decision submodule is used to formulate response strategies based on the detection results: based on predefined strategies and rules, response decisions are formulated according to the results of the threat detection submodule. The execution submodule executes response operations, intercepts and alarms them: through the interface with network equipment and security systems, response operations are executed;

[0055] The threat detection submodule detects threats in real time, the response decision submodule formulates strategies, the execution submodule performs response operations, and the result display and alarm submodule displays results and issues alarms.

[0056] The system optimization and improvement module includes an integrated automated machine learning submodule, a deployed behavior analysis and anomaly detection module submodule, a real-time learning and adaptation mechanism submodule, and a model explanation and transparency enhancement submodule. The integrated automated machine learning submodule is used to realize automated feature selection, model selection, and hyperparameter optimization processes: using automated machine learning tools and frameworks, automatically performing feature selection, model selection, and hyperparameter optimization through Auto-sk l earn; deploying behavior analysis and anomaly detection module submodules to continuously monitor and analyze network behaviors and detect anomalies in a timely manner: using behavior analysis tools and anomaly detection algorithms to continuously monitor and analyze network behaviors; realizing real-time learning and adaptation mechanism submodules enables the system to update models and rules in real time and adapt to new threats: by setting a scheduled update mechanism and online learning algorithms, the system can receive new data and update models and rules in real time; enhancing model explanation and transparency submodules is used to improve the comprehensibility of the model and facilitate security personnel to make decisions: using model explanation technology, improving the interpretability of the model through feature importance analysis, and facilitating security personnel to make decisions;

[0057] The integrated automated machine learning submodule automatically optimizes the model, the deployed behavior analysis and anomaly detection module submodule continuously monitors network behavior, the real-time learning and adaptation mechanism submodule is updated in real time, and the strengthening model interpretability and transparency submodule improves model interpretability.

[0058] Working principle: First, the packet capture submodule captures network traffic data through the network interface card, and then passes the captured data to the data preprocessing submodule for cleaning, screening and format conversion to obtain the preprocessed data. The feature extraction module receives the preprocessed data. The traditional feature extraction submodule extracts common features. The deep learning feature automatic extraction submodule automatically learns high-level features. The enhanced feature extraction submodule strengthens and mines the features. The anomaly detection module obtains the data output by the feature extraction module. The statistical analysis submodule calculates statistical indicators and compares them with the baseline model. The deep learning anomaly detection submodule detects abnormal patterns through the deep learning model. The deep analysis module receives data. The deep packet inspection submodule analyzes the packet payload. The encrypted traffic analysis submodule analyzes the encrypted traffic. The intelligent sandbox module Suspicious traffic and files are isolated and analyzed. The threat intelligence integration submodule of the comprehensive analysis and learning module obtains and integrates threat intelligence. The machine learning submodule performs model training and learning. The dynamic rule generation submodule formulates new rules based on the results. The trapping submodule sets traps and collects attacker data. The source tracing analysis submodule traces and analyzes threats. The threat detection submodule detects threats in real time. The response decision submodule formulates strategies. The execution submodule performs response operations. The result display and alarm submodule displays results and issues alarms. Finally, the integrated automated machine learning submodule automatically optimizes the model. The deployment behavior analysis and anomaly detection module submodule continuously monitors network behavior, realizes real-time learning and the adaptation mechanism submodule is updated in real time. The model explanation and transparency enhancement submodule improves model explainability.

[0059] The system can dynamically generate new detection rules based on real-time analysis and machine learning results to respond to ever-changing network threats in a timely manner. At the same time, it can be associated and integrated with external threat intelligence data, enriching the information sources for analysis and improving the ability to discover new and potential threats. The introduction of intelligent sandbox technology to isolate and analyze suspicious traffic and files increases the depth and comprehensiveness of detection. By setting traps and tracing the source of threats, it provides a more in-depth analysis method for preventing and responding to threats.

[0060] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A network traffic analysis system based on machine learning, including a network traffic analysis module, characterized in that: The network traffic analysis module includes a data collection and preprocessing module, a feature extraction module, an anomaly detection module, a deep analysis module, a comprehensive analysis and learning module, a trapping and tracing module, a real-time response and display module, and a system optimization and improvement module; The data collection and preprocessing module is responsible for collecting raw traffic data from the network and performing necessary preprocessing operations; The feature extraction module is responsible for analyzing network traffic data, extracting key feature information, and using these features for subsequent anomaly detection and in-depth analysis; The anomaly detection module is responsible for analyzing the extracted features using machine learning algorithms to identify abnormal behavior patterns; The deep analysis module is responsible for performing deep analysis on the traffic marked as abnormal to determine whether it is a real network threat; The comprehensive analysis and learning module is responsible for comprehensively analyzing the results of each module to improve the accuracy and reliability of the system; The trapping and tracing module is responsible for trapping and tracing the detected network threats; The real-time response and display module is responsible for displaying the system's detection results to the user in real time, and providing corresponding measures to block malicious connections and send alarm notifications; The system optimization and improvement module is responsible for optimizing and improving the performance of the entire system.

2. The network traffic analysis system based on machine learning according to claim 1 is characterized in that: The data acquisition and preprocessing module includes a data packet capture submodule and a data preprocessing submodule. The data packet capture submodule collects traffic data from various nodes and devices in the network, including data packets and traffic logs. The data preprocessing submodule cleans, filters and converts the format of the collected data, removes invalid data, and provides clean and unified format data for subsequent analysis.

3. The network traffic analysis system based on machine learning according to claim 1 is characterized in that: The feature extraction module includes a traditional feature extraction submodule, a deep learning feature automatic extraction submodule and an enhanced feature extraction submodule. The traditional feature extraction submodule extracts common source address and destination address network traffic features, the deep learning feature automatic extraction submodule uses a deep learning model to automatically learn and extract features, and the enhanced feature extraction submodule further mines and strengthens valuable features.

4. The network traffic analysis system based on machine learning according to claim 1 is characterized in that: The anomaly detection module includes a statistical analysis submodule and a deep learning anomaly detection submodule. The statistical analysis submodule calculates the statistical indicators of the traffic based on the established normal network behavior baseline model, compares the current traffic, and discovers abnormal behavior patterns. The deep learning anomaly detection submodule uses deep learning technology to detect complex abnormal patterns.

5. The network traffic analysis system based on machine learning according to claim 1 is characterized in that: The deep analysis module includes a deep packet detection submodule, an encrypted traffic analysis submodule and an intelligent sandbox module. The deep packet detection submodule is responsible for deeply analyzing the content of the data packet, including the payload part, to detect whether there is hidden malicious code and disguised information. The encrypted traffic analysis submodule is responsible for using specific algorithms and technologies to analyze the encrypted traffic and identify potential signs of threats. The intelligent sandbox module is responsible for running suspicious traffic or files in an isolated environment, observing their behavior, and determining whether there is a threat.

6. The network traffic analysis system based on machine learning according to claim 1, characterized in that: The comprehensive analysis and learning module includes a threat intelligence integration submodule, a machine learning submodule and a dynamic rule generation submodule. The threat intelligence integration submodule obtains external threat intelligence data and performs correlation analysis with local traffic data. The machine learning submodule uses a deep learning algorithm and a convolutional neural network (CNN) to train and learn traffic data to establish a prediction and classification model. The dynamic rule generation submodule dynamically generates new detection rules based on the results of machine learning and real-time analysis.

7. The network traffic analysis system based on machine learning according to claim 1, characterized in that: The trapping and tracing module includes a trapping submodule and a tracing analysis submodule. The trapping submodule is responsible for attracting attackers and collecting their behavior data to provide the system with more threat features and clues. The tracing analysis submodule is used to trace the source, propagation path and impact range of the threat after it is detected.

8. The network traffic analysis system based on machine learning according to claim 1, characterized in that: The real-time response and display module includes a real-time response submodule and a result display and alarm submodule. The real-time response submodule is responsible for displaying the system's detection results to the user in real time, and the result display and alarm submodule is used to present the analysis results to security personnel in a visual manner and issue an alarm in a timely manner when a serious threat is found.

9. The network traffic analysis system based on machine learning according to claim 8, characterized in that: The real-time response submodule includes a threat detection submodule, a response decision submodule and an execution submodule. The threat detection submodule is responsible for real-time monitoring of threats in traffic, the response decision submodule is used to formulate a response strategy based on the detection results, and the execution submodule executes response operations, intercepts and alerts them.

10. The network traffic analysis system based on machine learning according to claim 1, characterized in that: The system optimization and improvement module includes an integrated automated machine learning submodule, a deployed behavior analysis and anomaly detection module submodule, a real-time learning and adaptation mechanism submodule, and an enhanced model interpretability and transparency submodule. The integrated automated machine learning submodule is used to realize automated feature selection, model selection and hyperparameter optimization processes. The deployed behavior analysis and anomaly detection module submodule is used to continuously monitor and analyze network behavior and detect anomalies in a timely manner. The real-time learning and adaptation mechanism submodule enables the system to update models and rules in real time and adapt to new threats. The enhanced model interpretability and transparency submodule is used to improve the comprehensibility of the model and facilitate security personnel to make decisions.

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