Intelligent network flow anomaly detection and automatic isolation system

Abstract

The invention discloses an intelligent network traffic anomaly detection and automatic isolation system, which comprises a data acquisition module, a preprocessing module, an intelligent traffic analysis module, an anomaly detection engine, an automatic isolation execution module and a visual monitoring and alarm platform, by introducing a deep learning algorithm and a multi-dimensional anomaly detection algorithm, deep mining and comprehensive analysis of network traffic can be realized, feature information is automatically learned and extracted from mass data, an accurate traffic behavior model is constructed, and abnormal behaviors in the traffic are detected in real time. And full-process automatic processing from data acquisition, preprocessing, intelligent analysis to automatic isolation is realized. When abnormal traffic is detected, the system can automatically execute isolation measures and monitor the isolation purpose in real time, and compared with a traditional method based on a rule base or threshold judgment, the method is higher in detection accuracy and real-time performance.

Description

Technical Field

[0001] The present invention relates to the technical field of network security, and in particular to an intelligent network traffic anomaly detection and automatic isolation system. Background Art

[0002] With the rapid development of information technology, networks have become an indispensable part of modern society's infrastructure. However, the complexity and openness of the network environment also present numerous security risks, such as cyberattacks, data leaks, and malware transmission. To ensure the secure and stable operation of networks, it is crucial to promptly detect and effectively address abnormal behavior in network traffic.

[0003] Traditional network traffic monitoring methods rely heavily on pre-set rule bases or threshold judgments, which are insufficient in the face of ever-changing network environments and attack vectors. On the one hand, rule base updates lag behind the development of attack vectors, making it difficult to effectively address new attacks. On the other hand, threshold judgment methods are susceptible to fluctuations in network traffic, leading to false positives or missed detections. Therefore, a more intelligent and flexible network traffic anomaly detection system is needed to address these challenges.

[0004] In recent years, the rise of artificial intelligence (AI), particularly the rapid development of deep learning, has provided new insights and approaches for network traffic anomaly detection. Deep learning algorithms can automatically learn and extract features from massive amounts of data, enabling effective analysis and processing of complex data. Therefore, applying deep learning to network traffic anomaly detection is expected to improve detection accuracy and real-time performance.

[0005] To this end, an intelligent network traffic anomaly detection and automatic isolation system is proposed, which aims to achieve real-time monitoring, intelligent analysis and automatic response of network traffic through advanced technical means, thereby improving the security protection capabilities of the network. Summary of the Invention

[0006] The purpose of the present invention is to provide an intelligent network traffic anomaly detection and automatic isolation system to achieve real-time monitoring, intelligent analysis and automatic response to network traffic, thereby improving the security protection capability of the network.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] An intelligent network traffic anomaly detection and automatic isolation system includes a data acquisition module, a preprocessing module, an intelligent traffic analysis module, an anomaly detection engine, an automatic isolation execution module and a visual monitoring and alarm platform. The data acquisition module, the preprocessing module, the intelligent traffic analysis module, the anomaly detection engine, the automatic isolation execution module and the visual monitoring and alarm platform work together to achieve real-time monitoring, intelligent analysis and automatic response to network traffic.

[0009] As a further solution of the present invention, the data acquisition module is responsible for capturing the original traffic data from the network environment. The data acquisition module can adopt network packet capture technology to capture data packets passing through the network in real time by at least monitoring the network interface or mirror port. At the same time, the data acquisition module supports data acquisition of multiple protocols, including at least TCP / IP, HTTP, FTP common protocols and custom protocols. The data acquisition module sends the collected data to the preprocessing module for further processing.

[0010] As a further solution of the present invention, the preprocessing module cleans, decodes and formats the collected raw traffic data. The preprocessing module first deduplicates and denoises the data to improve data quality; then, the cleaned data is decoded into a recognizable format and converted into a data structure suitable for subsequent analysis; finally, the preprocessed data is sent to the intelligent traffic analysis module for analysis.

[0011] As a further solution of the present invention, the intelligent traffic analysis module can use deep learning algorithms to deeply mine and analyze the preprocessed traffic data. The intelligent traffic analysis module first constructs a traffic feature set, including features based on statistical characteristics and content-based features; then, a suitable deep learning model is used to train and learn the feature set to construct a traffic behavior model. The traffic behavior model can capture the normal behavior patterns of network traffic and be used for subsequent anomaly detection.

[0012] During the real-time detection phase, the intelligent traffic analysis module compares the received real-time traffic data with the constructed traffic behavior model, and evaluates the degree of traffic abnormality by calculating the difference or similarity index between the real-time traffic and the model. When the difference exceeds the preset threshold, the traffic is considered to have abnormal behavior and is marked as suspected abnormal traffic.

[0013] As a further solution of the present invention, the anomaly detection engine further screens and determines suspected abnormal traffic based on the output results of the intelligent traffic analysis module. The anomaly detection engine adopts a multi-dimensional anomaly detection algorithm to conduct in-depth analysis of traffic data from multiple angles. By calculating the statistical characteristics, distribution characteristics, and correlation characteristic indicators of the traffic data, the abnormal degree of the traffic is comprehensively evaluated. When the traffic data exhibits abnormal characteristics in multiple dimensions, it is considered that the traffic does have abnormal behavior and is determined to be abnormal traffic.

[0014] As a further solution of the present invention, the automatic isolation execution module immediately starts the isolation mechanism after detecting abnormal traffic. The automatic isolation execution module first analyzes the source, target, and type information of the abnormal traffic to determine the scope and method of isolation; then, it automatically configures the network equipment according to the isolation strategy to isolate the abnormal traffic to a safe area or completely block it; at the same time, the automatic isolation execution module also supports real-time monitoring and evaluation of the isolation effect to ensure the effectiveness of the isolation measures.

[0015] As a further solution of the present invention, a visual monitoring and alarm platform is used to provide an intuitive network traffic monitoring interface and alarm function. The visual monitoring and alarm platform can display the status of network traffic, anomaly detection results and isolation execution status in real time, and display statistical information and analysis results of traffic data in the form of charts and dashboards to help administrators quickly understand the overall status of the network; at the same time, the visual monitoring and alarm platform also supports custom alarm rule settings. When serious anomalies or specific events are detected, alarm information can be sent to administrators through multiple methods such as email, SMS, and pop-up windows to ensure that administrators can respond and handle them in a timely manner.

[0016] As a further solution of the present invention, the deep learning model is constructed by the following method:

[0017] (1) Selecting Convolutional Neural Network (CNN): The local connection and weight sharing characteristics of CNN enable it to effectively extract feature information from images. In network traffic analysis, traffic data can be regarded as a special "image" and CNN can be used for feature extraction and classification. By constructing a multi-layer convolutional layer, pooling layer and fully connected layer structure, deep mining and analysis of traffic data can be achieved;

[0018] (2) Selecting Long Short-Term Memory (LSTM) networks: In network traffic analysis, LSTM can be used to capture the temporal characteristics of traffic data and predict future traffic trends. By combining CNN and LSTM models, comprehensive analysis and prediction of traffic data can be achieved.

[0019] (3) Select an unsupervised learning algorithm: Use an unsupervised learning algorithm to build a traffic behavior model, use a clustering algorithm to perform cluster analysis on the traffic data, and classify similar traffic data into the same category; then, evaluate the degree of traffic abnormality by comparing the difference between real-time traffic and the cluster center.

[0020] As a further solution of the present invention, the specific method for selecting anomaly detection algorithm is as follows:

[0021] (1) Select an anomaly detection algorithm based on statistics: including at least the Z-score algorithm and the chi-square test. These algorithms evaluate the distribution of data by calculating the statistics of traffic data and judge the degree of anomaly based on the distribution characteristics. However, these algorithms have strict assumptions about the distribution of data and are easily affected by extreme values.

[0022] (2) Select a density-based anomaly detection algorithm: at least the local outlier factor (LOF) algorithm and DBSCAN. These algorithms evaluate the degree of data anomaly by calculating the density or distance around the data point. In areas with low density, data points are considered to be abnormal. This method can handle data distributions of arbitrary shapes and is robust to noise and outliers in the data.

[0023] (3) Select a tree-based anomaly detection algorithm: at least the Isolation Forest algorithm, which isolates data points by constructing multiple isolation trees and judges the degree of anomaly based on the ease with which the data points are isolated. The earlier the data points are isolated, the more abnormal they are considered. This method is computationally efficient and easy to implement, and is suitable for anomaly detection in large-scale data sets.

[0024] As a further solution of the present invention, the isolation strategy is formulated as follows:

[0025] In the automatic isolation execution module, it is necessary to formulate appropriate isolation strategies to deal with different types of abnormal traffic. The following are specific isolation strategies:

[0026] (1) Source address-based isolation: Isolate traffic from specific source addresses. This method is suitable for known attack sources or suspicious IP address ranges. By configuring ACL or firewall rules on network devices, traffic from these addresses can be redirected to an isolated area or directly discarded.

[0027] (2) Destination address-based isolation: Isolate traffic destined for specific destination addresses. This method is suitable for protecting critical services or sensitive data from attacks. By configuring routing policies or security group rules on network devices, traffic destined for these addresses can be redirected to isolated areas or subjected to deep inspection.

[0028] (3) Isolation based on protocol type: Isolate traffic of specific protocol types and all unauthorized SSH and RDP remote access protocol traffic. This method can effectively prevent unauthorized remote login and data leakage risks;

[0029] (4) Dynamic isolation strategy: Dynamically adjust the isolation strategy based on the results of real-time traffic analysis. When a large-scale DDoS attack is detected, more stringent isolation measures can be automatically enabled to prevent the attack from spreading. At the same time, the intensity and scope of the isolation strategy can also be dynamically adjusted based on the attack type and attack intensity factors.

[0030] Compared with the existing technology, the intelligent network traffic anomaly detection and automatic isolation system of the present invention has the following beneficial effects:

[0031] The modules in the present invention work together to achieve real-time monitoring, intelligent analysis and automatic response to network traffic. By introducing deep learning algorithms and multi-dimensional anomaly detection algorithms, it is possible to achieve in-depth mining and comprehensive analysis of network traffic. These algorithms can automatically learn and extract feature information from massive data, build accurate traffic behavior models, and detect abnormal behavior in traffic in real time, realizing full-process automation from data collection, preprocessing, intelligent analysis to automatic isolation. When abnormal traffic is detected, the system can automatically execute isolation measures and monitor the isolation purpose in real time. Compared with traditional methods based on rule bases or threshold judgments, the detection accuracy and real-time performance of the present invention are higher. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only examples of the embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0033] Figure 1 A diagram showing the connection relationship between modules in an embodiment of the present invention;

[0034] Figure 2 Flowchart of an embodiment of the present invention. DETAILED DESCRIPTION

[0035] In order to make the purpose, technical solutions and advantages of the present invention more clear, the following is a further detailed description of the present invention in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0036] In the description of the embodiments of the present invention, it should be understood that the terms "upper", "lower", "front", "back", "inside", "outside", etc., indicating directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the embodiments of the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore should not be understood as limiting the embodiments of the present invention.

[0037] In the description of the embodiments of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installation", "connection" and "connection" should be understood in a broad sense. For example, it can be a fixed connection, an integral connection, or a detachable connection; it can be the internal connection of two components; it can be a direct connection or an indirect connection through an intermediate medium. For ordinary technicians in this field, the specific meanings of the above terms in the embodiments of the present invention can be understood according to specific circumstances.

[0038] like Figure 1-Figure 2 As shown, an embodiment of the present invention provides an intelligent network traffic anomaly detection and automatic isolation system, including: a data acquisition module, a preprocessing module, an intelligent traffic analysis module, an anomaly detection engine, an automatic isolation execution module, and a visual monitoring and alarm platform. The data acquisition module, the preprocessing module, the intelligent traffic analysis module, the anomaly detection engine, the automatic isolation execution module, and the visual monitoring and alarm platform work together to realize real-time monitoring, intelligent analysis, and automatic response to network traffic.

[0039] In an embodiment of the present invention, the data acquisition module is responsible for capturing original traffic data from the network environment. The data acquisition module can adopt network packet capture technology to capture data packets passing through the network in real time by at least monitoring the network interface or mirroring the port. At the same time, the data acquisition module supports data acquisition of multiple protocols, including at least TCP / IP, HTTP, FTP common protocols and custom protocols. The data acquisition module sends the collected data to the preprocessing module for further processing.

[0040] The preprocessing module cleans, decodes, and formats the collected raw traffic data. Because raw traffic data often contains a large amount of redundant information and noise, direct analysis can affect the accuracy of detection results. Therefore, the preprocessing module first performs operations such as deduplication and denoising on the data to improve data quality. The cleaned data is then decoded into a recognizable format (such as protocol fields and timestamps) and converted into data structures suitable for subsequent analysis (such as flow records and session logs). Finally, the preprocessed data is sent to the intelligent traffic analysis module for analysis.

[0041] The intelligent traffic analysis module is the core component of this invention. It uses deep learning algorithms to conduct in-depth mining and analysis of preprocessed traffic data. This module first constructs a traffic feature set, including statistical features (such as packet size and arrival interval) and content-based features (such as protocol fields and payload content). It then uses a suitable deep learning model (such as a convolutional neural network (CNN) or a long short-term memory (LSTM) network) to train and learn the feature set to construct a traffic behavior model. This model can capture normal behavior patterns of network traffic and be used for subsequent anomaly detection.

[0042] During the real-time detection phase, the intelligent traffic analysis module compares the received real-time traffic data with the established traffic behavior model. By calculating the difference or similarity between the real-time traffic and the model, the abnormality of the traffic is evaluated. When the difference exceeds the preset threshold, the traffic is considered to have abnormal behavior and marked as suspected abnormal traffic.

[0043] Based on the output of the intelligent traffic analysis module, the anomaly detection engine further screens and identifies suspected anomalous traffic. This engine employs multi-dimensional anomaly detection algorithms (such as density-based clustering and the isolation forest algorithm) to conduct in-depth analysis of traffic data from multiple perspectives. By calculating traffic data's statistical, distributional, and correlation characteristics, it comprehensively assesses the degree of traffic anomaly. When traffic data exhibits anomalous characteristics across multiple dimensions, the traffic is deemed anomalous and identified as such.

[0044] The automatic isolation execution module immediately initiates isolation upon detecting abnormal traffic. This module first analyzes the source, destination, and type of abnormal traffic to determine the scope and method of isolation. It then automatically configures network devices (such as firewalls and routers) based on the isolation policy, isolating the abnormal traffic to a safe zone or completely blocking it. The module also supports real-time monitoring and evaluation of isolation effectiveness to ensure the effectiveness of isolation measures.

[0045] The visual monitoring and alarm platform provides an intuitive network traffic monitoring interface and alarm functionality. It displays real-time network traffic status, anomaly detection results, and isolation enforcement. It displays traffic statistics and analysis results through charts and dashboards, helping administrators quickly understand the overall network status. The platform also supports customizable alarm rules. When serious anomalies or specific events are detected, administrators can receive alerts via email, SMS, pop-up notifications, and other methods, ensuring timely response and resolution.

[0046] In an embodiment of the present invention, the deep learning model is constructed by the following method:

[0047] Building a deep learning model is one of the core tasks of the intelligent traffic analysis module. To achieve efficient traffic analysis, it is necessary to select appropriate deep learning models and algorithms. The following are some possible implementation methods:

[0048] (1) Convolutional Neural Network (CNN): CNN performs well in processing image data. Its local connection and weight sharing characteristics enable it to effectively extract feature information from images. In network traffic analysis, traffic data can be regarded as a special "image" (such as time series data, traffic packet sequence, etc.), and CNN is used for feature extraction and classification. By constructing a multi-layer convolutional layer, pooling layer, and fully connected layer structure, in-depth mining and analysis of traffic data can be achieved.

[0049] (2) Long Short-Term Memory (LSTM): LSTM is a special type of recurrent neural network (RNN) that introduces mechanisms such as forget gates, input gates, and output gates to address the gradient vanishing or gradient exploding problems that traditional RNNs face when processing long sequences of data. In network traffic analysis, LSTM can be used to capture the temporal characteristics of traffic data and predict future traffic trends. By combining models such as CNN and LSTM, comprehensive analysis and prediction of traffic data can be achieved.

[0050] (3) Unsupervised learning algorithms: In addition to supervised learning algorithms, unsupervised learning algorithms can also be considered to build traffic behavior models. For example, clustering algorithms (such as K-means, DBSCAN, etc.) can be used to perform cluster analysis on traffic data, classifying similar traffic data into the same category. Then, the degree of traffic anomaly is evaluated by comparing the difference between real-time traffic and cluster centers. This method does not require pre-labeling of data samples and can automatically discover abnormal patterns in the data.

[0051] In the embodiment of the present invention, the specific method for selecting the anomaly detection algorithm is as follows:

[0052] In the anomaly detection engine, it is necessary to select an appropriate anomaly detection algorithm to further filter and determine suspected abnormal traffic. The following are some commonly used anomaly detection algorithms and their characteristics:

[0053] (1) Statistical anomaly detection algorithms: such as the Z-score algorithm and the chi-square test. These algorithms evaluate the distribution of traffic data by calculating statistics (such as mean and variance) and determine the degree of data anomaly based on the distribution characteristics. However, these algorithms have strict assumptions about the distribution of data and are easily affected by extreme values.

[0054] (2) Density-based anomaly detection algorithms: such as the local outlier factor (LOF) algorithm and DBSCAN. These algorithms evaluate the degree of anomaly in the data by calculating the density or distance around the data point. Data points in areas with low density are considered to be anomalies. This method can handle data distributions of arbitrary shapes and is robust to noise and outliers in the data.

[0055] (3) Tree-based anomaly detection algorithms: such as the Isolation Forest algorithm. This algorithm isolates data points by constructing multiple isolation trees and determines the degree of anomaly based on how easily the data points are isolated. The earlier a data point is isolated, the more abnormal it is considered. This method is computationally efficient and easy to implement, making it suitable for anomaly detection in large datasets.

[0056] It is worth noting that the above three algorithms need to be selected and used according to the system tasks and specific usage scenarios during implementation.

[0057] In the embodiment of the present invention, the isolation strategy is formulated as follows:

[0058] In the automatic isolation execution module, it is necessary to formulate appropriate isolation strategies to deal with different types of abnormal traffic. The following are some possible isolation strategies:

[0059] (1) Source address-based isolation: Isolate traffic from specific source addresses. This method is suitable for known attack sources or suspicious IP address ranges. By configuring ACLs (access control lists) or firewall rules on network devices, traffic from these addresses can be redirected to an isolated area or directly discarded.

[0060] (2) Destination-based isolation: Isolate traffic destined for specific destination addresses. This method is suitable for protecting critical services or sensitive data from attacks. By configuring routing policies or security group rules on network devices, traffic destined for these addresses can be redirected to an isolated area or subjected to deep inspection.

[0061] (3) Isolation based on protocol type: Isolate traffic of specific protocol types. For example, isolate all unauthorized remote access protocol traffic such as SSH and RDP. This method can effectively prevent risks such as unauthorized remote login and data leakage.

[0062] (4) Dynamic Isolation Strategy: Dynamically adjust the isolation strategy based on real-time traffic analysis results. For example, when a large-scale DDoS attack is detected, more stringent isolation measures (such as black hole routing and traffic scrubbing) can be automatically enabled to prevent the attack from spreading. At the same time, the intensity and scope of the isolation strategy can also be dynamically adjusted based on factors such as the attack type and attack intensity.

[0063] The workflow of an intelligent network traffic anomaly detection and automatic isolation system according to an embodiment of the present invention can be summarized into the following main steps:

[0064] S1: Data collection:

[0065] The data acquisition module captures the original traffic data in the network environment through network packet capture technology and supports multiple protocols, including TCP / IP, HTTP, FTP and other common protocols as well as custom protocols.

[0066] S2: Preprocessing:

[0067] The preprocessing module cleans, decodes and formats the collected raw traffic data, removes redundant information and noise, decodes it into a recognizable format, and converts it into a data structure suitable for subsequent analysis.

[0068] S3: Intelligent traffic analysis:

[0069] The intelligent traffic analysis module uses deep learning algorithms (such as CNN, LSTM, etc.) to deeply mine and analyze pre-processed traffic data, build a traffic behavior model, and evaluate the degree of abnormality of real-time traffic.

[0070] S4: Anomaly Detection

[0071] Based on the output results of the intelligent traffic analysis module, the anomaly detection engine uses multi-dimensional anomaly detection algorithms (such as density-based clustering algorithm, isolation forest algorithm, etc.) to screen and judge suspected abnormal traffic and determine abnormal traffic.

[0072] S5: Automatic isolation:

[0073] After detecting abnormal traffic, the automatic isolation execution module automatically configures network devices (such as firewalls, routers, etc.) according to the isolation strategy, isolates the abnormal traffic to a safe area or completely blocks it, and monitors the isolation effect in real time.

[0074] S6: Visual monitoring and alarm:

[0075] The visual monitoring and alarm platform provides a network traffic monitoring interface and alarm function, which displays the network traffic status, anomaly detection results and isolation execution status in real time. It supports the setting of custom alarm rules to ensure that administrators can respond and handle the situation in a timely manner.

[0076] The embodiments of the present invention have the following beneficial effects:

[0077] Real-time monitoring and intelligent analysis:

[0078] The system can capture network traffic data in real time, perform intelligent analysis through deep learning algorithms, quickly identify abnormal behavior in network traffic, and improve the real-time and accuracy of network security protection.

[0079] Efficient and accurate anomaly detection:

[0080] Adopting multi-dimensional anomaly detection algorithms, we conduct in-depth analysis of traffic data from multiple angles to ensure the comprehensiveness and accuracy of anomaly detection and reduce false positives and missed reports.

[0081] Automated Isolation Response:

[0082] The automatic isolation execution module can automatically configure network devices based on the detection results, quickly isolate abnormal traffic, reduce manual intervention, and improve emergency response speed.

[0083] Visual monitoring and flexible alarm:

[0084] The visual monitoring and alarm platform provides an intuitive network traffic monitoring interface and flexible alarm settings to help administrators quickly understand network status and respond to and handle security issues in a timely manner.

[0085] Flexible and scalable architecture:

[0086] The system adopts a modular design, with modules working together to support multiple protocols and algorithms. It is easy to expand and upgrade to meet the needs of network environments of different scales and complexities.

[0087] Improving network security protection capabilities:

[0088] Through real-time monitoring, intelligent analysis and automatic isolation, the system can effectively prevent security risks such as network attacks and data leaks, and enhance the overall security protection capabilities of the network.

[0089] The above shows and describes the basic principles of the present invention. The above is only a preferred embodiment of the present invention and is not intended to limit the present invention. The above embodiments and descriptions in the specification are only to illustrate the principles of the present invention. Without departing from the scope of the present invention, any modifications, equivalent substitutions and improvements made within the spirit and scope of the present invention should be included in the scope of protection of the present invention.

Claims

1. An intelligent network traffic anomaly detection and automatic isolation system, characterized in that include: The data acquisition module, preprocessing module, intelligent traffic analysis module, anomaly detection engine, automatic isolation execution module and visual monitoring and alarm platform work together to realize real-time monitoring, intelligent analysis and automatic response to network traffic.

2. According to claim 1, the intelligent network traffic anomaly detection and automatic isolation system is characterized in that: The data acquisition module is responsible for capturing the original traffic data from the network environment. The data acquisition module can adopt network packet capture technology, at least by monitoring the network interface or mirroring the port, to capture the data packets passing through the network in real time. At the same time, the data acquisition module supports data acquisition of multiple protocols, including at least TCP / IP, HTTP, FTP common protocols and custom protocols. The data acquisition module sends the collected data to the preprocessing module for further processing.

3. The intelligent network traffic anomaly detection and automatic isolation system according to claim 2 is characterized in that: The preprocessing module cleans, decodes and formats the collected raw traffic data. The preprocessing module first deduplicates and removes noise from the data to improve data quality; then, the cleaned data is decoded into a recognizable format and converted into a data structure suitable for subsequent analysis; finally, the preprocessed data is sent to the intelligent traffic analysis module for analysis.

4. The intelligent network traffic anomaly detection and automatic isolation system according to claim 3 is characterized in that: The intelligent traffic analysis module can use a deep learning algorithm to perform in-depth mining and analysis on the pre-processed traffic data. The intelligent traffic analysis module first constructs a traffic feature set, including features based on statistical characteristics and features based on content; Then, a suitable deep learning model is used to train and learn the feature set to build a traffic behavior model, which can capture the normal behavior pattern of network traffic and be used for subsequent anomaly detection; During the real-time detection stage, the intelligent traffic analysis module compares the received real-time traffic data with the constructed traffic behavior model, and evaluates the degree of traffic abnormality by calculating the difference or similarity index between the real-time traffic and the model. When the difference exceeds the preset threshold, the traffic is considered to have abnormal behavior and is marked as suspected abnormal traffic.

5. The intelligent network traffic anomaly detection and automatic isolation system according to claim 4 is characterized in that: Based on the output results of the intelligent traffic analysis module, the anomaly detection engine further screens and determines suspected abnormal traffic. The anomaly detection engine adopts a multi-dimensional anomaly detection algorithm to conduct in-depth analysis of traffic data from multiple angles. By calculating the statistical characteristics, distribution characteristics, and correlation characteristic indicators of the traffic data, the abnormal degree of the traffic is comprehensively evaluated. When the traffic data shows abnormal characteristics in multiple dimensions, it is considered that the traffic does have abnormal behavior and is determined to be abnormal traffic.

6. The intelligent network traffic anomaly detection and automatic isolation system according to claim 5, characterized in that: After detecting abnormal traffic, the automatic isolation execution module immediately starts the isolation mechanism. The automatic isolation execution module first analyzes the source, target, and type information of the abnormal traffic to determine the scope and method of isolation; then, it automatically configures network equipment according to the isolation strategy to isolate the abnormal traffic to a safe area or completely block it; at the same time, the automatic isolation execution module also supports real-time monitoring and evaluation of the isolation effect to ensure the effectiveness of the isolation measures.

7. The intelligent network traffic anomaly detection and automatic isolation system according to claim 6, characterized in that: The visual monitoring and alarm platform is used to provide an intuitive network traffic monitoring interface and alarm function. The visual monitoring and alarm platform can display the status of network traffic, anomaly detection results and isolation execution status in real time, and display the statistical information and analysis results of traffic data in the form of charts and dashboards to help administrators quickly understand the overall status of the network; at the same time, the visual monitoring and alarm platform also supports custom alarm rule settings. When serious anomalies or specific events are detected, alarm information can be sent to administrators through multiple methods such as emails, text messages, and pop-up windows to ensure that administrators can respond and handle them in a timely manner.

8. The intelligent network traffic anomaly detection and automatic isolation system according to claim 7, characterized in that: The deep learning model is constructed by the following method: (1) Selecting a convolutional neural network (CNN): The local connection and weight sharing characteristics of the CNN enable it to effectively extract feature information from images. In network traffic analysis, traffic data can be regarded as a special "image" and CNN can be used for feature extraction and classification. By constructing a multi-layer convolutional layer, pooling layer, and fully connected layer structure, deep mining and analysis of traffic data can be achieved. (2) Selecting Long Short-Term Memory (LSTM) Network: In network traffic analysis, LSTM can be used to capture the temporal characteristics of traffic data and predict future traffic trends. By combining CNN and LSTM models, comprehensive analysis and prediction of traffic data can be achieved. (3) Select an unsupervised learning algorithm: Use an unsupervised learning algorithm to build a traffic behavior model, use a clustering algorithm to perform cluster analysis on traffic data, and classify similar traffic data into the same category; then, evaluate the degree of traffic abnormality by comparing the difference between real-time traffic and cluster centers.

9. The intelligent network traffic anomaly detection and automatic isolation system according to claim 8, characterized in that: The specific selection method of the anomaly detection algorithm is as follows: (1) Select an anomaly detection algorithm based on statistics: at least including the Z-score algorithm and the chi-square test. The above algorithms evaluate the distribution of data by calculating the statistics of traffic data and judge the degree of data anomaly based on the distribution characteristics. However, the above algorithms have strict assumptions about the distribution of data and are easily affected by extreme values. (2) Select a density-based anomaly detection algorithm: at least the local outlier factor (LOF) algorithm and DBSCAN. The above algorithms evaluate the degree of data anomaly by calculating the density or distance around the data point. In the area with low density, the data point is considered to be abnormal. This method can handle data distribution of any shape and has a certain degree of robustness to noise and outliers in the data. (3) Select a tree-based anomaly detection algorithm: at least the Isolation Forest algorithm, which isolates data points by constructing multiple isolation trees and determines the degree of abnormality based on how easy it is to isolate the data points. The earlier a data point is isolated, the more abnormal it is considered. This method is computationally efficient and easy to implement, and is suitable for anomaly detection in large-scale data sets.

10. The intelligent network traffic anomaly detection and automatic isolation system according to claim 9, characterized in that: The isolation strategy is formulated as follows: In the automatic isolation execution module, it is necessary to formulate appropriate isolation strategies to deal with different types of abnormal traffic. The following are specific isolation strategies: (1) Isolation based on source address: Isolate traffic from specific source addresses. This method is applicable to known attack sources or suspicious IP address ranges. By configuring ACL or firewall rules on network devices, traffic from these addresses can be redirected to an isolated area or directly discarded. (2) Destination address-based isolation: Isolate traffic to specific destination addresses. This method is suitable for protecting critical services or sensitive data from attacks. By configuring routing policies or security group rules on network devices, traffic to these addresses can be redirected to isolated areas or subjected to deep inspection. (3) Isolation based on protocol type: Isolate traffic of specific protocol types and all unauthorized SSH and RDP remote access protocol traffic. This method can effectively prevent unauthorized remote login and data leakage risks; (4) Dynamic isolation strategy: The isolation strategy is dynamically adjusted based on the results of real-time traffic analysis. When a large-scale DDoS attack is detected, stricter isolation measures can be automatically enabled to prevent the attack from spreading. At the same time, the intensity and scope of the isolation strategy can also be dynamically adjusted based on the attack type and attack intensity factors.

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