Adaptive communication network anomaly detection system based on deep learning and reinforcement learning

Abstract

The invention provides an adaptive communication network anomaly detection and response system based on deep learning and reinforcement learning. According to the system, key features of network flow data are automatically extracted through an adaptive feature extraction module, and an anomaly detection model is constructed by using a deep learning technology so as to identify and position abnormal behaviors in a network. And in combination with a reinforcement learning algorithm, the system can dynamically adjust and optimize a response strategy, and rapid and effective response to various network abnormal behaviors is realized. In addition, the system adopts an end-to-end abnormal score learning method to improve the detection efficiency, and the communication overhead in the distributed system is reduced through a communication optimization technology. In the field of wireless communication, the system shows good stability, and can maintain high detection performance even in a non-ideal environment. And the transparency and the understandability of the detection process are enhanced by introducing model interpretability and a visualization tool.

Description

Technical field:

[0001] The present invention relates to security technologies in the field of communication networks, and in particular to an adaptive communication network anomaly detection and response system that integrates deep learning and reinforcement learning algorithms. Background technology:

[0002] In the current communication network field, the importance of ensuring network security is increasing. Especially with the continuous evolution of network attack methods, traditional signature- and rule-based anomaly detection methods have been unable to cope with increasingly complex network threats. These traditional methods usually rely on static feature libraries of known attack patterns, lack the ability to identify emerging attack patterns, and are inefficient when processing large-scale network data. With the explosive growth of network traffic, higher requirements are placed on anomaly detection technology, which must not only be able to process massive amounts of data, but also be able to quickly and accurately identify abnormal behavior.

[0003] In recent years, deep learning technology has attracted widespread attention due to its great potential in dealing with complex pattern recognition problems. Anomaly detection methods based on deep learning can automatically learn features from data, improving the accuracy and efficiency of detection. However, the training of deep learning models usually requires a large amount of labeled data, and the generalization ability and interpretability of the model still need to be improved. In addition, the dynamic nature of the network environment requires that the anomaly detection system can quickly adapt to new threats, while existing deep learning-based systems are still limited in real-time updates and adaptive adjustments.

[0004] As an important branch of machine learning, reinforcement learning has brought new insights to the field of anomaly detection due to its advantages in dealing with sequential decision-making problems. Through reinforcement learning, the system can learn the optimal response strategy in continuous interaction to adapt to the ever-changing network environment. However, how to effectively combine the perception ability of deep learning with the decision-making ability of reinforcement learning to build a system that can both accurately detect anomalies and respond intelligently is still a technical challenge.

[0005] In response to the above problems and challenges, the present invention proposes an adaptive communication network anomaly detection and response system based on deep learning and reinforcement learning. The system intelligently identifies key features in network traffic through an adaptive feature extraction module, detects abnormal patterns using a deep learning module, dynamically adjusts the response strategy through a reinforcement learning module, and achieves continuous optimization and learning of the system through an adaptive feedback loop module. This system is proposed to address the deficiencies of existing technologies in terms of real-time, accuracy, and adaptability of anomaly detection, and to provide a more comprehensive and efficient solution for communication network security.

[0006] Technical Solution

[0007] The purpose of the present invention is to propose an adaptive communication network anomaly detection and response system based on deep learning and reinforcement learning, which can intelligently identify key features in network traffic through an adaptive feature extraction module, detect abnormal patterns using a deep learning module, dynamically adjust the response strategy through a reinforcement learning module, and achieve continuous optimization and learning of the system through an adaptive feedback loop module.

[0008] The adaptive feature extraction module analyzes network traffic data through advanced algorithms, automatically identifies and extracts key features, such as traffic size, packet type, frequency distribution, etc. The key to this module is its adaptive ability, which can adjust the feature recognition process in real time according to the dynamic changes in network behavior. First, the system will perform data preprocessing, including cleaning invalid or abnormal data points, and unifying data from different sources and formats to ensure the accuracy and reliability of the data. Then, the system will use statistical methods and machine learning algorithms to extract key features, such as measuring the amount of data transmitted per unit time to identify abnormal fluctuations in traffic, analyzing the protocol type and destination address of the data packet to identify the distribution of different types of data packets, and counting the time interval between arrival of statistical packets to identify abnormal frequency patterns. In order to improve the accuracy and efficiency of feature extraction, the system will also dynamically adjust the weight of the features through adaptive algorithms, such as increasing the weight of traffic size during peak network traffic, while in normal periods, it may pay more attention to the type of data packets. In addition, the system will evaluate the importance of features through statistical learning methods, select the most representative features, and integrate different features through fusion technologies such as principal component analysis PCA or linear discriminant analysis LDA to improve the expressiveness of features. More importantly, the system monitors changes in network behavior in real time and dynamically updates feature sets to adapt to new network patterns, ensuring that the system can respond to changes in the network environment in a timely manner and maintain detection accuracy. The design of this adaptive feature extraction module not only improves the accuracy of anomaly detection, but also enhances the robustness and flexibility of the system, enabling it to cope with ever-changing network threats.

[0009] The deep learning anomaly detection module achieves in-depth learning and understanding of network traffic patterns by integrating autoencoders and generative adversarial networks (GANs). First, the autoencoder encodes the input network traffic data into a low-dimensional representation through an encoder, and then reconstructs this low-dimensional representation back to the original data through a decoder, thereby learning the intrinsic characteristics of the data. This process can be expressed in mathematical formulas as follows: Among them, x is the original data, Encoder and Decoder are encoder and decoder respectively, is the reconstructed data. The goal of the autoencoder is to minimize the reconstruction error, that is This helps capture the distribution characteristics of the data. Based on the autoencoder, the generative adversarial network further enhances the model's anomaly detection ability. GAN consists of two parts: the generator G and the discriminator D. The goal of the generator is to generate fake data that is as close to the real data as possible, while the goal of the discriminator is to distinguish between the real data and the fake data generated by the generator. This process can be expressed mathematically as: Among them, V(D,G) is the value function between the discriminator and the generator, x is the real data, z is the random vector sampled from the noise distribution, and G(z) is the data generated by the generator. Through this adversarial training, the generator continuously learns to generate more realistic data, while the discriminator continuously learns to more accurately identify true and false data. Through this method of integrating autoencoders and generative adversarial networks, the deep learning anomaly detection module can not only learn normal traffic patterns and reconstruct normal traffic characteristics, but also enhance the model's ability to identify abnormal traffic through adversarial training. This method, which combines unsupervised learning and semi-supervised learning, enables the model to more effectively identify and locate abnormal behaviors when processing large-scale network traffic data, thereby improving the system's anomaly detection performance and accuracy.

[0010] The reinforcement learning response strategy module includes three key units: strategy learning, effect evaluation, and strategy adjustment, forming a closed-loop feedback mechanism to continuously optimize the response strategy. In the strategy learning stage, the system uses historical response data to train a reinforcement learning model. The goal of this model is to learn what response measures to take in different network anomalies. The strategy function π maps the network state s to a response action a and implements iterative updates, where the update formula is π(s)←π(s)+α[Q(s,a)-π(s)], α is the learning rate, and Q(s,a) is the expected return value of the state-action pair. The effect evaluation unit is responsible for evaluating the effect of the current response strategy by calculating the heterogeneous return To achieve this, rk is the immediate return obtained at time step k, and γ is the discount factor. The policy adjustment unit dynamically adjusts the response strategy according to the results of the effect evaluation. The policy function π maximizes the expected return To update, the update formula is Where θ is the policy parameter, τ is the trajectory generated by following the policy π, and Q(st,at) is the reward value of the state-action pair. Through this dynamic adjustment mechanism, the reinforcement learning response strategy module can continuously learn and optimize response measures to adapt to the ever-changing network environment and abnormal behavior.

[0011] The end-to-end anomaly scoring learning module uses advanced scoring algorithms to learn anomaly scores directly from raw network traffic data, greatly simplifying the anomaly detection process and improving detection speed and accuracy. The module uses a deep neural network (DNN) to score data points for anomaly probability, and automatically extracts features and predicts anomalies by training an end-to-end model. The training goal of the model is to minimize the difference between the predicted probability and the actual label, which is usually measured by the cross-extraction loss function, which is defined as: Where N is the total number of data points, yi is the true label of data point i (usually 0 for normal and 1 for abnormal), and pi is the probability that the model predicts that data point i is abnormal. Through the back-propagation algorithm, the weights and biases of the model are updated according to the gradient of the loss function L to reduce the prediction error. In addition, in order to improve the generalization ability of the model, regularization terms such as L1 or L2 regularization are usually added during the training process to prevent overfitting. After the model is trained, it can New network traffic data is quickly scored to identify potential abnormal behaviors, thereby achieving efficient anomaly detection. This approach not only reduces the need for manual feature engineering, but also improves the automation and intelligence level of the system.

[0012] The communication optimization module improves the overall efficiency and response speed of the system through a series of efficient technical means. The module first adopts advanced data compression algorithms, such as lossless compression technologies such as LZ77 and LZ78, to reduce the bandwidth required for data transmission, and uses lossy compression algorithms such as JPEG or MP3 in appropriate scenarios to further reduce the amount of data. Secondly, by optimizing the communication protocol stack, simplifying the protocol layer and adjusting the parameters of protocols such as TCP / IP, such as window size and timeout retransmission mechanism, to adapt to different network conditions and application requirements. In addition, the module also uses network topology information and real-time status data to dynamically select the optimal transmission path, and ensures that the data transmission path is as short as possible through the shortest path algorithm and the minimum spanning tree algorithm to reduce transmission delay. At the same time, real-time monitoring and adaptive adjustment strategies are implemented to dynamically adjust the data compression algorithm, communication protocol parameters and transmission path selection strategy according to changes in network status to maintain efficient and stable communication. In terms of security, the module uses strong encryption algorithms to protect data security during data compression and transmission, and implements a strict authentication mechanism to ensure the legitimacy of the identities of both parties in data transmission. These comprehensive measures enable the communication optimization module to not only significantly reduce communication overhead, but also improve the system's response speed and stability, ensuring that efficient and reliable communication services can be provided in various network environments.

[0013] The unsupervised learning mechanism uses algorithms such as autoencoders to automatically discover abnormal patterns in data in the absence of labeled information. An autoencoder is an unsupervised learning neural network that compresses the input data into a low-dimensional representation through the encoder part, and then attempts to reconstruct the original data through the decoder part. In this process, the autoencoder learns the intrinsic characteristics and structure of the data, so that it can identify anomalies in the data. Specifically, the encoder part of the autoencoder uses a nonlinear transformation to map the input data x to a low-dimensional latent space z. This process can be expressed as z=fcnc(x), where fcnc is the encoding function. The decoder part maps the latent space z back to the data space and attempts to reconstruct the original data. The formula is Where fdec is the decoding function. The training goal of the autoencoder is to minimize the reconstruction error, that is This forces the model to learn an effective representation of the data. To enhance anomaly detection capabilities, the autoencoder can also use regularization techniques, such as sparse coding or denoising autoencoders, to increase the model's sensitivity to abnormal patterns. In addition, the system continuously improves the accuracy of anomaly scoring through an iterative optimization process, using the reconstruction error or anomaly score. to evaluate the degree of abnormality of a data point, where g is a nonlinear function that converts the reconstruction error into an anomaly score. This approach enables the system to improve detection capabilities under unknown threats and automatically adapt to new data patterns and abnormal behaviors.

[0014] The deep learning application module in the wireless communication can significantly enhance the robustness of the system by learning the time-varying and multipath effects of wireless signals. Specifically, the deep learning model uses structures such as convolutional neural networks (CNN) or recurrent neural networks (RNN) to learn the characteristics of the channel and the propagation mode of the signal from a large amount of historical data. By training these networks, the model can predict channel state information such as channel gain, delay, and Doppler shift, which is crucial for accurate signal detection. For example, convolutional neural networks can extract the time-frequency characteristics of the signal, while recurrent neural networks can capture the timing dependency of the signal. The training goal of the model is to minimize the difference between the predicted channel state and the actual channel state. The mean square error (MSE) is usually used as the loss function, and the formula is expressed as in is the channel response predicted by the model, and h is the actual channel response. In addition, the system uses advanced signal processing techniques, such as beamforming and multi-user detection, to improve the accuracy and efficiency of signal detection. Through these methods, the deep learning model can not only adapt to the complex changes of wireless signals, but also achieve effective signal separation and detection in a multi-user environment, thereby providing stable and reliable communication services in non-ideal environments.

[0015] The model interpretability and visualization module displays the characteristics, scores, and response strategies of abnormal traffic through an intuitive graphical interface, thereby enhancing the interpretability of the model and facilitating users to understand the decision-making process of the model. The module uses advanced visualization techniques such as bar charts, line charts, and scatter plots to intuitively display key indicators of network traffic and anomaly detection results, highlighting key features of abnormal traffic such as traffic peaks, abnormal packet types, and frequency patterns. At the same time, it also displays the anomaly score of each data point, uses a color coding or tagging system to distinguish between normal and abnormal traffic, and clearly displays the logic of the response strategy through a flowchart or decision tree. In addition, the module provides interactive exploration tools that allow users to observe changes in model predictions by adjusting parameters or selecting different features, and use explanatory techniques such as feature importance scores or local interpretable models (LIME) to explain the model's decisions. Integrated visualization tools such as Tableau or Power BI provide richer data visualization options, while allowing users to customize visualization views and choose the displayed data and chart types according to personal needs. Finally, the designed user feedback mechanism ensures that the visualization display can be continuously optimized based on user opinions and suggestions, improving user experience and model understandability.

[0016] The adaptive feedback loop module automatically collects key performance indicator data and user feedback by monitoring system performance in real time. The module first uses advanced monitoring tools to track the key performance indicators of the system in real time, such as detection accuracy, response time, resource consumption, etc., to ensure that changes in system performance can be discovered and responded to in a timely manner. Then, through the integrated user feedback mechanism, the user's experience and improvement suggestions are automatically collected. These feedbacks provide valuable first-hand information for system optimization. On this basis, the module uses data analysis and machine learning techniques to evaluate the overall performance of the system and identify performance bottlenecks and improvement points. According to the evaluation results, the module can automatically adjust system parameters, such as adjusting detection thresholds, optimizing algorithm parameters, or reconfiguring resource allocation to improve the performance and efficiency of the system. More importantly, this process is automated and closed-loop, which means that the system can continuously learn from new data and user feedback and gradually optimize its own performance. This adaptive adjustment mechanism not only improves the robustness and adaptability of the system, but also ensures that the system can continue to provide high-quality services in the face of changing network environments and user needs. In this way, the adaptive feedback loop module makes the entire system an evolving and improving intelligent body that can autonomously adapt to and respond to changes in the external environment, achieving true self-optimization and continuous learning.

[0017] The system is used in steps

[0018] The adaptive feature extraction module is used to extract the features of network traffic data, and the features are updated and optimized in real time:

[0019] The deep learning anomaly detection module is used to identify anomalies in network traffic, and the recognition ability of the model is enhanced by generating adversarial networks:

[0020] Use the reinforcement learning response strategy module to dynamically adjust response measures based on detection results:

[0021] The end-to-end anomaly scoring learning module is used to score network traffic anomalies and achieve rapid anomaly identification:

[0022] Use the communication optimization module to optimize the communication process in the distributed system:

[0023] Using unsupervised learning mechanisms to process unlabeled network traffic data:

[0024] Use deep learning application modules in wireless communications to perform channel estimation and signal detection in complex environments:

[0025] Use the model interpretability and visualization module to provide a visual display of the detection results:

[0026] The adaptive feedback loop module is used to conduct self-learning and optimization of the system based on the response effect and user feedback.

[0027] The above technical solution can bring the following three key technical effects:

[0028] 1. Significantly improve detection accuracy and response speed: Through the deep learning anomaly detection module, the system can use autoencoders and generative adversarial networks (GANs) to learn normal traffic patterns, and enhance the ability to identify abnormal traffic through adversarial training. This method of combining unsupervised learning and semi-supervised learning enables the model to more effectively identify and locate abnormal behavior when processing large-scale network traffic data. At the same time, the reinforcement learning response strategy module can dynamically adjust the response measures, and achieve fast and effective abnormal response through strategy learning, effect evaluation and strategy adjustment. This real-time adaptive adjustment mechanism not only improves the accuracy of detection, but also significantly shortens the system response time.

[0029] 2. Enhance the adaptability and stability of the system: The adaptive feature extraction module automatically identifies and extracts key features by analyzing network traffic data in real time, and optimizes the feature recognition process through adaptive algorithms to adapt to the dynamic changes in network behavior. This adaptive capability enables the system to respond to changes in the network environment in a timely manner and maintain detection accuracy and efficiency. In addition, the deep learning application module in wireless communications can adapt to the time-varying and multipath effects of wireless signals and enhance the robustness of the system in complex environments. Through these technologies, the system can not only perform well in ideal environments, but also maintain high detection performance in non-ideal environments.

[0030] 3. Improve the interpretability of the system and user experience: The model interpretability and visualization module displays the characteristics, scores, and response strategies of abnormal traffic through a graphical interface, which enhances the interpretability of the model. Users can quickly understand the detection results and decision-making process of the model through intuitive charts and graphs such as bar charts, line charts, and scatter plots. In addition, the module also provides interactive exploration tools that allow users to observe changes in model predictions by adjusting parameters or selecting different features, thereby gaining a deeper understanding of how the model works. This highly visual and interpretable display method not only increases user trust in the system, but also enhances the user experience, allowing even non-technical users to easily understand and utilize the system's detection results. Description of the drawings:

[0031] Figure 1 A schematic diagram of the system module of the present invention

[0032] Figure 2 The operating steps of the system of the present invention are shown in FIG. Specific implementation method:

[0033] 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.

[0034] Adaptive feature extraction module,The adaptive feature extraction module is the first link of the system, responsible for automatically identifying and extracting key features from the original network traffic data. These features are crucial for subsequent anomaly detection and can significantly improve the accuracy and efficiency of detection.

[0035] Traffic volume: Measures the amount of data transmitted per unit time and identifies abnormal fluctuations in traffic volume.

[0036] Packet type: Identify the distribution of different types of packets (such as TCP, UDP, ICMP) and detect abnormal types.

[0037] Frequency Distribution: Analyze the frequency pattern of packet arrival and identify abnormal frequency patterns.

[0038] Implementation steps:

[0039] 1. Data collection: The system collects network traffic data from network devices (such as routers and switches) in real time.

[0040] 2. Data cleaning: remove noise and outliers from the data to ensure the accuracy and reliability of the data.

[0041] 3. Feature extraction: Utilize statistical methods and machine learning algorithms to extract key features such as traffic size, packet type, and frequency distribution.

[0042] 4. Feature optimization: The feature weights are adjusted through adaptive algorithms to optimize the feature recognition process to adapt to the dynamic changes in network behavior.

[0043] 5. Feature output: The extracted feature data is passed to the deep learning anomaly detection module to provide input for subsequent anomaly detection.

[0044] Output feature data such as traffic size, packet type, frequency distribution, etc. to provide accurate input for the deep learning anomaly detection module.

[0045] Deep learning anomaly detection module,

[0046] The deep learning anomaly detection module uses advanced deep learning techniques such as autoencoders and generative adversarial networks (GANs) to learn normal traffic patterns and detect abnormal traffic. The module can automatically learn features from data to improve detection accuracy and efficiency.

[0047] Parameters and steps:

[0048] Autoencoder: Used to learn normal traffic patterns and reconstruct features.

[0049] Generative Adversarial Network (GAN): used to generate abnormal traffic samples and enhance the recognition ability of the model.

[0050] Implementation steps:

[0051] Feature input: Receives feature data provided by the adaptive feature extraction module.

[0052] Autoencoder training: Train the autoencoder to learn normal traffic patterns.

[0053] Generative adversarial network training: GAN is trained to generate abnormal traffic samples. Through adversarial training, the model's ability to identify abnormal traffic is enhanced and the value function between the discriminator and the generator is optimized.

[0054] Anomaly detection: Use the trained model to perform anomaly detection on network traffic and identify abnormal behavior.

[0055] Result output: The detection results are passed to the reinforcement learning response strategy module to provide a basis for dynamic response.

[0056] Output anomaly detection results, including normal and abnormal classification, to provide decision support for subsequent response strategies.

[0057] Reinforcement learning response strategy module,

[0058] The reinforcement learning response strategy module combines historical response data to dynamically adjust response measures. This module achieves rapid and effective response to abnormal network behavior through strategy learning, effect evaluation and strategy adjustment.

[0059] Parameters and steps:

[0060] Policy learning rate: controls the speed at which the policy is updated.

[0061] Discount factor (γ): Balances the importance of immediate returns and future returns.

[0062] Implementation steps:

[0063] Detection result input: Receives the anomaly detection results of the deep learning anomaly detection module.

[0064] Policy learning: Use historical response data to learn the optimal response strategy, which is achieved through techniques such as policy gradient methods or Q learning.

[0065] Effect evaluation: Calculate the cumulative returns and evaluate the effect of the response strategy.

[0066] Strategy adjustment: Dynamically adjust the response strategy based on the effect evaluation results and optimize the strategy function \(\pi\).

[0067] Strategy output: The optimized response strategy is passed to the communication optimization module and the deep learning application module in wireless communication to perform corresponding operations.

[0068] Output optimized response strategies, such as automatic isolation, warnings, or notifications, to improve the response speed and effectiveness of the system.

[0069] End-to-end anomaly scoring learning module,

[0070] The end-to-end anomaly scoring learning module uses advanced scoring algorithms to learn anomaly scores directly from raw network traffic data. The module uses a deep neural network to score data points for anomaly probability, simplifying the anomaly detection process and improving detection speed and accuracy.

[0071] Parameters and steps:

[0072] Cross Entropy Loss Function: Used for training deep neural networks.

[0073] Regularization term: prevents the model from overfitting.

[0074] Implementation steps:

[0075] Data input: Receive raw network traffic data.

[0076] Model training: Train a deep neural network model to learn anomaly scoring.

[0077] Anomaly scoring: Score the probability of anomalies on data points to provide a basis for rapid anomaly identification.

[0078] Scoring output: The anomaly scoring results are passed to the deep learning anomaly detection module to assist in anomaly detection.

[0079] Output anomaly scores to provide a basis for rapid identification of anomalies and improve the efficiency of the detection process.

[0080] Communication optimization module,

[0081] The communication optimization module is specially designed to reduce the communication overhead in distributed systems. By adopting efficient data compression algorithms, communication protocol optimization and transmission path selection strategies, the module significantly improves system efficiency and response speed.

[0082] Parameters and steps:

[0083] Data compression rate: Compress data to reduce the amount of transmission.

[0084] Communication protocol parameters: such as TCP window size.

[0085] Implementation steps:

[0086] Response policy input: Receives the response policy of the reinforcement learning response policy module.

[0087] Data compression: Use efficient data compression algorithms to reduce data transmission volume, such as LZ77, LZ78, etc.

[0088] Protocol optimization: Adjust communication protocol parameters, such as TCP window size and timeout retransmission mechanism, to improve communication efficiency.

[0089] Path selection: Select the optimal transmission path, reduce communication delays, and use the shortest path algorithm and minimum spanning tree algorithm, etc.

[0090] Optimize data output: pass the optimized communication data to the deep learning application module in wireless communication.

[0091] Output optimized communication data, reduce communication overhead and improve system efficiency.

[0092] Unsupervised learning mechanism,

[0093] Unsupervised learning mechanisms automatically discover abnormal patterns in data in the absence of labeled information. Through algorithms such as autoencoders, this mechanism can automatically identify and learn the inherent structure of data, enhancing the system's detection capabilities under unknown threats.

[0094] Parameters and steps:

[0095] Autoencoder reconstruction error: used to assess anomalies.

[0096] Implementation steps:

[0097] Unlabeled data input: Receive unlabeled network traffic data.

[0098] Autoencoder training: The autoencoder is trained to learn the data pattern and minimize the reconstruction error.

[0099] Anomaly pattern recognition: Automatically identify abnormal patterns in data and evaluate them using reconstruction error or anomaly score.

[0100] Pattern output: Feedback the abnormal pattern recognition results to the deep learning anomaly detection module.

[0101] Output abnormal pattern recognition results to enhance the system's anomaly detection capabilities.

[0102] Deep learning application module in wireless communications,

[0103] The deep learning application module in wireless communication performs channel estimation and signal detection in non-ideal environments. Through the deep learning model, the module can adapt to the time-varying and multipath effects of wireless signals and improve the robustness of the system in complex environments.

[0104] Parameters and steps:

[0105] Channel gain: predicts channel state information.

[0106] Doppler shift: adapts to the time-varying nature of the signal.

[0107] Implementation steps:

[0108] Optimized data input: Receives communication data optimized by the communication optimization module.

[0109] Channel estimation: Use deep learning models to predict channel states, such as channel gain and Doppler shift.

[0110] Signal Detection: Perform signal detection, identify abnormal signals, and utilize advanced signal processing techniques such as beamforming and multi-user detection.

[0111] Result output: The channel estimation and signal detection results are passed to the reinforcement learning response strategy module.

[0112] Output channel estimation results and signal detection results to improve the performance of the system in wireless communication.

[0113] Model interpretability and visualization module,

[0114] The model interpretability and visualization module provides a visual display of the detection results to enhance the interpretability of the model. The module displays the characteristics, scores, and response strategies of abnormal traffic through a graphical interface, allowing users to intuitively understand the decision-making process of the model.

[0115] Parameters and steps:

[0116] Visualization type: such as bar chart, line chart.

[0117] Implementation steps:

[0118] Detection result input: Receive the detection results and response strategies of each module.

[0119] Results visualization: The characteristics, scores, and response strategies of abnormal traffic are displayed through a graphical interface, and key indicators are intuitively displayed using charts and graphs.

[0120] Enhanced interpretability: Provide detailed explanations of the model’s decision making process, using Local Interpretable Models (LIME) or

[0121] Techniques such as feature importance scoring.

[0122] Visual output: Display the visualization results to users to provide intuitive understanding.

[0123] Output visual display results to enhance users' understanding of the model's decision-making process.

[0124] Adaptive feedback loop module,

[0125] The adaptive feedback loop module monitors system performance in real time, automatically collects performance data and user feedback, evaluates system performance, and automatically adjusts system parameters based on the evaluation results to achieve self-optimization and continuous learning of the system.

[0126] Parameters and steps:

[0127] Performance indicators: such as detection accuracy and response time.

[0128] User Feedback: Collect user feedback on system performance.

[0129] Implementation steps:

[0130] Performance monitoring: Real-time monitoring of system performance, such as detection accuracy and response time.

[0131] Data Collection: Automatically collect performance data and user feedback, using advanced monitoring tools and user feedback mechanisms.

[0132] Performance evaluation: Evaluate system performance, identify performance bottlenecks and improvement points, and utilize data analysis techniques.

[0133] Parameter adjustment: Automatically adjust system parameters based on evaluation results, such as adjusting detection thresholds or optimizing algorithm parameters.

[0134] Optimization output: Feedback the system parameter adjustment results to related modules, such as the adaptive feature extraction module and the deep learning anomaly detection module.

[0135] Output system parameter adjustment results to achieve self-optimization and improve the overall performance and adaptability of the system.

[0136] Through the above detailed description, the functions, parameters, steps and results of each module are clearly demonstrated, ensuring the efficiency, accuracy and adaptability of the entire system from data collection to anomaly detection, response strategy optimization, communication optimization, model interpretation and visualization, and self-optimization.

[0137] 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 communication network anomaly detection and response system based on deep learning and reinforcement learning, comprising: Adaptive feature extraction module, which is used to analyze network traffic data, automatically identify and extract key features, such as traffic size, packet type, frequency distribution, etc., and optimize the feature recognition process through adaptive algorithms to adapt to dynamic changes in network behavior; Deep learning anomaly detection module, which integrates autoencoders and generative adversarial networks to learn normal traffic patterns, reconstruct normal traffic features, and enhance the model's ability to identify abnormal traffic through adversarial training; The reinforcement learning response strategy module combines historical response data to dynamically adjust response measures, including strategy learning, effect evaluation, and strategy adjustment units, to achieve rapid and effective response to abnormal network behavior; The adaptive feedback loop module automatically collects performance data and user feedback, evaluates system performance by real-time monitoring of system performance, and automatically adjusts system parameters based on the evaluation results to achieve self-optimization and continuous learning of the system; the adaptive feature extraction module first analyzes and extracts key features of network traffic, which are then input into the deep learning anomaly detection module, where the integrated autoencoder and generative adversarial network learn and identify normal and abnormal traffic patterns; the detection results are then passed to the reinforcement learning response strategy module, which dynamically adjusts the response measures using historical response data; The adaptive feedback loop module monitors system performance, collects user feedback, and automatically adjusts system parameters to optimize overall performance. This closed-loop process ensures that the system can adapt to dynamic changes in network behavior in real time, quickly respond to abnormal behavior, and continuously self-optimize, thereby significantly improving the security and stability of the communication network.

2. The system according to claim 1, characterized in that The end-to-end anomaly scoring learning module adopts advanced scoring algorithms to learn anomaly scores directly from raw network traffic data, including but not limited to using deep neural networks to score data points for anomaly probabilities, so as to simplify the anomaly detection process and improve detection speed and accuracy.

3. The system according to claim 1, characterized in that The communication optimization module is specially designed to reduce the communication overhead in the distributed system, including but not limited to the use of efficient data compression algorithms, communication protocol optimization and transmission path selection strategies to improve system efficiency and response speed.

4. The system according to claim 1, characterized in that The unsupervised learning mechanism uses algorithms such as autoencoders to automatically discover abnormal patterns in data in the absence of labeled information, and through an iterative optimization process, continuously improves the accuracy of anomaly scoring and enhances the system's detection capabilities under unknown threats.

5. The system according to claim 1, wherein: The deep learning application module in wireless communication is used to perform channel estimation and signal detection in non-ideal environments, including but not limited to using deep learning models to adapt to the time-varying and multipath effects of wireless signals and enhance the robustness of the system.

6. The system according to claim 1, wherein: The model interpretability and visualization module provides a visual display of the detection results, including but not limited to displaying the characteristics, scores and response strategies of abnormal traffic through a graphical interface, enhancing the interpretability of the model and facilitating users to understand the model decision-making process.

7. The system according to claim 1, wherein: The adaptive feature extraction module further includes a feature importance evaluation unit for evaluating the contribution of features to anomaly detection through a statistical learning method and selecting the most representative features to improve the accuracy and efficiency of anomaly detection.

8. The system according to claim 1, wherein: The deep learning anomaly detection module also includes a post-processing unit for anomaly identification results, which is used to further analyze and confirm the detection results through logical rules or secondary detection methods, reduce false positives and false negatives, and improve the reliability of the system.

9. The system according to claim 1, wherein: The reinforcement learning response strategy module also includes a multi-objective optimization unit for selecting the optimal combination among multiple response measures, balancing the detection speed, accuracy and system resource consumption to achieve the best exception handling effect.

10. A communication network anomaly detection and response method using the system according to any one of claims 1 to 9, comprising the following steps: s1, uses the adaptive feature extraction module to extract the features of network traffic data, and updates and optimizes the features in real time; s2, uses the deep learning anomaly detection module to identify anomalies in network traffic, and enhances the recognition ability of the model by generating adversarial networks; s3, using the reinforcement learning response strategy module to dynamically adjust the response measures according to the detection results; s4, uses the end-to-end anomaly scoring learning module to score network traffic anomalies and achieve rapid anomaly identification; s5, optimize the communication process in the distributed system using the communication optimization module; s6, using unsupervised learning mechanisms to process unlabeled network traffic data; s7, using deep learning application modules in wireless communications to perform channel estimation and signal detection in complex environments; s8, uses the model interpretability and visualization module to provide a visual display of the detection results; s9, using the adaptive feedback loop module to perform system self-learning and optimization based on response effects and user feedback.

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