Distributed network security automatic intelligent compliance system

Abstract

The invention relates to the technical field of new-generation information, and discloses a distribution-based network security automatic intelligent compliance system, which is characterized in that a distributed node deployment module adopts a distributed algorithm to distribute compliance detection tasks to each node; the data acquisition and processing module is used for automatically acquiring network logs, user behaviors and traffic data; the compliance rule base and updating module performs compliance judgment according to a multi-dimensional dynamic rule matching method; the intelligent compliance judgment module performs multi-level evaluation on network compliance through a distributed deep learning model; and the dynamic risk response module calculates a real-time response priority, triggers a corresponding response mechanism based on the priority, and supports automatic emergency strategy generation. According to the invention, efficient detection and dynamic response of network compliance are realized through an intelligent technology, and an efficient, intelligent and reliable solution is provided for network security management.

Description

Technical Field

[0001] The present invention relates to the field of new generation information technology, and in particular to a distributed network security automated intelligent compliance system. Background Art

[0002] With the rapid development of network technology and the continuous improvement of informatization, network security has become an important issue that all walks of life in society urgently need to solve. Traditional network security compliance detection methods usually adopt a centralized architecture to monitor and evaluate the security compliance of the entire network through a single central node. However, this method has many limitations, mainly manifested in low detection efficiency, high risk of single point failure, and lack of adaptability to large-scale distributed network environments. In addition, the update of the compliance detection rule library often relies on manual operations, which cannot quickly respond to emerging network security threats, and the rule matching is mostly static, which makes it difficult to cope with complex network attack patterns and dynamically changing compliance requirements. At the same time, most traditional risk response mechanisms rely on preset fixed strategies and lack real-time dynamic adjustment and optimization capabilities, resulting in network security management being unable to respond to network security incidents in a timely and effective manner.

[0003] Therefore, developing an automated intelligent network security compliance system based on a distributed architecture, combined with dynamic rule updates, intelligent evaluation and real-time response mechanisms, has become an urgent need to improve the efficiency and accuracy of network security compliance detection. Summary of the invention

[0004] There is an urgent practical need to build a network security automated intelligent compliance system that integrates dynamic rule updates, intelligent evaluation, and real-time response mechanisms to improve the efficiency and accuracy of network security compliance detection. In view of this, the present invention proposes a distributed network security automated intelligent compliance system, which aims to solve the problems of low efficiency, high risk of single point failure, lack of adaptability, and lagging rule updates and response mechanisms in the prior art. This system can improve the efficiency and accuracy of detection while reducing the risk of single point failures through the collaborative work of distributed nodes. It can update the compliance detection rule base in real time, quickly adapt to new network security threats, and dynamically match complex network attack patterns and changing compliance requirements through an intelligent evaluation mechanism. In addition, the real-time response mechanism can dynamically adjust and optimize the risk response strategy according to the real-time situation of network security events, thereby achieving more flexible and effective network security management.

[0005] The present invention proposes a distributed network security automated intelligent compliance system, including a distributed node deployment module, a data acquisition and processing module, a compliance rule base and update module, an intelligent compliance determination module and a dynamic risk response module;

[0006] Wherein, the distributed node deployment module is configured to use a distributed algorithm to distribute compliance detection tasks to each node, and is connected to the data acquisition and processing module through a communication interface to coordinate the acquisition tasks of each node;

[0007] The data collection and processing module is configured to automatically collect and process network logs, user behavior and traffic data, and transmit the processed standardized data to the compliance rule library and update module through a data transmission interface;

[0008] The compliance rule library and update module is configured to store and update multi-dimensional dynamic rules, perform compliance determination based on received standardized data, and transmit the determination results to the intelligent compliance determination module;

[0009] The intelligent compliance determination module is configured to perform multi-level compliance assessment through a distributed deep learning model, and feed back the assessment results to the compliance rule base and update module to optimize the rule base, and send the assessment results to the dynamic risk response module;

[0010] The dynamic risk response module is configured to calculate the real-time response priority according to the evaluation results of the intelligent compliance determination module, and trigger the corresponding response mechanism based on the priority; the dynamic risk response module is also configured to support automated emergency strategy generation, calculate the optimal response path through a multi-objective optimization algorithm, and return the emergency strategy to the distributed node deployment module to dynamically adjust the node task allocation plan to achieve closed-loop optimization.

[0011] Preferably, the distributed node deployment module calculates the task weight of each node through the following distributed task allocation formula and dynamically allocates compliance detection tasks:

[0012] ;

[0013] Among them, W i represents the task allocation weight of the i-th node; R i represents the computing resource utilization of the i-th node; R j represents the computing resource utilization of the jth node; j represents an index variable ranging from 1 to n; n represents the total number of distributed nodes; L i represents the current task load of the i-th node; T represents the total task volume.

[0014] Preferably, the data collection and processing module performs multi-dimensional noise reduction processing on the collected network logs, user behavior and traffic data through the following formula:

[0015] ;

[0016] Where D(t) represents the original data at time t; D′(t) represents the denoised data at time t; M represents the number of samples in the sliding window; D k (t) represents the data value of the kth sampling point.

[0017] Preferably, the compliance rule base and the update module perform compliance determination through a dynamic rule matching algorithm, and the relevance score is calculated by the following formula:

[0018] ;

[0019] Among them, S k represents the matching relevance score of rule k; R ik It indicates the degree of compliance of the i-th parameter under rule k; p indicates the total number of parameters required for rule matching.

[0020] Preferably, the intelligent compliance determination module performs multi-level evaluation through a distributed deep learning model, and its model evaluation formula is as follows:

[0021] ;

[0022] Where C represents the compliance assessment result; σ represents the Sigmoid function; L represents the number of layers of the deep learning model; W i , b i Represent the weight and bias of the i-th layer respectively; X i Represents input data; ReLU(x)=max(0,x) represents the rectified linear unit activation function.

[0023] Preferably, the dynamic risk response module calculates the real-time response priority by the following formula, and triggers the corresponding response mechanism based on the priority:

[0024] ;

[0025] Among them, P r represents the risk response priority; V represents the risk severity score; E represents the exposure level; λ represents the dynamic adjustment factor; T r represents the expected response time; ϵ represents the minimum value that avoids the denominator being zero.

[0026] Preferably, the dynamic risk response module uses a multi-objective optimization algorithm to generate an optimal response path, and its objective function is as follows:

[0027] ;

[0028] Among them, T represents the time required for response; R represents the degree of risk reduction; C represents the response cost; α, β, and γ represent the target weight coefficients respectively.

[0029] Preferably, the system supports multi-threaded task scheduling optimization, and its scheduling efficiency is evaluated by the following formula:

[0030] ;

[0031] Among them, E s Indicates the task scheduling efficiency; L i represents the total load of the i-th task; T i represents the completion time of the i-th task; n represents the total number of tasks.

[0032] Preferably, the compliance rule base and update module optimize the rule base through an incremental learning mechanism, and the optimization increment calculation formula is:

[0033] ;

[0034] Among them, ΔR represents the optimization increment of the rule base; ω k represents the weight of rule k; , are the score values ​​of rule k before and after updating; q represents the number of updated rules.

[0035] Preferably, the system optimizes overall compliance through a closed-loop feedback mechanism, and the feedback adjustment formula is as follows:

[0036] ;

[0037] Among them, F t 、F t-1 They represent the feedback adjustment values ​​at time t and t-1 respectively; η represents the feedback adjustment rate; Rt, R t−1 denote the compliance evaluation values ​​at time t and t−1 respectively.

[0038] Compared with the prior art, the present invention has the following beneficial effects:

[0039] Efficiency and reliability: This system uses a distributed algorithm for task allocation, effectively avoiding the risk of single point failure and significantly improving the efficiency of compliance testing and the reliability of the system.

[0040] Dynamic rule adaptive update: Through multi-dimensional dynamic rule matching and incremental learning mechanism, this system can automatically update the rule base according to the latest network security threats, thereby enhancing the timeliness and accuracy of compliance detection.

[0041] Intelligent compliance assessment: This study uses a distributed deep learning model to conduct multi-level compliance assessment, which can cope with complex network behaviors and achieve more comprehensive compliance judgment.

[0042] Response mechanism optimization: Based on dynamic priority calculation and multi-objective optimization algorithm, this system can generate the optimal emergency response strategy in real time, significantly improving the efficiency of risk management and emergency response.

[0043] Closed-loop optimization capability: The system dynamically adjusts task allocation, rule base updates, and response strategies through a feedback mechanism to ensure continuous optimization of network security management. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0045] Figure 1 This is a functional block diagram of the distributed network security automated intelligent compliance system based on the present invention. DETAILED DESCRIPTION

[0046] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art. It should be noted that, in the absence of conflict, the embodiments of the present invention and the features described in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0047] See also Figure 1 ,This embodiment provides a distributed network security automated intelligent compliance system, including a distributed node deployment module, a data collection and processing module, a compliance rule base and update module, an intelligent compliance determination module and a dynamic risk response module;

[0048] Wherein, the distributed node deployment module is configured to use a distributed algorithm to distribute compliance detection tasks to each node, and is connected to the data acquisition and processing module through a communication interface to coordinate the acquisition tasks of each node;

[0049] The data collection and processing module is configured to automatically collect and process network logs, user behavior and traffic data, and transmit the processed standardized data to the compliance rule library and update module through a data transmission interface;

[0050] The compliance rule library and update module is configured to store and update multi-dimensional dynamic rules, perform compliance determination based on received standardized data, and transmit the determination results to the intelligent compliance determination module;

[0051] The intelligent compliance determination module is configured to perform multi-level compliance assessment through a distributed deep learning model, and feed back the assessment results to the compliance rule base and update module to optimize the rule base, and send the assessment results to the dynamic risk response module;

[0052] The dynamic risk response module is configured to calculate the real-time response priority according to the evaluation results of the intelligent compliance determination module, and trigger the corresponding response mechanism based on the priority; the dynamic risk response module is also configured to support automated emergency strategy generation, calculate the optimal response path through a multi-objective optimization algorithm, and return the emergency strategy to the distributed node deployment module to dynamically adjust the node task allocation plan to achieve closed-loop optimization.

[0053] It can be seen that this embodiment proposes a network security automated intelligent compliance system based on a distributed architecture, which is composed of multiple key modules, including a distributed node deployment module, a data collection and processing module, a compliance rule base and update module, an intelligent compliance determination module, and a dynamic risk response module;

[0054] In this embodiment, the distributed node deployment module is designed to use advanced distributed algorithms to allocate compliance detection tasks to each network node, and is connected to the data collection and processing module through a communication interface to ensure that the nodes can perform the collection tasks in a coordinated and consistent manner;

[0055] The data collection and processing module is endowed with the ability to automatically collect network logs, user behavior and traffic data, and can process these data. The processed standardized data will be transmitted to the compliance rule base and update module through the data transmission interface;

[0056] The compliance rule base and update module is responsible for storing and regularly updating a series of multi-dimensional dynamic rules. It will make compliance judgments based on the received standardized data and pass the judgment results to the intelligent compliance judgment module;

[0057] The intelligent compliance determination module uses a distributed deep learning model to conduct in-depth multi-level compliance assessments. The assessment results are not only fed back to the compliance rule base and update module to help optimize the rule base, but are also sent to the dynamic risk response module.

[0058] The dynamic risk response module calculates the real-time response priority based on the evaluation results of the intelligent compliance judgment module, and triggers the corresponding response mechanism based on this priority. In addition, the dynamic risk response module also has the ability to support the generation of automated emergency strategies, calculates the optimal response path through a multi-objective optimization algorithm, and returns these emergency strategies to the distributed node deployment module, thereby dynamically adjusting the node task allocation plan to achieve closed-loop optimization of the entire system.

[0059] It is understandable that the advantage of this embodiment is that it can significantly improve the automation and intelligence level of network security compliance. Through distributed node deployment, the system can efficiently utilize network resources to achieve comprehensive monitoring and compliance detection of large-scale network environments. At the same time, the automated collection and processing capabilities of the data acquisition and processing module ensure the accuracy and timeliness of the data, providing a solid foundation for compliance determination. The dynamic update mechanism of the compliance rule base and the update module enables the system to respond to new compliance requirements and threats in a timely manner, improving the flexibility and adaptability of the system. The intelligent compliance determination module performs multi-level evaluation through a deep learning model, which not only improves the accuracy of compliance determination, but also can discover potential compliance risks, providing strong support for the network security management of enterprises. The real-time response and automated emergency strategy generation capabilities of the dynamic risk response module further enhance the system's ability to respond to network security incidents and reduce the security risks of enterprises. In summary, the distributed network security automation intelligent compliance system of this embodiment provides enterprises with an efficient, intelligent and flexible network security compliance solution.

[0060] In some embodiments of the present application, the distributed node deployment module calculates the task weight of each node through the following distributed task allocation formula and dynamically allocates compliance detection tasks:

[0061] ;

[0062] Among them, W i represents the task allocation weight of the i-th node; R i represents the computing resource utilization of the i-th node; R j represents the computing resource utilization of the jth node; j represents an index variable ranging from 1 to n; n represents the total number of distributed nodes; L i represents the current task load of the i-th node; T represents the total task volume.

[0063] It can be understood that this embodiment proposes an innovative distributed task allocation formula for dynamically adjusting the task weight of each node, thereby optimizing the allocation of compliance detection tasks. In this formula, Wi represents the task allocation weight of the i-th node, which is calculated based on the computing resource utilization (Ri) of the node and the current task load (Li). At the same time, the formula also takes into account the computing resource utilization (Rj) of other nodes (j) to ensure the fairness and efficiency of task allocation. The total task volume (T) is used as a benchmark to evaluate the proportion of tasks that each node should undertake. In this way, the system can intelligently adjust the task allocation plan according to the real-time status of each node and the overall task requirements, thereby maximizing resource utilization and task execution efficiency. This innovation not only enhances the flexibility and responsiveness of the system, but also provides enterprises with a more reliable and efficient network security compliance solution.

[0064] In some embodiments of the present application, the data collection and processing module performs multi-dimensional noise reduction processing on the collected network logs, user behavior and traffic data using the following formula:

[0065] ;

[0066] Where D(t) represents the original data at time t; D′(t) represents the denoised data at time t; M represents the number of samples in the sliding window; D k (t) represents the data value of the kth sampling point.

[0067] It is understandable that this embodiment also proposes a unique multi-dimensional noise reduction processing method for improving the accuracy and efficiency of the data acquisition and processing module. In this method, D(t) represents the original data at time t, which may contain various noises and interferences. In order to obtain clearer and more accurate data, the system adopts a noise reduction process to generate D′(t), which is the noise-reduced data at time t. This process uses a sliding window technique, where M represents the number of samples in the sliding window. By considering multiple sampling points (D k (t) represents the data value of the kth sampling point), the system can effectively smooth the data and reduce the interference of noise. This method can not only improve the accuracy of the data, but also optimize the efficiency and reliability of subsequent compliance determination. By combining this multi-dimensional noise reduction processing technology, the network security automated intelligent compliance system of this embodiment can further improve its automation and intelligence level, and provide enterprises with more accurate and efficient network security compliance management solutions.

[0068] In some embodiments of the present application, the compliance rule base and the update module perform compliance determination through a dynamic rule matching algorithm, and the relevance score is calculated by the following formula:

[0069] ;

[0070] Among them, S k represents the matching relevance score of rule k; R ik It indicates the degree of compliance of the i-th parameter under rule k; p indicates the total number of parameters required for rule matching.

[0071] It is understandable that this embodiment also proposes an advanced dynamic rule matching algorithm to improve the accuracy and efficiency of compliance determination. k represents the matching relevance score of rule k, which is based on the weights of multiple parameters (W i ) and the degree of compliance of these parameters under rule k (R ik ). The weight of each parameter reflects its importance in the compliance judgment, while the degree of compliance measures the degree of match between the actual data and the rule requirements. By comprehensively considering these parameters, the system is able to give a comprehensive score to evaluate whether the network behavior or data meets the compliance requirements. In addition, the algorithm also takes into account the total number of parameters (p) required for rule matching to ensure the comprehensiveness and accuracy of the evaluation. This dynamic rule matching algorithm not only improves the accuracy of compliance judgments, but also enhances the adaptability and flexibility of the system. As the network environment and compliance requirements change, the system can automatically adjust the rule matching strategy to ensure the timeliness and accuracy of compliance judgments. This innovation enables the network security automated intelligent compliance system of this embodiment to better adapt to the complex and changing network environment, and provide enterprises with more comprehensive and efficient compliance management support.

[0072] In some embodiments of the present application, the intelligent compliance determination module performs multi-level evaluation through a distributed deep learning model, and its model evaluation formula is as follows:

[0073] ;

[0074] Where C represents the compliance assessment result; σ represents the Sigmoid function; L represents the number of layers of the deep learning model; W i , b i Represent the weight and bias of the i-th layer respectively; X i Represents input data; ReLU(x)=max(0,x) represents the rectified linear unit activation function.

[0075] It is understandable that this embodiment also proposes an innovative distributed deep learning model for multi-level compliance assessment. In this model, C represents the compliance assessment result, which is obtained through a series of complex calculations and aims to accurately reflect whether the network behavior or data meets the compliance requirements. To achieve this goal, the system uses the Sigmoid function (σ), which can map the input value between 0 and 1 for binary classification problems, that is, compliance or non-compliance. In addition, the model also takes into account the number of layers (L) of the deep learning model, each layer has its specific weights (Wi) and biases (bi), and these parameters are continuously optimized during the training process to improve the accuracy and generalization ability of the model. The input data (Xi) is passed through the model layer by layer, and each layer uses the rectified linear unit activation function (ReLU(x)=max(0,x)) for nonlinear transformation to enhance the expressive power of the model. This multi-level evaluation method not only improves the accuracy of compliance judgment, but also enables the system to automatically learn and adapt to new compliance requirements. By combining the advantages of distributed computing, the network security automated intelligent compliance system of this embodiment can efficiently process large amounts of network data, conduct compliance assessments in real time, and provide enterprises with more timely and reliable compliance management services.

[0076] In some embodiments of the present application, the dynamic risk response module calculates the real-time response priority by the following formula and triggers the corresponding response mechanism based on the priority:

[0077] ;

[0078] Among them, P r represents the risk response priority; V represents the risk severity score; E represents the exposure level; λ represents the dynamic adjustment factor; T r represents the expected response time; ϵ represents the minimum value that avoids the denominator being zero.

[0079] It is understandable that this embodiment also proposes an efficient dynamic risk response mechanism for responding quickly when a network security incident occurs. In this mechanism, P r Represents the priority of risk response, which is calculated based on multiple factors and aims to ensure that the system can give priority to the most important and urgent risk events. The severity score (V) of the risk is an important indicator to measure the potential harm of the risk. It reflects the loss or impact that may be caused if the risk occurs. The exposure level (E) takes into account the scope or number of audiences that the risk event may affect to assess the extensiveness and potential impact of the risk. The dynamic adjustment factor (λ) allows the system to flexibly adjust the priority of risk response based on factors such as the current network environment, resource status or business priority. Estimated response time (T r) is an important parameter for the system to evaluate the time required to handle risk events. It helps the system to allocate resources reasonably and ensure response in the shortest time. In order to avoid the situation where the denominator is zero, the system also introduces a minimum value (ϵ). Through this dynamic risk response mechanism, the network security automated intelligent compliance system of this embodiment can evaluate the priority of risk events in real time and automatically trigger corresponding response measures, such as isolating the affected network area, notifying the security team for emergency processing, or starting the backup system. This efficient response mechanism not only improves the security of the system, but also ensures the continuity and stability of the enterprise business.

[0080] In some embodiments of the present application, the dynamic risk response module uses a multi-objective optimization algorithm to generate an optimal response path, and its objective function is as follows:

[0081] ;

[0082] Among them, T represents the time required for response; R represents the degree of risk reduction; C represents the response cost; α, β, and γ represent the target weight coefficients respectively.

[0083] It is understandable that this embodiment also proposes a dynamic risk response path generation method based on a multi-objective optimization algorithm. In this method, T represents the time required for response, which is a key indicator for measuring the response speed. The shorter the time, the faster the system can respond to risk events and reduce potential damage. R represents the degree of risk reduction, which reflects the control and mitigation effect of the response measures on risk events. The higher the degree of risk reduction, the better the protection effect of the system. C represents the response cost, which covers the investment in manpower, material resources, financial resources and other aspects. The lower the cost, the stronger the economy and practicality of the system. In order to balance these three goals, the system introduces target weight coefficients (α, β, γ), which represent the importance of time, risk reduction and cost in overall optimization respectively. By adjusting these weight coefficients, the system can generate the optimal response path according to different business needs and risk preferences. This multi-objective optimization algorithm not only improves the rationality and effectiveness of the response path, but also enables the system to achieve optimal safety benefits under limited resources.

[0084] In some embodiments of the present application, the system supports multi-threaded task scheduling optimization, and its scheduling efficiency is evaluated by the following formula:

[0085] ;

[0086] Among them, E s Indicates the task scheduling efficiency; L i represents the total load of the i-th task; T i represents the completion time of the i-th task; n represents the total number of tasks.

[0087] It is understandable that this embodiment also proposes an optimization method for multi-threaded task scheduling, aiming to improve the processing capability and response speed of the system in a high-concurrency environment. s Represents the efficiency of task scheduling and is an important indicator for measuring system performance. It comprehensively considers the load of each task (L i ) and completion time (T i ), through reasonable task allocation and scheduling strategies, the system can efficiently handle multiple concurrent tasks. i It reflects the occupation of system resources by the task. The higher the load, the higher the requirement of the task on the system. i It measures the speed at which the system processes tasks. The shorter the time, the stronger the system's processing capability. By calculating the average load and completion time of all tasks, and combining the total number of tasks (n), the system can evaluate the current task scheduling efficiency. This multi-threaded task scheduling optimization method not only improves the concurrent processing capability of the system, but also ensures the stability and reliability of the system under high load conditions. Through this optimization, the network security automation intelligent compliance system of this embodiment can better adapt to complex and changing network environments, and provide enterprises with more comprehensive and efficient security protection.

[0088] In some embodiments of the present application, the compliance rule base and update module optimize the rule base through an incremental learning mechanism, and the optimization increment calculation formula is:

[0089] ;

[0090] Among them, ΔR represents the optimization increment of the rule base; ω k represents the weight of rule k; , are the score values ​​of rule k before and after updating; q represents the number of updated rules.

[0091] It is understandable that this embodiment also proposes a compliance rule base optimization method based on an incremental learning mechanism. In this method, ΔR represents the optimization increment of the rule base and is a key indicator for measuring the effect of rule base update. It evaluates the weight of each rule (ω k ) and score comparison (score values ​​before and after update) to calculate the optimization degree of the rule base during the update process. kIt reflects the importance of the rule in the overall rule base. The higher the weight, the greater the impact of the rule on the system decision. The comparison of the score values ​​before and after the update intuitively shows the improvement brought about by the rule update. By comprehensively considering the number of updated rules (q) and the optimization increment of each rule, the system can accurately evaluate the overall optimization effect of the rule base. This incremental learning mechanism not only improves the timeliness and accuracy of the rule base, but also enables the system to continue to learn and evolve, and better adapt to the ever-changing network security threats. Through this optimization, the network security automated intelligent compliance system of this embodiment can ensure the accuracy and effectiveness of compliance rules, and further enhance the network security protection capabilities of the enterprise.

[0092] In some embodiments of the present application, the system optimizes overall compliance through a closed-loop feedback mechanism, and the feedback adjustment formula is as follows:

[0093] ;

[0094] Among them, F t 、F t-1 They represent the feedback adjustment values ​​at time t and t-1 respectively; η represents the feedback adjustment rate; Rt, R t−1 denote the compliance evaluation values ​​at time t and t−1 respectively.

[0095] It is understandable that this embodiment also proposes an overall compliance optimization method based on a closed-loop feedback mechanism. In this method, F t and F t-1 Represent the feedback adjustment values ​​at time t and t-1, respectively, which reflect the adjustment and optimization of the compliance status by the system at different time points. By comparing these two values, the system can evaluate the trend of compliance over time and make corresponding adjustments accordingly. η represents the feedback adjustment rate, which determines the sensitivity and speed of the system's response to compliance changes. R t and R t-1 They represent the compliance assessment values ​​at time t and t-1 respectively. They are obtained by comprehensively checking and evaluating the compliance status of the system and are key indicators for measuring the compliance level of the system.

[0096] By introducing a closed-loop feedback mechanism, the system can monitor and evaluate its own compliance status in real time. Once a deviation or deficiency is found, the feedback adjustment process is immediately started to quickly restore and improve the compliance level by adjusting the system configuration, optimizing strategies, or enhancing protective measures. This mechanism not only enhances the system's self-repair and self-optimization capabilities, but also ensures that the system always maintains a high degree of compliance and security during long-term operation. Through this optimization, the network security automation intelligent compliance system of this embodiment can provide enterprises with more stable and reliable network security protection.

[0097] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0098] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0099] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0100] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0101] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A distributed network security automated intelligent compliance system, characterized in that: It includes distributed node deployment module, data collection and processing module, compliance rule base and update module, intelligent compliance determination module and dynamic risk response module; Wherein, the distributed node deployment module is configured to use a distributed algorithm to distribute compliance detection tasks to each node, and is connected to the data acquisition and processing module through a communication interface to coordinate the acquisition tasks of each node; The data collection and processing module is configured to automatically collect and process network logs, user behavior and traffic data, and transmit the processed standardized data to the compliance rule library and update module through a data transmission interface; The compliance rule library and update module is configured to store and update multi-dimensional dynamic rules, perform compliance determination based on received standardized data, and transmit the determination results to the intelligent compliance determination module; The intelligent compliance determination module is configured to perform multi-level compliance assessment through a distributed deep learning model, and feed back the assessment results to the compliance rule base and update module to optimize the rule base, and send the assessment results to the dynamic risk response module; The dynamic risk response module is configured to calculate the real-time response priority according to the evaluation results of the intelligent compliance determination module, and trigger the corresponding response mechanism based on the priority; the dynamic risk response module is also configured to support automated emergency strategy generation, calculate the optimal response path through a multi-objective optimization algorithm, and return the emergency strategy to the distributed node deployment module to dynamically adjust the node task allocation plan to achieve closed-loop optimization.

2. The distributed network security automation intelligent compliance system according to claim 1 is characterized in that: The distributed node deployment module calculates the task weight of each node through the following distributed task allocation formula and dynamically allocates compliance detection tasks: ； Among them, W i represents the task allocation weight of the i-th node; R i represents the computing resource utilization of the i-th node; R j represents the computing resource utilization of the jth node; j represents an index variable ranging from 1 to n; n represents the total number of distributed nodes; L i represents the current task load of the i-th node; T represents the total task volume.

3. The distributed network security automation intelligent compliance system according to claim 2 is characterized in that: The data collection and processing module performs multi-dimensional noise reduction processing on the collected network logs, user behavior and traffic data through the following formula: ； Where D(t) represents the original data at time t; D′(t) represents the denoised data at time t; M represents the number of samples in the sliding window; D k (t) represents the data value of the kth sampling point.

4. The distributed network security automation intelligent compliance system according to claim 3 is characterized in that: The compliance rule base and update module perform compliance determination through a dynamic rule matching algorithm, and the relevance score is calculated by the following formula: ； Among them, S k represents the matching relevance score of rule k; R ik It indicates the degree of compliance of the i-th parameter under rule k; p indicates the total number of parameters required for rule matching.

5. The distributed network security automation intelligent compliance system according to claim 4 is characterized in that: The intelligent compliance determination module performs multi-level evaluation through a distributed deep learning model, and its model evaluation formula is as follows: ； Where C represents the compliance assessment result; σ represents the Sigmoid function; L represents the number of layers of the deep learning model; W i 、b i Represent the weight and bias of the i-th layer respectively; X i Represents input data; ReLU(x)=max(0,x) represents the rectified linear unit activation function.

6. The distributed network security automation intelligent compliance system according to claim 5 is characterized in that: The dynamic risk response module calculates the real-time response priority using the following formula and triggers the corresponding response mechanism based on the priority: ； Among them, P r represents the risk response priority; V represents the risk severity score; E represents the exposure level; λ represents the dynamic adjustment factor; T r represents the expected response time; ϵ represents the minimum value that avoids the denominator being zero.

7. The distributed network security automation intelligent compliance system according to claim 6 is characterized in that: The dynamic risk response module uses a multi-objective optimization algorithm to generate the optimal response path, and its objective function is as follows: ； Among them, T represents the time required for response; R represents the degree of risk reduction; C represents the response cost; α, β, and γ represent the target weight coefficients respectively.

8. The distributed network security automation intelligent compliance system according to claim 7 is characterized in that: The system supports multi-threaded task scheduling optimization, and its scheduling efficiency is evaluated by the following formula: ； Among them, E s Indicates the task scheduling efficiency; L i represents the total load of the i-th task; T i represents the completion time of the i-th task; n represents the total number of tasks.

9. The distributed network security automation intelligent compliance system according to claim 8 is characterized in that: The compliance rule base and update module optimize the rule base through an incremental learning mechanism, and the optimization increment calculation formula is: ； Among them, ΔR represents the optimization increment of the rule base; ω k represents the weight of rule k; , are the score values ​​of rule k before and after updating; q represents the number of updated rules.

10. The distributed network security automation intelligent compliance system according to claim 9 is characterized in that: The system optimizes overall compliance through a closed-loop feedback mechanism, and its feedback adjustment formula is as follows: ； Among them, F t 、F t-1 They represent the feedback adjustment values ​​at time t and t-1 respectively; η represents the feedback adjustment rate; Rt, R t−1 denote the compliance evaluation values ​​at time t and t−1 respectively.

Images