Network security detection system

Abstract

The invention discloses a network security detection system, which comprises an intention perception layer, the intention perception layer is connected with a dynamic immune module, the dynamic immune module is connected with a time management and strategy layer, and the time management and strategy layer is connected with an ethical and transparent decision engine. According to the system, multiple advanced technologies are combined, the network security capability is improved, through deep intention analysis, the system can rapidly recognize complex threats, reduce false alarms and effectively cope with unknown attacks, the self-adaptive immune response function enables the system to dynamically adjust defense strategies according to threat changes, continuous protection is ensured, and meanwhile, the system has a good application prospect. The time-dimension enhancement technology helps to identify advanced threats with a long latency period, the visual interface and report improve the operation convenience, a security team can rapidly understand and respond to the threats, in addition, the modular design ensures the high expansibility of the system, is convenient to upgrade and integrate new functions, and the system has the multiple advantages of accurate detection, rapid response and simple and convenient operation.

Description

Technical Field

[0001] The present invention relates to the technical field of network security, and in particular to a network security detection system. Background Art

[0002] Network security detection is a technology and process designed to identify and respond to potential security threats and abnormal behaviors in network environments. These threats may include malware infection, unauthorized access, data leakage, and other forms of network attacks. Through network security detection, enterprises and organizations can detect and resolve these threats in a timely manner, thereby protecting the confidentiality, integrity, and availability of their networks and information resources. Network security detection generally involves monitoring system and network activities to detect abnormal or suspicious behavior. Identify and remove malware such as viruses, worms, and Trojan horses. Study the interactive behavior of users and machines to identify activities that deviate from normal patterns. Use external intelligence data to enhance local detection capabilities.

[0003] Current network security detection technologies mainly include signature detection, behavior detection, anomaly detection, machine learning and artificial intelligence detection, sandbox analysis, and endpoint detection and response (EDR). Signature detection quickly identifies known attacks by matching predefined threat features, but is ineffective against unknown or variant threats; behavior detection and anomaly detection focus on identifying abnormal activities of the system, which can detect unknown threats, but are prone to false positives, resulting in an increased burden on the security team; machine learning and AI detection use data analysis to identify complex threats. Although it improves the detection rate, it relies on a large amount of training data and accurate models, and may sometimes be subject to data bias or errors; sandbox analysis observes the behavior of unknown files by running them in an isolated environment. Although it can effectively combat "zero-day" attacks, it will cause detection delays and affect real-time response; finally, EDR solutions monitor and respond to activities on terminal devices to enhance device security, but implementation and management are complex, especially requiring professional skills to analyze and respond to threats, and may not be able to defend against advanced persistent threats. Summary of the invention

[0004] In view of the defects in the prior art, the present invention provides a network security detection system, including an intention perception layer, the intention perception layer is connected to a dynamic immunity module, the dynamic immunity module is connected to a time management and strategy layer, the time management and strategy layer is connected to an ethics and transparent decision engine, and the ethics and transparent decision engine is connected to a collection layer and an expansion layer;

[0005] The intent perception layer includes a user intent analysis module and intent anomaly spectrum management, the dynamic immunity module includes an adaptive immunity center and a threat isolation and analysis sandbox, the time management and strategy layer includes a multi-dimensional timing analysis engine and a time evolution supervision system, and the ethics and transparent decision-making engine includes a transparency display module and an ethical risk assessor.

[0006] Preferably, the user intention analysis module uses deep learning and natural language processing technology to analyze data from user behavior logs, system commands and network traffic to identify the potential intentions of user operations in real time. The intention anomaly spectrum management builds and maintains the intention spectrum of users and systems, provides automated comparative analysis, and is updated in real time through the knowledge graph.

[0007] Preferably, the adaptive immune center simulates the memory and response mechanism of the immune system and builds complex attack identification and response strategies based on historical data. The threat isolation and analysis sandbox uses appropriate induction strategies to allow suspicious activities to enter the security sandbox and conduct behavioral analysis in an isolated environment without affecting other parts of the system.

[0008] Preferably, the multi-dimensional time series analysis engine integrates retrospective analysis of historical events and predictive modeling of future threats, and provides a basis for security decision-making through AI-driven analysis. The event evolution supervision system monitors the evolution trend of security events in real time, maintains a clear insight into the event situation, and provides timely strategy adjustment suggestions.

[0009] Preferably, the transparency display module provides a visual interface for security incident processing, transparent decision-making paths, and ensures the transparency and explainability of the system operation process. The ethical risk assessor is a built-in compliance analysis to ensure that the operation of the detection and response mechanism complies with privacy and ethical standards and resolves possible legal and ethical risks.

[0010] Preferably, the collection layer and the extension layer are open API interfaces of the API and integration framework, which support integration with existing security tools within the enterprise, including SIEM and IAM tools, to improve system compatibility and scalability.

[0011] The beneficial effects of the present invention are embodied in:

[0012] The present invention adopts deep intent analysis, which can quickly identify the true intention of complex threats in massive data, reduce false alarms and improve the detection ability of unknown attacks. Secondly, the system has an adaptive immune response function, which can dynamically adjust the defense strategy according to the changes in threats, thereby providing continuous and effective protection. Furthermore, due to the use of multi-dimensional enhancement technology, the system can correlate events over a larger time frame and identify advanced threats with a long latent time. In addition, the system also emphasizes user-friendliness, through an intuitive interface and reporting format, it helps the security team to quickly understand and take appropriate measures. In addition, the modular design ensures the high scalability of the system, and can be easily upgraded and integrated with additional functions as security needs change. These advantages make the system not only leading in detection accuracy and response speed, but also has significant advantages in ease of operation and adaptability, providing comprehensive security protection for enterprises. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following is a brief introduction to the drawings required for the specific embodiments or the description of the prior art. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn according to the actual scale.

[0014] Figure 1 This is a schematic diagram of the system process framework. DETAILED DESCRIPTION

[0015] The following embodiments of the technical solution of the present invention are described in detail in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and are therefore only used as examples, and cannot be used to limit the protection scope of the present invention.

[0016] It should be noted that, unless otherwise specified, the technical terms or scientific terms used in this application should have the common meanings understood by those skilled in the art to which the present invention belongs.

[0017] like Figure 1 As shown, a network security detection system includes an intention perception layer, the intention perception layer is connected to a dynamic immunity module, the dynamic immunity module is connected to a time management and strategy layer, the time management and strategy layer is connected to an ethics and transparent decision engine, and the ethics and transparent decision engine is connected to a collection layer and an expansion layer;

[0018] The intent perception layer includes the user intent analysis module and the intent anomaly spectrum management; the dynamic immunity module includes the adaptive immunity center and the threat isolation and analysis sandbox; the time management and strategy layer includes the multi-dimensional timing analysis engine and the time evolution supervision system; the ethics and transparent decision-making engine includes the transparency display module and the ethical risk assessor.

[0019] The user intention analysis module uses deep learning and natural language processing technology to analyze data from user behavior logs, system commands and network traffic to identify the potential intentions of user operations in real time. The intention anomaly spectrum management builds and maintains the intention spectrum of users and systems, provides automated comparative analysis, and is updated in real time through the knowledge graph.

[0020] The adaptive immunity center simulates the memory and response mechanism of the immune system and builds complex attack identification and response strategies based on historical data. The threat isolation and analysis sandbox uses appropriate induction strategies to allow suspicious activities to enter the security sandbox and conduct behavioral analysis in an isolated environment without affecting other parts of the system.

[0021] The multi-dimensional time series analysis engine integrates retrospective analysis of historical events and predictive modeling of future threats, and provides a basis for security decision-making through AI-driven analysis. The event evolution supervision system monitors the evolution trend of security events in real time, maintains a clear insight into the event situation, and provides timely strategy adjustment suggestions.

[0022] The transparency display module provides a visual interface for security incident processing, transparent decision-making paths, and ensures the transparency and explainability of the system operation process. The ethical risk assessor is a built-in compliance analysis to ensure that the operation of the detection and response mechanism complies with privacy and ethical standards and resolves possible legal and ethical risks.

[0023] The collection layer and extension layer are open API interfaces for the API and integration framework, which support integration with existing security tools within the enterprise, including SIEM and IAM tools, to improve system compatibility and scalability.

[0024] Specifically, the user intention analysis module uses the deep learning framework TensorFlow or PyTorch to build a model based on behavioral pattern recognition. Natural language processing technology is used to analyze the interaction information between users and the system and monitor user operation intentions.

[0025] The main steps are to extract relevant data from user operation logs, system commands, and network traffic, and build a user behavior feature library. Use historical data to supervise the model and train a classification model that can identify user behavior intentions. In the production environment, use stream processing tools Apache Kafka and Apache Flink to achieve rapid processing of real-time data and intent recognition. Monitor user file access and application startup frequency, and analyze whether there are abnormal operations that do not match the daily pattern behind their intentions, such as data leakage and malicious operations.

[0026] The abnormal intent genealogy management uses the graph database Neo4j to build and maintain the intent genealogy of users and systems. The knowledge graph technology is used to analyze and compare the deviation between user behavior and the normal behavior genealogy. Based on historical behavior data, the normal intent genealogy of each user and system is built. By monitoring system behavior, the intent genealogy graph is dynamically updated and maintained. The PageRank or GraphSAGE algorithm of the graph database is used to identify abnormal intent. If a user's access rights exceed the resources within his or her scope of responsibility in a specific time period, the system will detect the abnormal operation based on the intent genealogy and issue an early warning.

[0027] The adaptive immunity center is based on machine learning models for risk identification and response strategy learning (such as reinforcement learning models). It uses cluster computing technology Kubernetes for rapid expansion to ensure efficient processing of large amounts of data. It builds an immune "memory" database to store known threat characteristics and their response strategies. The system monitors and learns new attack methods in real time and incorporates them into the immune response mechanism. It establishes a priority-based response strategy to ensure that the system responds quickly and appropriately based on the type of threat. When the system detects a new type of cyber attack, it quickly adjusts the defense strategy through a machine learning model and remembers the threat to optimize future responses.

[0028] Threat Isolation and Analysis The sandbox creates an isolated environment using the container technology Docker. Use virtualization technologies VMware and Hyper-V to further ensure that the system is isolated from the sandbox environment. Once the system detects a high-risk activity, it directs the activity to the sandbox for analysis. In-depth analysis of suspicious operations is performed in the sandbox environment, and their potential impact is simulated. The ELK Stack (Elasticsearch, Logstash, Kibana) is used to monitor activities in the sandbox in real time and generate analysis reports. When a suspicious process attempts to execute a privileged command, the system introduces it into the sandbox for execution, observes its complete behavior, and prevents potential harm to the main system.

[0029] The multi-dimensional time series analysis engine uses the time series database TimescaleDB to store and analyze historical security events. AI prediction models such as LSTM or RNN are used to predict future attack paths. The timestamps and associated data of all security events are stored in the time series database. The evolution of security events in time series is displayed through the visualization tool Grafana. Train AI models to predict possible future attack paths and means by learning from event sequence data. Increase monitoring intensity at key time points (such as when the company releases new products or when senior personnel change), predict possible network attacks, and prevent potential risks.

[0030] The event evolution monitoring system uses the stream processing platform Apache Flink to achieve real-time monitoring. Use data visualization platforms such as PowerBI or Tableau to display event development trends. Use the stream processing system to analyze the real-time evolution of events. Use the AI ​​analysis system to identify event development trends and make recommendations for defense strategy adjustments. Use the automation tool Jenkins to regularly generate reports for decision makers to refer to. In the early stages of DDoS attacks, the system analyzes traffic growth trends, predicts its expansion direction, and adjusts network configuration in advance.

[0031] The transparency display module uses the visualization framework D3.js to provide a dynamic display of the security decision-making process. Explanatory AI technology explains the model output through LIME or SHAP to make the decision-making process transparent. Setting up a transparent interface provides security managers with a clear interface to view the basis and process of each decision of the system. When AI makes a risk judgment, the system automatically generates an explanation to help security personnel understand the logic behind the decision. Security personnel can view the basis for the system's decision on a potential threat in the interface, such as why a certain behavior is marked as suspicious.

[0032] The ethical risk assessor uses the compliance checking tool OpenSCAP to ensure that system operations comply with data privacy regulations. Built-in privacy protection models (such as differential privacy) ensure that the system does not leak private information when processing personal data. During each security decision process, the system will conduct a privacy risk assessment and comply with privacy regulations such as GDPR and CCPA. The system automatically generates compliance audit logs for internal or external review. When dealing with threat detection involving personal information, the system will protect the data through differential privacy technology to ensure that sensitive information is not inadvertently leaked.

[0033] API and integration framework RESTful API integrates with existing security tools SIEM and IAM systems. Use microservice architecture to provide flexible expansion capabilities. Allow third-party security tools to interact with the system through REST API, such as obtaining logs from SIEM systems or sending detection results to IAM systems. Package each functional module into microservices to facilitate subsequent expansion and function updates. It can be integrated with the company's existing SIEM platform to automatically extract data from logs and improve detection efficiency.

[0034] The learning and optimization platform uses online learning models, and the system continuously optimizes and updates the detection model during operation. Combined with A / B testing, evaluate the detection effect of the new model on existing threats. Based on new threat intelligence and security events, the system updates itself through online learning models. Evaluate new models in production environments through A / B testing, and decide whether to deploy updates based on test results. When a new variant of a certain type of phishing attack appears, automatically update its detection rules through the online learning module to improve response capabilities. Through intent analysis and behavioral immune system, you can better understand and predict complex threats.

[0035] Compared with traditional behavior detection methods, the present invention analyzes the intention behind the user's operation instead of focusing only on the direct behavior, which enables more accurate identification of normal operations and malicious activities. Understanding the user's intention helps to more accurately distinguish abnormal activities from legitimate behaviors, thereby reducing the false positive rate and the risk of missed reports.

[0036] The present invention draws on the self-learning and memory characteristics of the biological immune system, and can dynamically adjust its defense strategy according to new threat intelligence and historical threat patterns. Through continuous learning and updating of machine learning models, the system can quickly adapt to new attack methods that appear in the current network and improve defense efficiency. The present invention uses a multi-dimensional time series analysis engine to enable the system to fully perceive the development trend of security incidents from historical and future dimensions. The present invention can predict future attacks by using AI models, which enables the system to take preventive measures in advance and significantly improve the defense level of the entire security situation.

[0037] Every security decision and process of the system of the present invention is transparent and visual. This transparency makes security operations easier for the security team to understand and trust. By providing a detailed display of the decision path, compliance and transparency requirements for monitoring within the enterprise are met, reducing legal and regulatory risks. The present invention uses standardized APIs and modular design, which can be easily integrated with the company's existing security facilities (such as SIEM and IAM systems) to reduce deployment difficulty. The system architecture of the present invention can allow new functional modules to be quickly expanded to adapt to changing business needs.

[0038] The system of the present invention can be equipped with an embedded continuous learning platform to allow the system to autonomously learn from new threat intelligence and security incidents, and automatically optimize its detection capabilities and strategies. Through continuous updating and optimization, the system can always stay at the forefront of technology and strategy to prevent the ever-changing threat evolution. The built-in ethical risk assessor of the present invention ensures that user privacy and data protection regulations such as GDPR and CCPA are not accidentally violated during detection activities.

[0039] 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 aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and specification of the present invention.

Claims

1. A network security detection system, comprising an intention perception layer, characterized in that: The intention perception layer is connected to a dynamic immunity module, the dynamic immunity module is connected to a time management and strategy layer, the time management and strategy layer is connected to an ethics and transparent decision engine, and the ethics and transparent decision engine is connected to a collection layer and an expansion layer; The intent perception layer includes a user intent analysis module and intent anomaly spectrum management, the dynamic immunity module includes an adaptive immunity center and a threat isolation and analysis sandbox, the time management and strategy layer includes a multi-dimensional timing analysis engine and a time evolution supervision system, and the ethics and transparent decision-making engine includes a transparency display module and an ethical risk assessor.

2. A network security detection system according to claim 1, characterized in that: The user intention analysis module is used to: Use deep learning and natural language processing technologies to analyze data from user behavior logs, system commands, and network traffic to identify the potential intentions of user operations in real time; The intent exception spectrum management is used to: Build and maintain user and system intent genealogies, provide automated comparative analysis, and update in real time through knowledge graphs.

3. A network security detection system according to claim 1, characterized in that: The adaptive immune center is used to: Simulate the memory and response mechanisms of the immune system and build complex attack recognition and response strategies based on historical data; The Threat Isolation and Analysis Sandbox is used to: Through appropriate induction strategies, suspicious activities are brought into a secure sandbox and behavioral analysis is performed in an isolated environment.

4. A network security detection system according to claim 1, characterized in that: The multi-dimensional timing analysis engine is used to: Integrate retrospective analysis of historical events with predictive modeling of future threats, and provide a basis for security decision-making through AI-driven analysis; The event evolution supervision system is used to: Monitor the evolution trend of security incidents in real time, maintain a clear insight into the incident situation, and provide timely strategy adjustment suggestions.

5. A network security detection system according to claim 1, characterized in that: The transparency display module is used for: Provide a visual interface for security incident handling, transparent decision-making path, and ensure transparency and explainability of the system operation process; The ethical risk assessor is used to: Built-in compliance analysis ensures that detection and response mechanisms operate in compliance with privacy and ethical standards, mitigating potential legal and ethical risks.

6. A network security detection system according to claim 1, characterized in that: The collection layer and the extension layer are used to: The open API interface of the API and integration framework supports integration with existing security tools within the enterprise, including SIEM and IAM tools, to improve system compatibility and scalability.

Images