9052 results about "Network security" patented technology

A system and method for an AI-controlled sensor network for threat mapping and characterization. The system deploys a network of honeypots and sensors across various geographic locations and network segments, collecting and aggregating data on network traffic and potential threats. An AI orchestrator analyzes this data using advanced machine learning models, generating dynamic honeypot profiles and a comprehensive threat landscape. The system can adapt in real-time to emerging threats, optimize resource allocation, and provide actionable intelligence. By correlating data across multiple points, the system offers enhanced threat detection capabilities and proactive cybersecurity measures, surpassing traditional security information and event management (SIEM) tools.

The present disclosure generally relates to a two-stage system for detecting malicious nodes in Wireless Sensor Networks (WSNs), enhancing network security and resilience. The system employs a distributed approach, leveraging Cluster Heads (CHs) and a central server for efficient and accurate detection. Initially, sensor nodes are monitored for comprehensive node and network metrics, statistically ranked by significance in identifying malicious behavior. CHs perform a resource-aware first-stage detection based on their resource weight, filtering potential threats locally. Results are then aggregated at a server for a second-stage analysis using a hybrid Machine Learning (ML) and Deep Learning (DL) approach. This advanced analysis, combined with statistically relevant metrics, significantly improves detection accuracy. By integrating resource-conscious CH operation with powerful server-side ML / DL, this system offers a scalable, energy-efficient, and highly effective solution for securing WSNs against malicious node attacks, surpassing traditional detection methods in both speed and precision.

The invention provides a network security penetration detection method and system based on artificial intelligence, and the method comprises the steps: firstly obtaining real-time network flow data containing multi-dimensional communication behavior records and protocol metadata of a target network system, and then extracting network behavior sequence features capable of describing communication behavior time relevance and a protocol interaction mode from the network flow data; inputting the network behavior sequence features into a pre-trained multi-modal threat prediction model, generating a potential attack vector prediction result containing an attack intention label, penetration path probability distribution and a threat level score, and dynamically verifying the potential attack vector prediction result based on a threat knowledge base, and finally, generating a self-adaptive defense strategy according to the penetration threat detection result, triggering defense interface deployment of a target network system, updating attack mode characteristics and a defense rule set of a threat knowledge base, and realizing intelligent and efficient network security penetration detection and defense.

An AI-based Cybersecurity System and Method enable real-time detection, analysis, and mitigation of cyber threats within computing networks using adaptive artificial intelligence. The system continuously monitors network traffic, extracts behavioral and contextual attributes, and applies deep learning-based inference to identify anomalous activities indicating security breaches. The method integrates several computational units, including a network monitoring unit, feature extraction unit, artificial intelligence processor, contextual reasoning processor, and decision synthesis unit, to compute a composite risk index quantifying threat likelihood and severity. A classification processor categorizes detected threats into types such as ransomware, phishing, or unauthorized access, while a mitigation control processor initiates automated response actions to isolate compromised nodes and restore network integrity. An adaptive learning processor updates AI models using feedback from confirmed incidents. This provides a scalable, self-evolving cybersecurity framework that minimizes human intervention and enhances resilience against dynamic and zero-day threats.

The invention provides an analyzable anti-attack network security method and system based on an AI unified model. The method comprises the following steps: S1, carrying out attack source tracing and attack mode identification on an input data stream; s2, performing protocol structure analysis and grammar element extraction on the input data stream in a grammar verification layer, and starting a grammar rule matching process to obtain a grammar exception perception set; s3, fusing attack vector information on the basis of a grammar anomaly perception set in a semantic analysis layer, constructing a semantic relation graph, and outputting a semantic risk vector; s4, taking the semantic risk vector as input, combining a business scene, resource constraint and strategy preference, modeling a defense target, and outputting an optimal response path; and S5, forming a model evolution path based on local feedback and global collaboration. Through a three-layer full-information analysis mechanism and behavior feedback driving, interpretable recognition of attack intentions and collaborative optimization of defense paths are realized, attack recognition is comprehensive, response decision is accurate, and strategy evolution is controllable.

The invention relates to the technical field of network security, in particular to a network security big data state evaluation method based on pattern recognition, which comprises the following steps of: extracting multi-modal features from a network flow log, a system event log, a host behavior log and threat intelligence data, generating a feature matrix, performing feature dimensionality reduction by adopting an auto-encoding network, and obtaining a network security big data state evaluation result; carrying out attack behavior classification and abnormal mode identification in combination with unsupervised clustering and a graph neural network; constructing an attack transition probability matrix based on a Markov model; forming a time sequence attack chain; predicting an attack development trend; and a dynamic protection instruction is issued to the safety equipment. According to the method, the unknown attack detection capability can be improved, the time sequence attack traceability is enhanced, the security situation assessment is optimized, and the method is suitable for security situation awareness in cloud computing, industrial internet and large-scale network environments.

The invention provides a network security protection method and system applied to regional digital and intelligent asset businesses, and the method comprises the steps: collecting asset data flows of a plurality of asset business nodes in a target region, carrying out the threat feature extraction of the asset data flows based on a preset threat knowledge graph, and obtaining a threat feature extraction result; generating a dynamic threat feature vector corresponding to the asset service node; inputting the dynamic threat feature vector into a pre-trained dynamic protection model, and outputting a real-time protection strategy adaptive to the asset service node through a multi-layer decision network in the dynamic protection model; performing strategy execution on the network flow of the asset service node based on the real-time protection strategy, generating a strategy execution result and feeding back the strategy execution result to the dynamic protection model; and performing adaptive optimization on decision parameters of the dynamic protection model according to a strategy execution result, and generating an updated dynamic protection model for a protection decision of a next round of asset business nodes.

The invention provides an industrial control network security advanced threat detection system fused with artificial intelligence. The system comprises a multi-source data acquisition module, an intelligent analysis engine, a threat detection module, a dynamic defense module and a self-evolution learning system which perform data interaction in sequence. The industrial control network security advanced threat detection system fused with artificial intelligence realizes collaborative decision-making among the modules through a dynamic knowledge graph. Through multi-source data fusion, dynamic knowledge graph and lightweight model design, the core problems of protocol analysis, threat association, defense collaboration and model adaptability in the industrial control network security field are solved, and a full-stack protection system covering'perception-analysis-decision-response-evolution 'is constructed. The deep analysis capability of an industrial protocol is improved, the dynamic threat association analysis is broken through, the agility of a defense strategy is enhanced, and the feasibility of continuous optimization of a model is improved, so that a systematic solution is provided for advanced threat defense in a complex industrial control environment.

The invention provides a network security defense method and system based on intrusion modeling trapping, and the method comprises the steps: constructing a dynamic virtual asset atlas based on a real network topology, generating a virtual node and an interaction link through protocol fingerprint confusion, implanting an interface deformation rule in combination with a session context, and constructing a protocol stack controllable dynamic trapping interface. Extracting topology correlation degree parameters, triggering node recombination and attack path shunting rules, using a protocol stack mirror image tunnel confusion instruction flow path, constructing a dynamic attack path confusion network, and directionally transmitting an intrusion instruction to a trapping interface protocol stack. And when the parameters reach a threshold value, a node recombination strategy and an interface deformation rule are updated, a dynamic attack and defense game mechanism is formed, the attack behavior induction and feature capture capability is enhanced, and active interference and accurate traceability of network intrusion behaviors are realized. According to the technical scheme provided by the embodiment of the invention, active shunting of the attack path and accurate induction of the trapping interface can be realized, and the attack detection rate is improved.

The invention discloses a network threat multi-modal detection method based on a large model, and belongs to the technical field of network security, and the method comprises the steps: collecting three types of heterogeneous data of NetFlow flow of a network layer, a system call chain sequence of a host layer and a protocol load of an application layer, and carrying out the desensitization processing and feature coding to generate a unified tensor format; the method comprises the following steps: through network security threat intelligence and MITRE ATTamp; performing supervision fine tuning on the large model by using a CK attack chain sample, and constructing a network threat identification special model; cross-device behavior characteristics are extracted through a model self-attention mechanism, and a dynamic behavior map is constructed; and finally, comprehensively evaluating an attack mode matching degree, a node vulnerability mean value, historical alarm association and an attack path risk by adopting a weighted fusion algorithm, and triggering a high-confidence alarm when a comprehensive score exceeds 0.8. According to the method, through multi-modal data fusion and dynamic graph analysis, the detection precision and response efficiency of the complex attack chain are improved.

The disclosed embodiments provide systems and methods for continuous exposure and attack surface management using a data fabric. Data from multiple heterogeneous cybersecurity sources, including vulnerability scanners, threat intelligence, cloud security tools, and endpoint monitoring systems, is ingested and integrated into a semantically harmonized representation, such as a security knowledge graph. This unified data model normalizes, correlates, and contextualizes diverse cybersecurity information, enabling comprehensive and real-time assessment of an organization's cybersecurity risk posture. Automated workflows trigger proactive remediation actions based on dynamically calculated exposure metrics. Additional embodiments leverage the same data fabric architecture to support specialized cybersecurity use cases, including unified vulnerability management (UVM), cyber asset attack surface management (CAASM), continuous threat exposure management (CTEM), and asset exposure management (AEM).

The invention relates to a risk management and control method and system based on real-time behavior analysis, and the method comprises the steps: carrying out the structural processing of multi-source behavior data through lightweight protocol decoding and behavior label embedding, and constructing an original behavior data set of a user and an entity; extracting multi-dimensional behavior characteristics by using a sliding window analysis and sparse representation mechanism, and constructing a user behavior graph by combining graph embedding learning; constructing a time-sensitive behavior trend model through streaming modeling and an incremental learning strategy, identifying an abnormal evolution trajectory in real time, and introducing a dynamic risk threshold regulation and control mechanism; adopting a high-throughput flow data processing and fast similarity matching algorithm to construct a fusion discrimination model, giving risk levels to abnormal behaviors and classifying the abnormal behaviors; and finally, performing closed-loop optimization in combination with a historical treatment effect. The system has the advantages of high real-time performance, high calculation efficiency, adaptability to complex network environments and the like, and the network security protection capability can be effectively improved.

The invention relates to the technical field of network security, in particular to a network security dynamic early warning method and system based on a knowledge graph, and the method comprises the following steps: collecting real-time traffic data of a network, analyzing a data sequence by recognizing the characteristics of a state sequence and using a long and short-term memory network, and recognizing a behavior mode; and constructing a state time sequence model. According to the method, structured analysis of network behaviors is realized through time dependence analysis, refined mining of incidence relations between events, determination of a trigger chain and time relation of potential threats and reasoning process of a knowledge graph, abnormal behaviors and attack behaviors can be identified more accurately, global analysis capability of network threats is enhanced, and the network threats can be analyzed more accurately. The network environment change can be adapted in real time by dynamically adjusting the early warning threshold value and the early warning parameter, a more flexible and efficient threat response mechanism is realized, the combination of the early warning signal and the security event response is updated in real time, and the response speed and the processing efficiency of the security problem are optimized.

The invention relates to the technical field of computer network security and data transmission, and discloses a computer network security data transmission method and device, and the method comprises the steps: obtaining real-time threat intelligence data, a real-time packet loss rate and a real-time transmission performance parameter, carrying out the encryption, and obtaining encrypted fragmented data; inputting the real-time transmission performance parameters into a path performance evaluation model to obtain a path performance score; sorting according to the path performance score to obtain a transmission path set; according to the transmission path set, matching to obtain a sequence corresponding relation; generating complete transmission data according to the sequence corresponding relation; performing anti-attack optimization according to the real-time transmission performance parameters to obtain a link connection strategy; analyzing the link quality according to the real-time packet loss rate to obtain a transmission link quality score; and performing dynamic adjustment according to the transmission link quality score, the link connection strategy and the complete transmission data to obtain real-time transmission data. According to the method, real-time and complete computer network security data transmission can be realized.

The invention discloses a cloud desktop security access control method based on a zero-trust architecture, belongs to the technical field of network security, and is used for solving the problems of difficulty in hidden attack detection, cross-cloud attack chain breakage and conflict between security control and service continuity in a multi-cloud environment. Firstly, user identity attributes, session metadata and service call logs are aggregated, an identity-resource-behavior triple dynamic graph is constructed, cross-session association features are extracted, and a multi-dimensional behavior baseline is generated. And quantifying the access deviation degree based on the behavior baseline, triggering sensitive operation traceability analysis, constructing a time sequence risk propagation model, identifying latent attack features, predicting a penetration path and outputting a risk propagation coefficient. And finally, dynamically generating a process-level micro-isolation strategy according to a risk result, gradually adjusting the authority through a nonlinear authority attenuation function, inserting a secondary authentication node when unexpected resource jump is detected, reconstructing a communication white list, and realizing collaborative optimization of security protection and service continuity.

Methods and systems for dynamic shielding in network security is disclosed. The method comprises analyzing real-time network traffic to detect a potential threat and assessing a severity level for each detected threat. Vulnerabilities of the devices in the network and the communications network to which each device is connected can be determined. A risk score can be generated for the threat based on the severity level of the threat and the vulnerabilities in the network. A response strategy can be implanted based on the risk score. The response strategy can include predefined actions for risk scores below a threshold, and maximum protective measures for risk scores above the threshold. The methods and systems can employ machine learning algorithms for threat detection and classification and utilize adaptive learning to refine future response strategies based on historical data of network interactions and system responses.

The invention relates to the technical field of network security, in particular to a computer network information security monitoring method, system and device and a medium, and the method comprises the steps: obtaining encrypted traffic data and application log data in a network environment, and constructing a space-time associated original data set based on the encrypted traffic data and the application log data; performing protocol analysis on the original data set with the time-space association to generate a protocol fingerprint feature vector; inputting the protocol fingerprint feature vector and the application log data into a preset heterogeneous multi-modal analysis model, and obtaining a multi-dimensional security situation assessment result containing a threat level and an attack path; and based on the multi-dimensional security situation assessment result, generating a dynamic defense strategy instruction set through a reinforcement learning algorithm, and issuing a strategy instruction to a network execution node in real time. The method and the device have the effects of realizing real-time accurate detection of encryption threats and constructing a dynamic defense system with balanced security and efficiency.

The invention discloses a lightweight malicious network traffic detection method based on heterogeneous modal feature fusion, and mainly solves the problems of low feature extraction efficiency and insufficient single modal feature representation of the existing method. Comprising the following steps: acquiring and optimizing a network flow data set, preprocessing the network flow data set, and extracting and generating spatial feature, time sequence feature and behavior pattern feature vectors; an improved self-attention mechanism network is constructed, a lightweight heterogeneous modal feature fusion model LMF is designed, multi-modal feature deep fusion is performed through dynamic weight distribution, a lightweight classification model is trained, and the model is utilized to detect network malicious traffic. Through the lightweight heterogeneous modal feature fusion model LMF and in combination with a dynamic weight distribution mechanism, spatial distribution, time sequence dependence and behavior semantic information of network traffic are deeply mined, data processing efficiency, malicious traffic detection accuracy and system robustness are improved, and the method is suitable for efficient malicious traffic identification and defense in the field of network security.

A distributed intelligent authentication method based on dynamic multi-modal fusion relates to the field of network security, and adopts an alliance chain + DAG hybrid block chain architecture, combines a threshold signature to realize secret key fragment management, and switches among PBFT, Raft and probabilistic algorithms through a dynamic consensus mechanism to improve authentication efficiency. The multi-mode authentication module is based on a dynamic weight distribution algorithm, integrates biological characteristics, behavior analysis, equipment fingerprints and environmental factors, and combines an LSTM-GAN model and a quantum random number driven challenge-response mechanism to realize zero-trust verification under environmental perception. The session management module generates a session key by using a chaotic mapping algorithm. In the aspect of privacy protection, CKKS homomorphic encryption, zero-knowledge proof and attribute-based encryption are fused. According to the method, the block chain technology, the secure multi-party computing technology, the machine learning technology and the quantum cryptography technology are fused, and a high-performance, high-security and strong-privacy-protection distributed authentication solution is provided.

The invention relates to the technical field of network security, in particular to a network security analysis method and system based on big data. Comprising the following steps: collecting related multi-source heterogeneous data of a network, and carrying out standardized processing such as cleaning and de-noising; network analysis is carried out based on the preprocessed data, network traffic is analyzed in real time by using machine learning and deep learning algorithms, and abnormal conditions are detected; constructing a risk prediction model according to a network analysis result and related information, and predicting a future network security risk level; if the risk level exceeds the threshold value, determining a security event source and a responsibility subject through data tracing; and finally, generating a safety response strategy according to risk prediction and data traceability results, and performing disposal. The corresponding system covers the modules of data acquisition, preprocessing, network analysis, risk prediction, data tracing, security response and disposal and the like, and all the modules work cooperatively to form a complete network security analysis and guarantee system, so that the stable operation of the network system is guaranteed.

The invention relates to the technical field of integration of artificial intelligence security and network security, and discloses an AI dynamic security transmission system based on an SASE framework, which realizes security access control based on AI dynamic identity verification through an SASE integration access module, acquires and predicts network performance change in real time by using a network state sensing module, and transmits the network performance change to a network server. Equipment, environment and data content are subjected to multi-dimensional analysis by means of a security risk assessment module, a quantitative risk score is generated, and a transmission protocol, parameters and encryption strength are dynamically adjusted according to a network state and the risk score by means of a dynamic transmission optimization module and a self-adaptive encryption module; the problem that safety protection and transmission efficiency are difficult to cooperate in a traditional architecture is effectively solved, low-delay and high-reliability data transmission service can be provided for AI application in a complex network environment, meanwhile, self-adaptive dynamic protection of the whole data transmission process is achieved, and data safety is comprehensively guaranteed.

The invention provides a large-scale network node scene construction method and system based on a network target range, and relates to the technical field of network security, and the method comprises the steps: dividing network nodes into a plurality of attack and defense levels through calculating the vulnerability association degree between the nodes, and screening a target network node template based on a vulnerability chain-type propagation coefficient. Secondly, optimizing a network topology structure by adopting a graph theory clustering algorithm, and constructing a connection relation between nodes based on service affinity; and finally, according to the resource utilization rate index, constructing a node operation environment, deploying network services, implanting a vulnerability program and carrying out anomaly detection and repair, and finally generating a large-scale network node scene meeting resource optimization scheme constraints. According to the method, the large-scale network target range with a complex topological structure and a real vulnerability environment can be automatically constructed, and the efficiency and authenticity of network security attack and defense drilling are improved.

The present invention discloses a post-quantum key negotiation system and method based on the IKE protocol, which relates to the field of network security, especially the design and implementation of key exchange and identity authentication protocols. The present invention provides security for data protection and identity authentication by combining the IKE protocol scheme with the post-quantum cryptographic algorithm; by introducing post-quantum cryptographic algorithms such as ML‑KEM, ML‑DSA and FALCON, the quantum resistance of key exchange and identity authentication is enhanced, and the flexibility and adaptability of the protocol are also improved; in addition, the dynamic algorithm selection mechanism adopted by the present invention enables users to optimize security performance according to specific needs, thereby improving the overall efficiency of the system while ensuring information security.

The invention discloses a network security verification method and system for a security system, and belongs to the technical field of network security, and the method comprises the steps: obtaining a biological feature recognition request triggered by a user on terminal equipment, synchronously collecting dynamic behavior track data, and generating a behavior feature code; inputting physiological feature data and behavior feature codes in the biological feature recognition request into a feature verification model, when an output biological behavior correlation index is lower than a first preset threshold value, triggering a dynamic verification strategy adjustment instruction, generating a multi-dimensional verification superposition strategy, and executing at least two verification operations in the multi-dimensional verification superposition strategy, the verification result is input into the distributed time synchronization node to generate an encryption token; and when the synchronous matching degree of the encryption token among different nodes exceeds a second preset threshold value, outputting a security verification passing instruction. According to the method, multi-modal feature fusion, dynamic hierarchical verification and time-space encryption technologies are adopted, replay attacks are prevented, an adaptive defense system is constructed, and reliable authentication of a high-security network scene is ensured.

The invention provides an industrial control network security service security guarantee system based on behavior analysis, which belongs to the technical field of industrial control network security, and comprises a multi-source data fusion acquisition module, a dynamic behavior modeling engine, a federal learning analysis cluster, an attack chain prediction module, a self-adaptive protection strategy executor and a model evolution feedback ring, wherein the multi-source data fusion acquisition module synchronously acquires industrial control network flow (including OPC UA / Modbus / DNP3 protocol analysis), equipment operation logs, user operation behavior fingerprints and physical interface state data, and the physical interface state data comprises electrical characteristic fluctuation monitoring of USB / network interfaces. According to the scheme, through multi-technology fusion and closed-loop design, the problems of static performance, single-dimension analysis defects and response lag of a traditional industrial control security scheme are effectively solved, a comprehensive protection system with dynamic modeling, intelligent decision making, privacy protection and continuous optimization is constructed, and the security and service reliability of an industrial control network are remarkably improved.

The invention discloses an APT attack chain reconstruction method based on a knowledge graph and a graph neural network, and belongs to the technical field of network security. Multi-source heterogeneous attack clues are subjected to advanced analysis by introducing a large language model, and time sequence enhanced knowledge graph representation and the deep learning ability of a graph convolution attention network are combined, so that the multi-source heterogeneous attack clues are reconstructed. The method promotes the efficient completion and dynamic reconstruction of the attack chain, and solves the problems that in the prior art, due to the problems of data sparsity, relation complexity, time sequence characteristics and the like, obvious limitation exists in the aspects of attack chain completion and inference, and a traditional method lacks the deep learning ability for the implicit relation in the attack chain. And thus, the problem of insufficient inference capability on unknown attack behaviors is solved.

The invention provides a network security protection method and system based on information fusion, and the method comprises the steps: firstly obtaining a multi-source heterogeneous data set, which comprises a traffic interaction data unit, an equipment log data unit and a protocol analysis data unit, of a target network, then carrying out the time sequence correlation analysis of the traffic interaction data unit, and generating a traffic behavior feature set; and executing state mode analysis on the equipment log data unit to generate an equipment operation feature set, and executing semantic recognition on the protocol analysis data unit to generate a protocol analysis feature set. Then, on the basis of a multi-dimensional feature fusion rule, cross-dimensional feature fusion processing is carried out on the feature set, and a network situation feature set is generated; and calling a threat identification model to carry out threat identification on the set, generating a threat identification result set containing threat type identifiers and influence range parameters, and finally generating a security response strategy set according to the threat identification result and issuing the security response strategy set to a security control node to execute protection operation, thereby effectively improving the network security protection capability.

The invention relates to a graph-based network security event modeling method and system, and the method comprises the steps: obtaining topological data and security policy information of a network where network security equipment is located, and carrying out the hierarchical construction of a knowledge graph, and obtaining a multi-layer security graph; acquiring real-time monitoring data of the network security equipment, and performing graph adversarial learning association with the multilayer security graph to obtain a dynamic evolution graph sequence; obtaining alarm data of the network security device, and performing anomaly detection on the multilayer security map to obtain an anomaly propagation situation; performing security assessment construction on the dynamic evolution diagram sequence and the abnormal propagation situation to obtain an initial security assessment scheme; performing alarm identification and attack link prediction on the alarm data to obtain a link prediction result; and performing evaluation and prediction on the initial security evaluation scheme and the link prediction result to obtain a security situation evaluation strategy. According to the invention, the security condition of the current network can be evaluated more accurately.

The invention discloses a network security threat research and judgment method, system and device and a storage medium, and the method comprises the following steps: S1, obtaining network traffic, terminal logs, application program interface calling records and threat intelligence data in real time, carrying out the standardized cleaning and format conversion of the data, and building a unified data lake; s2, matching, identifying and determining threats through a preset known threat feature library, constructing a normal behavior baseline by using an unsupervised learning algorithm, and marking suspicious events deviating from the baseline; s3, for the suspicious event marked in the step S2, mining a potential attack path and an attack intention by combining knowledge graph technology associated asset information, a historical attack chain and a homologous IP address; and S4, based on the attack success probability, the influence asset importance and the diffusion speed, calculating a threat level by adopting a fuzzy comprehensive evaluation model, and generating a research and judgment report containing disposal suggestions.

The invention discloses a network security analysis early warning system based on artificial intelligence, and the system comprises a data collection layer which captures full flow based on DPI, aggregates firewall logs, terminal behaviors and threat intelligence, and constructs a structured data pool; through TLS fingerprint identification of AI driving, the encrypted traffic is penetrated, and a sampling strategy is dynamically adjusted in combination with reinforcement learning. The intelligent analysis layer is used for carrying out cross validation on known threats and abnormal behaviors; the time sequence CNN extracts encrypted traffic features, and a novel threat detector is rapidly generated by using historical attack fragments in combination with a meta-learning framework; sHAP value driving dynamic feature selection and optimization feature vector input; the decision-making early warning layer is used for fusing multi-source features through a Bayesian network and generating 0-100 score risk scores; a self-adaptive threshold module is combined to adjust a score threshold in real time, and a high-risk event is pushed; the collaborative response layer is used for triggering a preset decision tree, deploying a GAN dynamic honeypot to trap an attacker and reversely tracing; the Neo4j visually restores the attack path, and blocking is executed after the threat is confirmed by a progressive response mechanism.