1465 results about "Complex network" patented technology

A dynamic evaluation method for an extreme rainstorm waterlogging disaster risk for disaster prevention and reduction. The method comprises: investigating and surveying urban system data and disaster prevention and reduction data, using GIS technology to divide disaster-bearing objects into refined risk units on the scale of urban buildings and road networks, and determining the spatial distribution of the disaster-bearing objects; on the basis of an extreme rainstorm waterlogging scene, simulating the disaster influence of a dynamic change process of a flood ponding depth on the disaster-bearing objects; developing refined dynamic evaluation on a waterlogging risk by combining the two methods of waterlogging process simulation and an indicator system; using a spatial complex network and a shortest path plan to calculate a traffic capacity and emergency service accessibility of a road network system; and on this basis, taking into comprehensive consideration the rational allocation of disaster prevention emergency drainage and emergency rescue services to a high-risk area, and proposing dynamic evaluation technology for a waterlogging risk that integrates a disaster evolution process and a disaster prevention response process, and ultimately realizing the dynamic evaluation of the waterlogging risk of each disaster-bearing unit during the waterlogging disaster evolution.

The invention provides a flow analysis and threat detection method and device based on machine learning, and the method comprises the steps: collecting a real-time flow data package of a target network environment, carrying out the protocol analysis and session recombination, and generating a real-time flow feature data set containing multi-dimensional flow features; loading a pre-trained multi-level threat classification model, inputting the real-time traffic feature data set into a feature extraction layer of the model, carrying out normalized coding on traffic features of corresponding dimensions through feature coding channels, generating a real-time feature vector sequence, inputting the real-time feature vector sequence into a primary classifier of the model, and classifying the real-time traffic features according to the real-time feature vector sequence; and performing abnormal probability calculation and cluster division on the real-time feature vector sequence through a mixed detection unit, outputting a primary threat tag and an abnormal confidence coefficient corresponding to each real-time feature vector, inputting the primary threat tag and the abnormal confidence coefficient into an aggregation classifier, performing dynamic weighted aggregation, and generating a comprehensive threat score so as to judge whether a threat response strategy is triggered or not. According to the invention, the accuracy and timeliness of threat detection in a complex network environment can be improved.

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 a network information security protection method and system based on artificial intelligence dynamic defense. The method comprises the following steps: constructing a multi-level security policy library containing basic defense, scene defense and emergency response based on preprocessed network data features (including protocol features, behavior sequences and abnormal indexes); then, combining real-time threat intelligence and a network environment state, and utilizing a strategy combination optimization algorithm to generate a weight matrix reflecting strategy priorities and dependency relationships; dynamically extracting and fusing various defense strategies from a strategy library according to the weight matrix to form a customized defense scheme; and eliminating resource competition and logic contradiction between strategies through a conflict detection mechanism, and finally deploying a conflict-free optimization scheme to a target node. By adopting the method, collaborative deployment of multi-dimensional security policies can be realized, and the accuracy and real-time performance of security protection in a complex network environment are remarkably improved.

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 relates to a fuzzy testing method, device and equipment based on a large language model, and the method comprises the steps: analyzing and analyzing RFC standard documents of various network protocols through the large language model, and extracting protocol rules and constraint conditions; according to the protocol rule and the constraint condition, a large language model driving method is adopted to generate a test case conforming to protocol characteristics; and performing a differential test through the test case, and verifying the consistency between the actual software of the network protocol and the RFC standard document. According to the invention, the accuracy and security of implementation of the complex network protocol can be improved, and the test efficiency and coverage rate are optimized.

The invention discloses a message feature extraction method and related equipment, and relates to the technical field of feature extraction, and the method comprises the steps: obtaining original message data of target network flow and a corresponding physical layer signal waveform; analyzing the original message data based on a preset protocol layering rule, and determining a multi-layer protocol field set; determining a protocol layer feature vector based on the multi-layer protocol field set; determining a frequency domain feature vector based on the physical layer signal waveform; constructing a cross attention weight matrix based on the protocol layer feature vector and the frequency domain feature vector; and generating a target message feature based on the cross attention weight matrix. According to the method, through dual-mode deep fusion of protocol semantics and physical signals, the analysis capability of the features on encrypted traffic and protocol confusion attacks is enhanced, meanwhile, the feature robustness is improved through dynamic weight distribution and an anti-replay mechanism, and endogenous security features with high discrimination power are provided for complex network threat detection.

The invention relates to a network security situation awareness method and device for multi-source data fusion, equipment and a medium, and the method comprises the steps: respectively collecting kernel logs and network flow data, and carrying out the standardization processing to generate a communication data set; constructing a dynamic process link map, defining a communication type, frequency and data volume, and updating a topological relation in real time; a sliding time window is adopted to count communication time sequence characteristics, and abnormal signals deviating from normal distribution are screened in combination with a time sequence analysis technology; training a robustness classifier model through an adversarial sample enhancement technology, fusing a graph neural network to analyze a dependency relationship between processes, and filtering a false alarm signal; and the hidden channel is accurately identified and alarm information is generated in combination with a dynamic similarity threshold and a threat intelligence gain coefficient, so that the problems of insufficient multi-source data fusion, high false alarm rate of a static rule base, failure in detection of weak signals of the hidden channel and the like in a traditional method are solved. And the real-time perception and response capability of APT attacks in a complex network environment is improved.

The invention relates to the technical field of computer network security, and discloses a computer network security access control management method based on big data. The method comprises the following steps: constructing a network security situation knowledge graph, collecting a real-time access behavior sequence through a probe, and synchronizing the real-time access behavior sequence to the knowledge graph; simulating a network entity interaction state in the knowledge graph, and predicting a threat propagation path and a potential intrusion behavior; setting a dynamic access control strategy, constructing a multi-dimensional feature matrix in combination with a real-time access behavior sequence association influence degree and a strategy execution priority constraint condition, calculating a strategy conflict risk score by using a deep learning model, comparing with a preset threshold to judge whether a conflict exists or not, and if yes, reconstructing the strategy; and automatically executing access blocking, session termination and data encryption operations according to the reconstructed strategy, recording an execution log and security feedback data, and updating the knowledge graph in real time. According to the method, the dynamic property and the security of access control are improved, and security threats in a complex network environment can be effectively handled.

The invention relates to the technical field of traffic data processing, and discloses a traffic edge computing data security sharing method and system, and the method comprises the steps: collecting traffic edge computing network multi-source heterogeneous data, and carrying out the classification standardization processing to generate a structured data set; designing a dynamic data sharing security protocol based on a multi-party security computing protocol and a homomorphic encryption algorithm; verifying the authority of a requester in a multi-level manner by using an attribute-based access control model and a zero-knowledge proof mechanism, and generating a dynamic access token; storing data by adopting a fragmentation storage and redundancy encryption strategy, and recording storage information through a hash chain; and dynamically adjusting the encryption strength and the sharing strategy according to the network threat level and the data sensitivity. The method effectively guarantees safe sharing of traffic data, accurately controls access authority, improves storage and sharing efficiency, adapts to complex network environment changes, and provides powerful support for development of an intelligent traffic system.

The invention relates to the technical field of network security, in particular to a network security situation awareness and early warning method based on big data analysis, and the method comprises the following steps: collecting multi-modal security data, and carrying out the noise reduction and normalization processing; constructing a high-dimensional security feature vector by using feature engineering; establishing an anomaly detection model based on physical constraints and a deep residual network; a self-adaptive loss optimization strategy training model is adopted, so that the detection accuracy is improved; through cross-domain transfer learning, model adaptability is optimized, and unknown attacks can be detected; future attack trends are analyzed in combination with historical data, and defense strategies are dynamically adjusted. Through combination of deep learning and big data analysis, the accuracy, real-time performance and adaptive capability of network security situation awareness are improved, unknown attacks and variant attacks can be effectively detected, the false alarm rate is reduced, the security protection capability is enhanced, and the method is suitable for a security defense system in a complex network environment.

The embodiment of the invention provides a computer communication method and system based on the Internet of Things, and the method comprises the steps: constructing a multi-level system architecture, carrying out the preprocessing and feature extraction of an original data flow, recognizing the data characteristics through a time sequence analysis method, and constructing a dynamic data model; deploying a monitoring agent at a transmission node to collect network performance indexes in real time, and constructing a network quality evaluation model; for an incomplete sensor data flow, prior probability distribution is constructed based on a dynamic data model and a network quality grade, and an optimal estimation value of missing data is calculated by adopting a Bayesian reasoning framework and an iterative algorithm; establishing a mapping relation between a network state and an optimal parameter through reinforcement learning to realize self-adaptive adjustment and optimization; and grading the data according to reliability, extracting high-reliability data points as anchor points, designing an iterative refinement algorithm to realize information propagation, and fusing to obtain a complete sensor data stream. According to the method, the problems of poor data recovery accuracy, static parameter configuration and insufficient adaptability in a complex network environment are solved.

The invention relates to the technical field of network communication, and discloses an intelligent control method and system of a switch. The method comprises the following steps: receiving a heterogeneous data stream in a target network domain; performing data processing by using a preset flow feature aggregation model, and generating a flow dynamic pressure field distribution diagram and a protocol priority mapping matrix; constructing an adaptive scheduling threshold model, and generating a multi-target flow control instruction set; a cascade effect is simulated based on a network congestion chained conduction model, and collaborative routing parameters are optimized; and iteratively optimizing through a mixed integer programming framework, and outputting a network scheduling action sequence to a switch control engine. The system correspondingly comprises a data receiving module, a data processing module, an instruction generation module, a simulation optimization module and an instruction output module. According to the method, heterogeneous data can be effectively processed, congestion can be accurately predicted, flow control can be optimized, the overall performance of the network can be improved, and communication requirements in a complex network environment can be met.

The invention relates to the technical field of computer network management, and discloses an intelligent alarm preprocessing method of an adaptive rule engine, which comprises the following steps: firstly, acquiring real-time operation data of network equipment and a preset alarm baseline, and analyzing a historical alarm sequence through an association rule mining model to obtain the preset baseline; then inputting the data into an alarm decision model based on an event atlas analysis algorithm, processing the data by the model by using a multi-dimensional time window algorithm, calculating adaptive weight correction processing parameters in combination with node resource load parameters, and outputting strategy parameters; and finally, adjusting rule engine judgment logic according to parameters to realize alarm intelligent filtering and aggregation. In addition, a link emergency optimization step is provided. The method improves the accuracy and adaptive capability of alarm processing, and is suitable for a complex network environment.

The invention belongs to the technical field of power system security, discloses a load frequency control system attack detection method based on reinforcement learning, and aims to improve the recognition and defense capability of a power system on complex network attacks and overcome the defects of a traditional detection method in the aspects of attack sample generation, unknown attack recognition and system adaptability. According to the method, an attack agent based on a Markov decision process is constructed, and an improved reinforcement learning algorithm is adopted to generate a high-concealment confrontation sample; designing a bimodal detection architecture fusing LSTM supervised learning and auto-encoder unsupervised learning, and introducing an adaptive weight fusion mechanism to realize attack type identification and anomaly detection; and incremental learning and a parameter dynamic adjustment mechanism are combined, so that the detection model has continuous learning and evolution capabilities. The method can be applied to a power grid dispatching center or an intelligent micro-grid, real-time monitoring and attack defense of a load frequency control system are achieved, and the operation safety and robustness of a power system are remarkably improved.

The invention discloses a data slice recombination order-preserving method based on a Beidou short message, which relates to the technical field of data transmission and recombination optimization, and ensures the stability and low error rate of data in the transmission process by dynamically optimizing the size and quantity of slices according to channel bandwidth and delay through a self-adaptive slice adjustment mechanism. A unique serial number identifier and a first check code attached to each fragment are beneficial to identifying data under a complex channel condition, and errors and chaos in a recombination process are reduced. At a receiving end, a conflict analysis model is constructed by analyzing sequence offset and delay conditions of fragment arrival in real time, and a multi-layer correction instruction is provided according to an overall conflict rate and a time sequence consistency coefficient. The order-preserving mechanism ensures that the original order of the data can be maintained even in a complex network environment by preferentially processing the unlocked fragment and generating a retransmission instruction of the lost fragment. The combination of dynamic priority adjustment and a retransmission mechanism promotes the structure of the original data after recombination.

The invention relates to a visual safety risk early warning system for pseudo-classic architecture mixed structure construction, in particular to the field of safety risk early warning of pseudo-classic architecture construction, through multi-source data fusion and intelligent algorithm cooperation, the construction safety risk management and control capability is remarkably improved, the system collects mechanical and environmental data of key nodes of a high-support formwork body in real time, and the construction safety risk early warning capability is improved. Load distribution is dynamically updated in combination with the building information model, and a high-precision space-time label data stream is generated; predicting a short-term instability risk based on a two-channel neural network, constructing a complex network model to quantify a node failure linkage effect, and generating a visual risk propagation thermodynamic diagram; finally, a grading alarm strategy is triggered through a dynamic threshold value, augmented reality visual warning and equipment linkage control is achieved, the system breaks through the limitation of traditional manual monitoring, full-process closed-loop management of risk perception, prediction, positioning and response is achieved, the occurrence rate of safety accidents is effectively reduced, and the safety and reliability of construction of the complex structure of the pseudo-classic architecture are guaranteed.

The invention discloses an intelligent operation and maintenance method for modular data acquisition and analysis sewage treatment equipment, which comprises the following steps: designing a modular data acquisition unit, configuring an acquisition module with a standardized interface, and realizing quick access of multi-source equipment; protocol conversion, data compression and timestamp completion are completed by using an edge computing gateway, and stable data reporting under a complex network is realized by combining an MQTT protocol; a time sequence database is adopted to store and fuse OA / ERP system data, and an information island is broken; remote diagnosis, intelligent early warning and visual decision making are realized by means of a remote fault analysis interface, a user-defined rule engine and a configurable billboard; and operation and maintenance closed-loop management is achieved in cooperation with a workflow engine. The method effectively solves the problems of low data acquisition integration level, unstable communication, weak analysis capability and the like in traditional operation and maintenance, has the characteristics of modularization, intelligence, high efficiency and the like, remarkably improves the operation and maintenance management level of the sewage treatment equipment, reduces the operation and maintenance cost, and has a wide application prospect.

The invention relates to the technical field of Internet of Things data transmission, in particular to a mass data cross-security domain transmission high-low priority queue hierarchical scheduling method based on Internet of Things equipment, which comprises edge computing node classification, independent channel design, a dynamic weight distribution algorithm and a block chain encryption watermark technology. The timeliness is improved through a preemptive and compensation mechanism, and the reliability is enhanced in combination with queue degradation protection and a dynamic adjustment strategy. The method can effectively solve the problems that timeliness is insufficient and safety and efficiency are difficult to balance in a traditional method, the key data transmission efficiency and the system self-adaptive capacity are remarkably improved in a complex network environment, and the method has wide application prospects.

The invention discloses an intelligent management system applied to a convention and exhibition center, relates to the technical field of convention and exhibition intelligent management, and realizes unified and high-precision data input through protocol conversion and multi-mode denoising. Second-level alarm and topology reconstruction are supported in cooperation with anomaly detection and self-adaptive management on the edge side; the comprehensive interpretation precision of the passenger flow and the environment situation is improved by using a multi-modal fusion algorithm; then mapping the perception information into a virtual venue situation by means of a digital twinning and self-learning mechanism, and realizing continuous optimization of sensor calibration and algorithm weight; finally, multi-Agent complex network dynamics is introduced, interaction evolution of a large-scale crowd in an emergency scene is simulated, closed-loop management of active prediction and emergency layout is provided for intelligent exhibition, and leap-type improvement in multiple levels of monitoring precision, safety disposal, resource scheduling and the like is expected to be achieved so as to cope with exhibition layout adjustment.

The invention belongs to the technical field of network security, and particularly relates to an intelligent association and global situation risk monitoring method based on multi-source data, which comprises the following steps: acquiring a multi-source heterogeneous data set; the multi-source heterogeneous data set is preprocessed, and preprocessed multi-source data is obtained; obtaining an attack behavior association graph according to the multi-source data, and performing anomaly detection on the association graph by using a graph neural network to obtain an explicit attack link and a potential attack link; obtaining a network security situation dynamic graph based on the explicit attack link and the potential attack link; and generating a risk assessment report according to the network security situation dynamic graph, triggering a corresponding security policy, and performing risk monitoring according to the security policy. According to the method, the accuracy and response speed of threat detection are remarkably improved, the global network security situation awareness capability is enhanced, and an intelligent solution is provided for security protection in a complex network environment.

The embodiment of the invention provides a security authentication method, device and system for a test platform, and a storage medium, and relates to the technical field of network security. The method comprises the following steps: performing fingerprint acquisition on virtual and real equipment to generate a unique identifier, dynamically slicing based on a software defined network to form an isolated test environment, and injecting a zero-trust access strategy associated with the fingerprint; and based on the zero-trust access policy and the device fingerprint, allocating a minimum permission through an attribute-based access control model, continuously evaluating device behaviors to calculate a dynamic trust score, and adaptively updating the permission according to the trust score. By constructing a full-process security certification system and through multi-dimensional mechanism cooperation, dynamic risk perception and timely strategy adjustment, fine-grained control of virtual and real equipment access in an industrial control scene is realized, the dynamic adaptability and risk resistance of a test platform in a complex network environment are improved, and the test efficiency is improved. Equipment access safety, authority distribution minimization and behavior monitoring continuity are ensured, and the safety risk of the industrial control system is reduced.

The invention discloses a non-direct connection-oriented data routing method, which comprises the following steps of: S1, acquiring topological structures, link bandwidths, time delays, loads and energy consumption states of nodes in a network in real time, and constructing a time sequence dynamic matrix of the nodes and links; s2, on the basis of the dynamic matrix, adopting a neural network adaptive enhanced ant colony optimization method to generate alternative paths; s3, generating a grey wolf optimization algorithm initial population by using the alternative paths, and constructing a multi-dimensional composite fitness function; s4, according to the fitness function, driving the grey wolf optimization algorithm to perform multi-scale iteration to update the path; s5, topology and node state prediction is carried out based on the dominant path, and the prediction path is optimized in advance; and S6, issuing the optimal path and the alternative path at the same time, carrying out data parallel forwarding, and driving a neural enhanced ant colony optimization algorithm to update online. The method improves the network path selection efficiency and the resource utilization rate, and is suitable for data routing in a complex network environment.

The invention discloses an AI-based network security event tracing method, system and device and a medium, and the method specifically comprises the steps: constructing a network entity association graph based on a multi-modal data set, mining the implicit association between entities through a graph convolutional network, recognizing an APT attack chain, and obtaining graph feature data; based on the multi-modal data set, an LSTM-Transform hybrid model is adopted to analyze time sequence characteristics of network traffic, slow penetration and low-frequency detection behaviors are detected, and time sequence characteristic data are obtained; based on the graph feature data and the time sequence feature data, high-value features are screened through a genetic algorithm, and cross-modal combination features are generated by using a depth auto-encoder; based on cross-modal combination features, a network environment digital twin is constructed, an attack diffusion path is simulated, and a service influence range is quantified. According to the method, accurate tracing of the network security event is realized, and the detection and tracking capabilities of complex network attacks and the intelligent level of a response strategy are comprehensively improved.

The invention relates to the technical field of artificial intelligence, can be applied to the fields of finance, medical treatment and the like, and provides a network fault processing method based on artificial intelligence, and the method comprises the following steps: carrying out the trend prediction and anomaly detection through a machine learning model based on real-time network performance indexes and historical alarm data, and obtaining a network fault detection result; generating an alarm set containing fault type inference; performing association analysis on the alarm set and network topology and change records, constructing a fault propagation map, and outputting a root cause node list sorted according to probability; generating an equipment parameterized repair script according to the root cause node list; executing the repair script and monitoring an execution state in real time to obtain a repair result; and verifying the repair result. Compared with the prior art, the method has the advantages that the end-to-end fault processing period is remarkably shortened, the manual intervention requirement is reduced, and the service continuity is guaranteed in a complex network environment.

The invention discloses an AI-based enterprise benefit policy intelligent matching and declaration auxiliary system, and relates to the technical field of policy declaration auxiliary systems. The data collection module collects policy and enterprise data in a multi-modal fusion manner, and dynamically updates according to activeness; the preprocessing module performs cleaning through a high-performance cluster, analyzes a policy through sentiment analysis and performs AES encryption; the feature extraction module uses GAN enhancement and cognitive calculation mining to update a knowledge graph; the matching calculation module calculates the matching degree in combination with a complex network, and the declaration auxiliary module demonstrates the process through VR and AR and answers questions through intelligent customer service. The risk assessment module assesses the risk by using a block chain and the like; the user interaction module supports a brain-computer interface and the like and adaptively adjusts the interface; and the data updating and feedback module records feedback by means of the block chain and optimizes a prediction mechanism. The system is advantaged in that multi-channel accurate acquisition is facilitated, enterprise matching policy bonus is facilitated, a declaration link is simplified, the cost is reduced, data authenticity is guaranteed, cooperation is promoted, continuous optimization is carried out according to feedback, and enterprise benefit policy landing is promoted.

The invention discloses a multidirectional frame audio stream transmission method, device, equipment and medium, and the method is realized through cooperation of a transmitting end and a receiving end: the transmitting end cuts an original audio stream into independent audio frames, gives priority identifiers to the independent audio frames, and determines redundant coding parameters and transmission paths for different priority frames in combination with a predefined static strategy; generating a data packet containing an original data block and a redundant data block, and sending the data packet through at least one network path; and a receiving end caches the multi-path data packet, recovers lost data by using redundant data blocks to recombine a complete audio frame, and executes error concealment processing on the frame which cannot be recombined to generate a replacement frame. According to the method, based on a multi-path parallel transmission, forward error correction (FEC) redundancy mechanism and a cost-aware static scheduling strategy, lossless forwarding and instantaneous recovery of audio streams are realized on the premise of not waiting for network feedback and avoiding inter-frame dependence, and high-quality real-time audio transmission service can still be provided in a complex network environment.

The invention discloses a method based on a large language model and ATTamp; the invention discloses a CK framework penetration test automation method and device, and the method comprises the steps: firstly carrying out the structural analysis of multi-source input information and tool output, and guaranteeing that key fields are not discarded; then combining a retrieval enhancement generation technology and a network security knowledge base to provide domain knowledge support for the large language model, so as to generate a model with ATTamp; a penetration test task tree marked by CK tactics, technologies and sub-technologies; on the basis, an optimal tool is automatically selected through a tool resource library and a multi-dimensional screening mechanism, an execution instruction is generated, and finally an execution result is returned to the input analysis module to form a self-adaptive optimization test closed loop. According to the method, semantic fidelity compression and standardization processing of long information can be realized aiming at the problems of large output format difference, more information redundancy and the like of different penetration testing tools, and efficient, explainable and auditory technical support can be provided for automatic penetration testing in a complex network environment.

The invention relates to the technical field of network security monitoring scheme design based on computing power, in particular to a network security monitoring method and system based on computing power and electronic equipment. The method comprises the following steps: collecting network traffic, system logs and user behavior data; dynamically distributing CPU / GPU computing power according to network flow, attack frequency and the like; performing cleaning and format conversion on the data; intrusion detection, vulnerability scanning and traffic anomaly analysis are executed through multi-task parallel processing; a potential attack mode is mined in combination with deep learning model and rule engine association analysis; and judging threats according to a preset rule base and triggering early warning. According to the method, the resource utilization rate is improved through intelligent computing power scheduling, the threat recognition capability is enhanced in combination with deep learning and rule double engines, the method can adapt to a complex network environment, the threat detection accuracy is 98.7% according to actual measurement display, the resource occupation is reduced by 30%, and the method is suitable for high-concurrency scenes such as a cloud platform and the Internet of Things.

The invention discloses a website AI intelligent risk assessment real-time evasion system and an evasion method thereof. The system comprises a VPN connection module to ensure that data capture is safe and hidden; the data capturing module collects various types of information of a target website in real time; the risk feature extraction module performs multi-dimensional feature extraction; the AI risk analysis and evaluation model identifies risks from multiple aspects of images, scripts and the like and grades the risks; the risk avoiding and warning module executes a hierarchical response strategy according to a result; the feedback and model optimization module collects a feedback and sample optimization model. According to the method, website risk avoidance is realized through the steps of VPN connection, data acquisition, feature extraction, risk assessment, strategy execution, model optimization and the like. According to the method, VPN and artificial intelligence technologies are combined, website risks can be comprehensively evaluated in real time, access strategies are dynamically adjusted, complex network attacks are effectively coped, and user network access safety is guaranteed.