833 results about "Traffic analysis" patented technology

The invention discloses a network line intelligent operation and maintenance monitoring management system and method. The method comprises the steps that an intelligent network monitoring and optimizing system regularly collects network equipment information through a protocol, and a monitoring agent collects server and machine room environment data; processing data in real time by adopting distributed storage and a stream processing framework; predicting a network fault by adopting threshold monitoring and a machine learning model, and identifying a bottleneck and scheduling resources based on flow analysis; in combination with deep packet inspection and an AI algorithm, network traffic is monitored in real time, security threats are identified, and IPS system protection is deployed; a repair operation is automatically triggered through a self-healing strategy, and backup resources are started; the AI engine analyzes historical and real-time data, generates network topology and link state visual content, and automatically generates reports, optimization and decision support; according to the intelligent operation and maintenance monitoring management system and method for the network line, a fault tree analysis and decision support system is introduced, and a plurality of fault reasons are intelligently analyzed and repair measures are automatically executed.

A method is provided for encrypted network traffic analysis. The method includes capturing network traffic data; calculating entropy of said data to classify traffic as encrypted or non-encrypted; applying statistical and sequential feature hybridization on encrypted traffic to extract comprehensive features; analyzing the features using a neural network model to identify encrypted traffic types and detect anomalies; and refining the analysis based on entropy and neural network insights through a feedback loop.

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.

A method for managing edge devices includes: monitoring network traffic between an edge device (ED), an edge node (EN), and a cloud device (CD) to obtain a first dataset; monitoring an activity performed on the ED to obtain a second dataset; analyzing the first dataset to generate a network traffic analysis output (NTAO); analyzing the second dataset to generate an activities analysis output (AAO); performing a first tagging of the ED under as either a secured devices category or an unsecured devices category based on the NTAO, AAO, and a first identifier; performing a second tagging of the ED under a corresponding unsecured devices sub-category; upon tagging the ED and based on the AAO, making a determination that the activity is a malicious activity; and implementing a security policy to disrupt a user of the ED.

The invention relates to a sensitive data exception cross-border detection method and system based on flow analysis, and the method comprises the steps: obtaining a plurality of to-be-detected target flow sessions; analyzing each target flow session through a dynamic risk assessment model, and performing risk assessment based on the risk score of the report information, the risk score based on the historical behavior and a comprehensive risk assessment rule matched according to the current service scene to obtain a risk value; the dynamic risk assessment model is dynamically adjusted according to data flow behavior; and when it is detected that the risk value corresponding to the target stream session exceeds a preset risk threshold, triggering real-time early warning. Through application of the method and the device, the problem that sensitive data is misreported or misreported due to the fact that an existing detection method is difficult to dynamically adapt to the change of the data flow in a complex scene of cross-border data flow is solved, the change of the data flow is dynamically adapted to the complex scene of cross-border data flow, and the sensitive data is prevented from being misreported or misreported.

The invention relates to the field of parking lot management, in particular to a parking management method and system based on data analysis. The method comprises the following steps: acquiring real-time state information of a parking lot and a historical parking monitoring log of the parking lot; analyzing the dynamic characteristics of the vehicle in the fixed parking space according to the real-time state information of the parking lot, and predicting the available time so as to generate the predicted available time of the fixed parking space; acquiring traffic flow data of a target parking lot area; performing multi-section traffic flow analysis on the traffic flow data of the target parking lot area, and performing traffic flow time sequence trend evolution to generate real-time road state characteristics of the target area; and performing time sequence parking demand analysis on the historical parking monitoring log of the parking lot, and performing dynamic parking demand prediction of the target parking lot according to the real-time road state characteristics of the target area so as to generate a dynamic parking demand prediction value. According to the invention, parking lot management with high efficiency and high utilization rate is realized.

The invention discloses a network data intelligent tool system and method based on a model context protocol MCP, and relates to the technical field of computer networks. The system comprises an AI analysis main body, an MCP Pcap tool engine, context management, a Pcap data source, various MCP Pcap tools, a Pcap tool gateway interface, a Pcap tool interface and a data packet interface. The invention provides an interaction mechanism of direct embedding and bypass data forwarding in tool calling. According to the method, direct embedded transmission of small-size data in tool calling is supported, separation of data transmission and instruction calling is achieved, the technical bottleneck that large-size Pcap data cannot be efficiently exchanged through a text channel is effectively overcome, efficient processing of the AI model on network packet capture data is achieved, and the data transmission efficiency is improved. And the efficiency and the automation degree of large-scale network traffic analysis are improved.

The invention relates to the technical field of intelligent logistics distribution, in particular to an automobile market logistics distribution scheduling system based on AI optimization, which comprises a data acquisition module, an order management module, a traffic flow analysis module and a scheduling decision module. Wherein the data acquisition module is used for acquiring current position data and traffic flow data of a delivery vehicle and order information of a customer in real time; the order management module is used for carrying out priority ranking on the orders; the traffic flow analysis module is used for predicting traffic conditions in a period of time in the future and identifying a road section and a time period which are about to be congested; and the scheduling decision module is used for optimizing the scheduling and distribution paths of the vehicles to generate an optimal scheduling scheme. According to the invention, by combining real-time traffic prediction, order priority ranking and a path optimization algorithm, the logistics distribution efficiency is effectively improved, distribution delay and resource waste are reduced, and timely distribution of high-priority orders is ensured.

The invention discloses a smart city traffic signal lamp management and control system, and relates to the technical field of traffic signal control. The system comprises a region division module, a traffic flow detection sensor, a region traffic flow analysis module, a signal lamp management and control strategy generation module, an inter-region signal lamp cooperative control module and a variable information sign and information processing and generation module. The system can accurately monitor and analyze urban traffic flow conditions in real time by comprehensively applying an advanced region division algorithm, a traffic flow detection technology and an accurate traffic flow state evaluation model, and can intelligently generate a signal lamp management and control strategy according to an analysis result, so that the traffic lamp management and control efficiency is improved. Comprising the steps that a congestion relieving dynamic timing algorithm is adopted in a congestion area, the number of times of signal lamp switching is reduced, a flow balance guiding timing algorithm is applied in an area with small flow, in addition, cooperative control over signal lamps between the areas can be achieved, and efficient connection and balanced distribution of traffic flow between the areas are ensured.

The invention provides a network security method, system and device based on an attack address, and a medium. The method comprises the following steps: capturing and storing network traffic data based on a device supporting deep packet inspection and traffic analysis; constructing a machine learning model, performing real-time data processing on the network flow data through the machine learning model, performing anomaly identification on the processed network flow data, and determining a suspicious forged IP; performing reverse DNS query on the suspicious forged IP, and judging whether the suspicious forged IP is associated with a known malicious domain name or server; obtaining log data, and analyzing a suspicious behavior pattern of a forged IP in combination with network traffic data and a DNS query result to obtain a behavior pattern of an attacker; and adjusting and optimizing a network defense strategy according to the behavior mode of the attacker. The method can effectively cope with the forged IP attack, track the behavior pattern of an attacker, adjust and optimize the network defense strategy, and improve the overall network security protection capability.

The invention discloses an information security risk assessment method based on an API. The method comprises the steps that API assets in an enterprise are obtained through flow analysis; identifying and positioning the log file or sensitive data in the file by using a sensitive data identification model; integrating the vulnerability rule set, and detecting vulnerability vulnerabilities existing on the API through an API vulnerability detection model; a behavior risk automatic monitoring engine is set, and real-time monitoring and early warning are carried out on the behavior of abnormally calling the AP I to obtain data; constructing a network attack monitoring model; drawing an API risk link relationship based on a risk monitoring model trained by historical monitoring data, and speculating a suspicious data leakage path in combination with clue association ranking analysis; performing comprehensive trace reserving on the API data use behavior; and calculating the risk by using a matrix method, and generating an API information security risk assessment report. Through the method, a comprehensive API security risk assessment system can be established, API security risks can be effectively identified and managed, and enterprise data security is protected.

The invention relates to the technical field of traffic monitoring, in particular to a Beidou-based traffic flow real-time monitoring method and system, and the method comprises the following steps: receiving signal data and analyzing a signal source based on a Beidou short message communication terminal, extracting vehicle positioning information and road occupation information in vehicle operation state data, and transmitting the vehicle positioning information and the road occupation information to a Beidou short message communication terminal; and analyzing the vehicle speed and the traffic flow density, and carrying out matching operation on the analysis result and the road section number to obtain traffic flow state data. According to the method, vehicle positioning and road occupation information is extracted through signal analysis, efficient acquisition and integration of traffic state data are realized, road section numbers are matched to improve data space precision, traffic pressure is reflected, dynamic flow analysis is supported, adjacent road section data are integrated, abnormal areas are positioned, shunting paths and proportions are optimized, and emergency response efficiency is improved. Real-time regulation and control of traffic flow are realized through dynamic pushing, the flexibility and responsiveness of traffic operation are enhanced, and the monitoring precision and the road network operation efficiency are comprehensively improved.

The invention discloses an intelligent network traffic anomaly detection and automatic isolation system, which comprises a data acquisition module, a preprocessing module, an intelligent traffic analysis module, an anomaly detection engine, an automatic isolation execution module and a visual monitoring and alarm platform, by introducing a deep learning algorithm and a multi-dimensional anomaly detection algorithm, deep mining and comprehensive analysis of network traffic can be realized, feature information is automatically learned and extracted from mass data, an accurate traffic behavior model is constructed, and abnormal behaviors in the traffic are detected in real time. And full-process automatic processing from data acquisition, preprocessing, intelligent analysis to automatic isolation is realized. When abnormal traffic is detected, the system can automatically execute isolation measures and monitor the isolation purpose in real time, and compared with a traditional method based on a rule base or threshold judgment, the method is higher in detection accuracy and real-time performance.

The invention discloses a multi-stage spatial-temporal clustering method and system based on a fused mahalanobis distance. The method comprises the following steps: acquiring a spatio-temporal data set, and determining a spatio-temporal neighbor relation of samples in the data set; calculating a space communication distance and a time decay distance of the sample; fusing the space communication distance and the time decay distance by using a mahalanobis distance to obtain a relative distance of the sample; selecting a class cluster center from the data set according to the local density of the sample and the relative distance; adopting a multi-stage distribution strategy to distribute non-class-cluster center samples to corresponding class clusters; wherein the multi-stage allocation strategy comprises an inevitable allocation stage based on space-time shared neighbor and a similarity allocation stage based on a weighted similarity matrix. According to the method, the key problems that an existing space-time clustering algorithm is insufficient in space-time attribute coupling processing and sensitive to distribution errors are solved, and the clustering accuracy, robustness and practicability in the fields of intelligent traffic analysis, seismic sequence recognition and the like are remarkably improved.

The invention provides a generative adversarial-driven intelligent security defense method and system, and solves the problem of dynamic network security defense through three-layer architecture innovation: 1, data fusion layer reconstruction: employing a multi-modal feature extraction engine driven by an MoE architecture, dynamically allocating computing power resources to a plurality of expert models, and improving the heterogeneous data distillation efficiency; an LLM for fine adjustment in the security field is introduced, a cross-modal semantic similarity matrix is constructed, and the accuracy of unstructured threat intelligence analysis is improved; a second dynamic attack and defense layer is constructed, a GPT-4 architecture attack generator is deployed, and generation of a multi-stage APT attack chain is simulated; a double-agent reinforcement learning framework is designed, and the confrontation training efficiency is improved; upgrading a three-cognitive decision-making layer, constructing a dynamic threat map based on a time sequence diagram neural network, and updating an adjacent matrix in real time; a plurality of agent clusters are deployed, the capabilities of encrypted traffic analysis and attack blocking are improved, and the problems of data layer defects, attack and defense confrontation limitation and decision-making layer bottleneck in the prior art are solved.

The invention discloses a micro-service full-link gray level traffic management system and method based on PaaS, and relates to the field of traffic management, and the system comprises an intelligent traffic analysis and decision center module, a micro-service arrangement and management platform, a full-link data analysis module, a dynamic safety protection and compliance auditing module, and a distributed configuration center. According to the micro-service full-link gray flow management system and method based on the PaaS, dynamic self-adaptive gray strategy formulation can be carried out, the latest service target, the gray version release plan and the system performance index threshold can be synchronously obtained in cooperation with a distributed configuration center, and the flow management efficiency is improved based on the information and the intelligent screening result of the flow. A routing strategy and a version switching strategy of gray traffic can be dynamically generated, and in the strategy making process, the real-time load condition of a system, the availability of micro-services and key index changes of services are fully considered.

Embodiments relate to generating specialized large language models by performing transfer learning on a base large language model. The base large language model is trained using network traffic capture files as training data to predict information in a network traffic capture file during inference. The base large language model is modified into specialized large language models for including in different applications for performing communication network analysis. In this way, the specialized large language models may be developed in an expedient and efficient manner by leveraging the training performed on the base large language model.

The invention provides a network traffic data security assessment method and system based on deep learning. The method comprises the following steps: converting original network traffic data into a graph structure data set comprising a topological structure, node attributes and time sequence behaviors; in the process, the time-space fusion input tensor is formed through the association strength between adjacent matrix and Laplacian matrix coding network entities and the fusion of time sequence characteristics extracted by time window slices. Compared with traditional flow analysis which only pays attention to a single protocol or a rate threshold value, the method achieves global relevance expression of network behaviors through graph structure modeling. Through graph structure modeling, multi-dimensional feature fusion and probabilistic risk assessment, the method can adapt to dynamic change of network topology and continuous evolution of an attack mode, so that a final assessment result is more accurate.

The invention relates to the technical field of network flow analysis, in particular to a non-intrusive network flow real-time analysis method and system based on an eBPF, and the method comprises the steps: deploying an eBPF program in a kernel mode, and capturing a network data packet entering a kernel protocol stack in real time; identifying an application layer protocol type and analyzing a key field; the structured data is transmitted to the user mode through the annular buffer area; and the user state carries out TCP stream recombination and session-level aggregation statistics on the structured data. According to the invention, on the premise that application codes are not modified, real-time and structured protocol analysis can be carried out on various common application layer protocols in a kernel layer; end-to-end delay is accurately disassembled through a full-link delay disassembling mechanism, and a performance bottleneck link is positioned; the analysis result is output in the form of structured data, second-level performance statistics and abnormal behavior detection are supported, and the operation and maintenance analysis efficiency and the system observability are remarkably improved.

The invention relates to the field of network security, and discloses a network abnormal traffic monitoring and attack defense system based on artificial intelligence, which comprises a data preprocessing module used for receiving original traffic data, executing cleaning, duplicate removal and normalization processing, and dividing a processing result into data fragments according to a preset time window; the network flow analysis module is used for receiving the data fragments, performing feature extraction and mode analysis and outputting an analysis result representing the flow abnormal degree, and the analysis result at least comprises a score value representing the overall abnormal degree and a feature vector representing the flow mode feature; the attack judgment module is used for comparing the score value with a preset abnormal threshold value so as to judge whether network abnormal traffic exists or not, if yes, the feature vector is further matched with a pre-constructed abnormal traffic type feature library, and a specific abnormal traffic type is determined according to a matching result; and the attack defense module is used for automatically triggering and executing a corresponding defense strategy.

The invention discloses a pipe network water leakage point detection and distance positioning method based on flow analysis. The method comprises the following steps: S1, constructing a flow balance model of a pipeline network; s2, monitoring the flow state of each node of the pipeline network in real time based on the flow balance model constructed in the step S1, and performing preliminary positioning on a leakage point when pipeline leakage is detected; s3, flow gradient analysis and reverse hydraulic fine positioning: in the suspected leakage area locked in the step S2, meter-scale precision positioning of a water leakage point is realized through a flow gradient analysis and reverse hydraulic iterative model; and S4, a plurality of sensor nodes are arranged on the water leakage pipeline determined in the step S3, detection data are collected, the position of a leakage point is determined according to a related positioning fusion algorithm, comprehensive decision making is carried out on the leakage point and the leakage point determined through flow gradient analysis and reverse hydraulic power in the step S3, and finally the accurate position of the pipeline leakage point is judged. The problems of low precision, weak interference resistance and the like of a traditional method can be solved, and accurate detection of leakage points is realized.

The invention relates to the technical field of network routing, and provides a real-time traffic analysis method and system for network routing, and the method comprises the steps: obtaining a network data packet of the network routing, carrying out the data preprocessing, generating a traffic event atlas, generating a dynamic threat score, carrying out the feature extraction of the traffic event atlas, and generating a behavior abnormality matrix; analyzing the abnormal behavior matrix and the dynamic threat score, generating an initial network routing strategy, and analyzing the initial network routing strategy to obtain network performance and security evaluation indexes; and adjusting the network routing based on the evaluation index to obtain an adjustment strategy, carrying out iterative optimization on the adjustment strategy to obtain a target network routing strategy, and carrying out forwarding processing on the network data packet based on the target network routing strategy to obtain a real-time flow analysis result of the network routing. Through the above scheme, real-time, dynamic and accurate analysis of the network traffic is realized, and the problems of low detection precision, slow response speed and lack of real-time adjustment capability in a highly dynamic and complex network environment are improved.

The invention discloses an intelligent route distribution system based on vehicle owner behavior prediction, and relates to the technical field of route distribution, and the distribution system comprises a data collection module which is used for obtaining the historical driving data of a vehicle owner; the behavior prediction module is used for analyzing the driving habit of the vehicle owner and predicting the behavior of the vehicle owner; the real-time traffic analysis module is used for acquiring real-time traffic data and analyzing real-time traffic conditions, weather information and road condition changes; the route optimization module is used for calculating an optimal route based on the prediction result and the real-time data and recommending the optimal route; and the user interaction module is used for displaying the recommended route and receiving user feedback and adjustment suggestions. According to the invention, by analyzing the historical driving data and driving habits of the vehicle owner, the system can provide route recommendation conforming to personal preferences, the user experience is improved, real-time traffic data and weather information are integrated, it is ensured that the recommended route adapts to the current traffic condition and road condition change, and a more accurate and efficient driving route can be provided.

The invention provides a real-time network security monitoring and protection method and system based on deep learning, and the method comprises the steps: extracting the local features of network traffic through a convolutional neural network, analyzing the time sequence features of abnormal network traffic through a long-short term memory network, and recognizing a network attack type; calculating the intensity of the network attack based on the traffic rate, the duration and the number of source IPs of the network attack, calculating a risk score based on the local features of the network traffic, and carrying out weighted calculation on the risk score and the intensity of the network attack to obtain a comprehensive score; and in response to different types of network attacks and in combination with the comprehensive scores of the different types of network attacks, executing different network attack protection measures, adjusting the protection level in real time according to the strength and risk scores of the network attacks, recording network attack information and protection measures, and generating a security log. The network flow can be analyzed in real time, the network attack type can be identified, corresponding protection measures can be taken, the accuracy of network attack detection is improved, and the risk is reduced.

The invention relates to a traffic signal management technology, in particular to an intelligent traffic signal control system, which comprises a traffic data acquisition module for acquiring data of a traffic sensor in real time; the data processing module is used for analyzing the data collected by the sensor and generating an optimized signal switching strategy; the signal control module is used for controlling the state of the traffic signal lamp according to the instruction of the data processing module and automatically adjusting the signal time according to the change of the traffic flow; the cloud computing platform module is used for remotely monitoring and managing the plurality of signal control units; and the fault diagnosis and maintenance module automatically monitors the operation state of each component of the system. By acquiring the traffic flow and the pedestrian state in real time, the signal switching strategy can be intelligently analyzed and optimized, the vehicle waiting time is shortened, the traffic capacity of the intersection is improved, and the system can adjust the state of the signal lamp in time according to flow analysis and behavior pattern recognition, so that the traffic jam in the peak period is effectively relieved.

The invention relates to the technical field of intelligent buildings and elevators, and discloses an intelligent elevator passenger flow analysis and optimization system which comprises a flow monitoring module used for monitoring the passenger flow of an elevator waiting area in real time and obtaining passenger flow data; the data processing module is used for analyzing the passenger flow data and predicting the passenger flow in a future time period; the elevator dispatching module optimizes the dispatching strategy of the elevator according to the prediction result of the data processing module, and improves the efficiency of passengers to get on and off the elevator; and the energy efficiency optimization module adjusts the acceleration and deceleration process of the elevator according to the running state of the elevator so as to reduce the energy consumption of the elevator. By monitoring the passenger flow in the elevator waiting area in real time and conducting accurate analysis, the passenger flow in the future time period can be predicted, the elevator dispatching strategy is optimized, the waiting time of passengers is shortened, the waiting efficiency is improved, and intelligent dispatching is conducted on the elevator based on the shortest waiting time and the minimum energy consumption target.

Systems, computer program products, and methods are described herein for advanced network traffic analysis in a computing environment. The present disclosure is configured to retrieve, from a traffic data log of a Web Application Firewall (WAF), information associated with a blocked traffic instance; implement, using a code analysis subsystem, a security testing protocol on a host application associated with the blocked traffic instance; determine an exposure associated with the host application based on at least implementing the security testing protocol; generate a notification comprising information associated with the exposure; and transmit a signal configured to cause a computing device associated with the host application to display the notification.

The invention discloses a power grid flow data privacy protection method and system based on a generative adversarial network, and the method comprises a flow feature extraction step, a source data set construction step, a flow analysis attack target model pre-training step, a TA-GAN model pre-training step, and an original flow confusion step. According to the method, a data set pre-training attack target model is constructed by extracting power grid flow burst and directivity features, a TA-GAN model based on a generative adversarial network is trained, and finally a generator in the TA-GAN model outputs a confused association time sequence feature according to an input association time sequence feature. And the confused associated time sequence characteristics are decoded back to the original flow data packet sequence, so that the aim of protecting the privacy of the flow data of the power grid is fulfilled. According to the method, the security of the whole digital power grid system is enhanced, and the privacy protection of the data is realized on the premise of not sacrificing the practicability of the power grid data and the operation efficiency of the power grid.

The invention discloses a road traffic information monitoring system and method, and the method comprises the steps: deploying a video monitor, a sensor, an unmanned plane and a satellite device to collect real-time road traffic data, and protecting the data safety through encryption transmission and multilayer identity authentication; the flow is monitored in real time by combining image recognition and sensor data, congestion is analyzed, traffic violation is recognized, and an alarm is given; future flow is predicted through machine learning and big data analysis, and the signal lamp period is intelligently adjusted; through combination of real-time and historical data, accident risk points are identified and early warning is carried out, and early warning is automatically issued and adjustment and control are carried out for severe weather. The AI technology automatically identifies accidents and notifies a management center, dispatches emergency resources, optimizes signal lamps and lane traffic, and dynamically adjusts surrounding flow; according to the road traffic information monitoring system and method, an automatic emergency processing system is established, accidents are automatically detected and alarms are triggered under the conditions of traffic accidents and emergencies, and emergency resources are quickly dispatched.

The invention relates to a lightweight network intrusion detection method fusing multiple tasks and transfer learning. The method comprises the following steps: acquiring to-be-detected traffic data; the to-be-detected traffic data are input into a multi-task intrusion detection model, whether the to-be-detected traffic is abnormal or not is judged, abnormal traffic is classified, performance indexes of network traffic are predicted, and the multi-task intrusion detection model is constructed based on a pre-trained lightweight deep learning model and is obtained through training of a training set. The training set comprises two-dimensional image data marked as normal traffic or abnormal traffic, attack categories and continuous values. According to the method, collaborative optimization of tasks such as anomaly detection, attack classification and performance prediction is realized, the overall performance of detection is remarkably improved, and meanwhile, efficient and accurate network traffic analysis can still be realized under the condition of limited data by utilizing the pre-trained lightweight deep learning model in transfer learning.