813 results about "Web environment" patented technology

A system may include an Al convergence system of systems for a value chain network environment.

The invention belongs to the technical field of electrical digital data processing and resource allocation, and provides an agent lightweight deployment method and a computing power elastic allocation method, lightweight deployment realizes one-time development of multi-platform deployment by constructing a plug-in container packaging tool and a multi-architecture compiling engine; static analysis is used for stripping redundant dependence of the model, and the model is dynamically cut in combination with edge resources; developing an intelligent resource description language, integrating a control group and a filtering technology to realize container-level resource monitoring, constructing a 12-dimensional dynamic state space fusing a node state, task characteristics and a network environment through computing power elastic distribution, and introducing a time delay, cost and reliability three-dimensional weighted reward model to quantify distribution earnings; an edge cloud cooperative training framework and an edge execution strategy are designed, experience is collected, a cloud end trains a Q network through federal learning, an optimization decision is played back in combination with priority experience, the agent deployment efficiency and the resource utilization rate are improved, and data privacy is guaranteed.

The invention relates to the technical field of earth leakage protection monitoring, and discloses an earth leakage circuit breaker remote monitoring system integrated with the Internet of Things. An electric leakage characteristic modeling module of the system obtains real-time monitoring data, extracts current waveform characteristic parameters, identifies an abnormal waveform mode and a first occurrence time sequence, and generates an original electric leakage characteristic template; the network path generation module performs topological sorting on the monitoring nodes based on the template, establishes an equipment connection path and constructs an Internet of Things topological path; a path anomaly analysis module extracts a node sequence and screens abnormal transmission paths to obtain an abnormal path set; the risk level judgment module collects terminal node risk tags and obtains a risk level tag group through matching; and the topological structure output module counts associated nodes, divides abnormal paths, establishes a mapping relation and generates a remote monitoring topological table. The system improves the accuracy and efficiency of electric leakage monitoring, and is suitable for a complex power network environment.

The invention provides a conference system, an intelligent agent and a conference processing method, and the system comprises an edge device which is used for carrying out the preliminary screening and feature extraction of a conference link file, and determining a first feature; the cloud server is used for analyzing the first feature after receiving the first feature sent by the edge device, and determining a conference link and conference starting time; the clock and network state monitoring module is used for evaluating the quality of a network environment where the intelligent agent is located within a first preset time before the conference starting time after receiving the conference link and the conference starting time sent by the cloud server, and selecting an optimal network access strategy to access the conference; the knowledge graph construction module is used for constructing a first knowledge graph according to the conference data; the conference recording and analyzing module is used for performing real-time translation according to the source language and the target language of the spokesman during the conference; and analyzing the conference content by adopting the optimized pre-trained large model and a multi-modal fusion algorithm to generate a conference summary document.

The invention discloses a TSN scheduling optimization method and device based on flow sensing autonomous learning, equipment and a medium. The method comprises the following steps: deploying a lightweight flow detection module in a switch or a router, and after a controller receives a data request, automatically identifying a newly arrived unknown service flow by using the lightweight flow detection module, and judging whether the newly arrived unknown service flow is a periodic TT flow or an unpredictable burst flow; the controller performs classification management on the identified service flow types, collects topological information and flow requirements of the whole network and issues the topological information and the flow requirements to the terminal nodes through a network management interface; the controller constructs an intelligent queue scheduling task based on the collected network topology information and traffic demand and converts the task into a Markov decision process MDP, network resources, queue states and priorities are used as a state space, a scheduling strategy is used as an action space, a reward function is designed in combination with throughput and delay indexes, and an intelligent queue scheduling task is obtained. Driving a dynamic environment through real-time data and training a DRL model; an enhanced queue scheduling mechanism Pro-CQF is adopted, different priority labels are configured according to classified flow types, and then mixed flow scheduling is carried out; the controller periodically collects time delay, packet loss rate and end-to-end transmission delay indexes and feeds back the indexes to the DRL model, and a scheduling strategy is updated online. According to the method, the traffic sensing and scheduling efficiency is greatly improved in a network environment in which multiple service flows coexist and end-side equipment functions are different, and the reliability and the expandability of the TSN in industrial Internet of Things, edge computing and other high-real-time application scenes are remarkably enhanced.

The invention discloses a data flow monitoring method and system based on Internet communication, and relates to the technical field of data flow monitoring, and the method comprises the steps: carrying out the time-space modeling of historical features, constructing a flow mode library through three-dimensional features, combining dynamic period detection with multi-scale fusion, and extracting normal period behaviors and abnormal modes. Real-time exception collaborative verification is carried out, a threshold detector and a prediction detector which run in parallel are utilized, multi-dimensional scores are adjusted through scene perception dynamic weights, and real exceptions and false alarms are accurately distinguished; dynamic parameter correction is carried out, PID control parameters are optimized based on false alarm feedback and reinforcement learning, an exponential decay update mode library is combined, fusion of threshold adaptive adjustment and a long-term and short-term flow mode is realized, and the robustness of the system is improved. According to the invention, the precision and robustness of data traffic monitoring can be effectively improved, false alarms and missing alarms are reduced, and the method adapts to a complex and changeable network environment.

A system includes one or more “BotMagnet” modules that are exposed to infection by malicious code. The BotMagnets may include one or more virtual machines hosting operating systems in which malicious code may be installed and executed without exposing sensitive data or other parts of a network. In particular, outbound traffic may be transmitted to a Sinkhole module that implements a service requested by the outbound traffic and transmits responses to the malicious code executing within the BotMagnet. Credentials for services implemented by a BotSink may be planted in an active directory (AD) server. The BotSink periodically uses the credentials thereby creating log entries indicating use thereof. When an attacker accesses the services using the credentials, the BotSink engages and monitors an attacker system and may generate an alert. Decoy services may be assigned to a domain and associated with names according to a naming convention of the domain.

The invention provides a network security decision-making method and device based on a large language model. The method comprises the following steps: collecting network state data and converting the network state data into natural language description; determining a semantic feature vector corresponding to the natural language description, and retrieving target threat knowledge matched with the semantic feature vector in a knowledge base storing various basic network threat knowledge; generating an analysis prompt text in combination with the target threat knowledge and the natural language description, inputting the analysis prompt text into a large language model, sequentially executing network threat analysis steps indicated in a preset thinking chain according to the analysis prompt text, and determining a network anomaly attribution and a corresponding protection strategy; and issuing the protection strategy to each distributed execution unit deployed in the target network environment, and converting the protection strategy into an equipment configuration command and executing the equipment configuration command. A closed-loop intelligent defense process from information perception to semantic understanding to collaborative response can be realized, and the real-time performance, the accuracy and the expandability of a security decision are improved.

An attack path modeling and discovery system and process of the present combines comprehensive network data with machine learning to determine the greatest risk to a network environment by exposing the path of least resistance an attacker would likely take. This system and process considers both the likelihood of a threat agent to exploit a vulnerability, and the potential for loss when that threat occurs. The system utilizes two key models to accomplish this goal. First, Knowledge Graphs (KG) are leveraged to comprehensively model relationships across an environment, and second, Graph Neural Networks (GNNs) are used to predict the path of least resistance to the network's user-defined most valuable assets.

The invention relates to the technical field of real-time rendering, cloud edge collaboration and three-dimensional visualization, in particular to a multi-instance cloud edge collaboration rendering method based on an unreal engine. Through cloud centralized scheduling and edge node plug flow collaborative architecture, on-demand distribution, dynamic start and stop and life cycle management of rendering instances are realized, and the resource utilization efficiency is remarkably improved; the system combines a relay agent mechanism of TURN and Nginx, gets through access restrictions among a public network, a local area network and a plurality of subnetworks, and realizes unified access and high-adaptability pixel flow transmission capability. Through edge-side integrated pixel flow and signaling service, distributed instance management and load balancing scheduling, the concurrent processing capability and horizontal expansibility of the system are enhanced, and the requirements of complex three-dimensional application scenes such as multiple users, real-time interaction and high-performance rendering are met. Through a cloud edge separation architecture, a relay forwarding mechanism and instance dynamic scheduling, efficient and stable pushing of unreal engine pixel streams in a multi-network environment is realized.

The invention provides a dynamic encryption communication method, system and device for an Internet of Things device, and a storage medium, and the method comprises the steps: obtaining the current geographic position information of the Internet of Things device, and determining the security demand level of the Internet of Things device according to the current geographic position information and a preset geographic position list of different security levels; acquiring a network performance index of a mobile network shared channel at the position of the Internet of Things equipment, and determining a congestion degree parameter of a surrounding network environment according to the network performance index; classifying according to the service type of the Internet of Things equipment to obtain a service volume level; and integrating the obtained security demand level, the congestion degree parameter and the service volume level, dynamically selecting a corresponding encryption algorithm to encrypt sensitive data of the Internet of Things equipment, and transmitting the encrypted data to a cloud server. By dynamically selecting the encryption algorithm and adjusting according to the location information, the security and the energy efficiency can be balanced in different scenes.

The invention belongs to the technical field of authority management, and particularly relates to a dynamic authority management system based on an integrated system, which is characterized in that on one hand, dynamic authority portraits of employees are described periodically by integrating multi-system data, and dynamic risk values of real-time operation behaviors of the employees are analyzed based on the latest dynamic authority portraits; on the other hand, through monitoring an organization event queue in real time and analyzing metadata of an event message, an organization event type is identified, a change rule corresponding to the organization event type is matched, an authority synchronization instruction is generated, and transactional operation is started; a behavior risk real-time assessment and event-driven cross-system collaboration mechanism is adopted, automatic and precise dynamic adjustment of the authority is realized, the access control security and management efficiency in an integrated network environment are improved, and in addition, a self-optimization capability based on machine learning is introduced, so that the authority management has the characteristics of continuous learning and evolution.

The invention belongs to the technical field of power information security, and discloses a power network context scene association access control method and system, and the method comprises the steps: obtaining an access request and multi-dimensional context information associated with the access request, the multi-dimensional context information comprises at least one of time information, position information, equipment state information, user behavior information and network environment information, analyzing the multi-dimensional context information, identifying a power business scene to which the current access request belongs, and dynamically generating or adjusting an access control strategy for the current access request; and performing an access control operation on the access request based on the dynamically generated or adjusted access control policy. According to the method, the access control strategy can be dynamically adjusted according to the real-time context and the specific service scene of the power network, the accuracy, the flexibility and the safety of access control are improved, and the method effectively adapts to a complex and changeable power network operation environment.

The invention discloses a traffic scheduling method and an electronic device, and relates to the technical field of traffic scheduling, and the method comprises the steps: determining the priority weight of a micro-service, and predicting a target traffic according to the historical traffic information of a network device; constructing a graph model according to the topological information of the network equipment and the dependency relationship of the micro-service, and performing embedded learning on nodes in the graph model to generate a state vector representing a network state; the priority weight, the state vector and the target traffic of the micro-service serve as input of a reinforcement learning model, and a traffic scheduling strategy of the network equipment is obtained; performing iterative search according to iterative particles formed by encoding the strategy network parameters of the reinforcement learning model and the feature learning network parameters of the graph model to determine reinforcement learning model parameters; and issuing the traffic scheduling strategy to the network equipment and executing the traffic scheduling strategy so as to solve the technical problem that a traffic scheduling method in related technologies is difficult to adapt to a dynamic and complex network environment and service requirements under a micro-service architecture, and the reliability of traffic scheduling is improved.

The invention provides an efficient multi-dimensional network penetration testing method based on RAG, which comprises the following steps: firstly, identifying sub-domain names possibly existing in a target website, and sequentially expanding attack surfaces of penetration testing to obtain vulnerability information pairs; secondly, a design model generated based on retrieval enhancement is adopted, vulnerability information pairs are extracted from the queue to be utilized, knowledge items related to local knowledge base retrieval and network intelligent search retrieval are utilized, and finally, a large model generates vulnerability utilization information according to the knowledge items; acquiring target machine permission for the previously acquired vulnerability information pair construction command injector, and further scanning other hosts of the intranet accessed by the target skipping machine; finally, combining vulnerability information obtained by penetration testing, utilizing a large model to sort vulnerabilities existing in different assets, and outputting penetration testing reports for different assets. According to the method and the system, comprehensive penetration testing of cross-network and cross-system is realized, security experts are helped to quickly identify, verify and repair vulnerabilities in a complex and changeable network environment, and high-efficiency and low-cost network security maintenance is realized.

The invention provides a vision-based cross-network interaction method and system, and relates to the technical field of intelligent interaction, and the method comprises the steps: obtaining a visual interaction sequence, constructing multi-modal feature representation, achieving cross-domain semantic alignment, deconstructing visual information into a hierarchical control instruction set, and transmitting the hierarchical control instruction set to a target network environment for execution after security classification. And a bidirectional mapping relation graph is constructed to realize incremental optimization. According to the method, semantic bridging between heterogeneous networks can be established, the cross-domain control precision is improved, and meanwhile safe interaction in a network isolation environment is guaranteed.

A security platform employs a variety techniques and mechanisms to detect security related anomalies and threats in a computer network environment. The security platform is “big data” driven and employs machine learning to perform security analytics. The security platform performs user / entity behavioral analytics (UEBA) to detect the security related anomalies and threats, regardless of whether such anomalies / threats were previously known. The security platform can include both real-time and batch paths / modes for detecting anomalies and threats. By visually presenting analytical results scored with risk ratings and supporting evidence, the security platform enables network security administrators to respond to a detected anomaly or threat, and to take action promptly.

The invention discloses a weak network environment photographing off-line uploading method and system, and relates to the technical field of file upload, and the method comprises the steps: monitoring the network bandwidth and the use conditions of a CPU and a memory in real time, building a dynamic calculation model, and achieving the adaptive adjustment of the size of a data block; dividing the file to be uploaded into blocks of which the sizes are pre-adjusted according to a dynamic blocking algorithm, and storing the blocks locally; according to a priority calculation model, establishing a dynamic priority queue, and calculating a priority for each block uploading task; a queue scheduler is set, high-priority blocks are ensured to be preposed, a priority queue is established, the queue is reordered at set time intervals, and high-priority block tasks are ensured to be preposed; the network connection state is detected in real time, the dynamic change of the network quality is continuously monitored, the time period when the network condition meets the preset condition is intelligently identified according to the monitoring result, and locally cached blocks are timely uploaded to a service system in an offline mode according to the priority. According to the invention, the file can be uploaded according to the network state.

The invention relates to the technical field of Internet of Things communication transmission, and discloses an Internet of Things data intelligent transmission method and system, and the method comprises the steps: abstracting the performance of a multi-source heterogeneous link through a software defined network controller, dynamically generating a slice template, carrying out the traffic marking and priority scheduling, constructing a multi-path target constraint optimization model, and carrying out the optimization of the multi-path target constraint optimization model. And in combination with a network function virtualization mechanism, multi-level intelligent transmission is implemented at edge nodes, and a final transmission control instruction is generated. In the prior art, in a heterogeneous multi-network environment, low-delay, high-reliability and high-bandwidth transmission cannot be guaranteed at the same time, and especially in an industrial automation scene, technical problems that a control instruction is easy to delay and a monitoring data bandwidth is limited exist. According to the invention, the overall transmission reliability and bandwidth utilization efficiency are improved through the dynamic priority traffic scheduling based on network slices, the multipath reliability product model and the dynamic batch processing and progressive compression technology based on real-time link feedback.

An attack detection and source tracing method includes acquiring entities in a target network environment and interaction event information between the entities and constructing a network event graph with the entities and the interaction event information; determining a graph embedding vector of each interaction event information in the network event graph as feature information based on a preset attack feature recognition model and determining an attack event in the network event graph according to the feature information; determining dependencies between the attack event and remaining interaction event information in the network event graph and searching for a corresponding interaction event information as source tracing information of the attack event according to the dependencies.

The invention discloses a task scheduling method and device, a storage medium and electronic equipment, and relates to the field of cloud computing. The method comprises the steps that a task model is constructed according to a target training task, and the task model is used for representing attributes and resource requirements of the target training task; on the basis of the task model, predicting performance results when the target training task is executed on the N computational nodes to obtain N performance results, N being an integer greater than 1, and the performance results comprising execution efficiency and resource utilization rate of the target training task on the computational nodes; filtering the N computing nodes according to the N performance results and the network environment information to obtain S first computing nodes, and determining a target node from the S first computing nodes; and executing the target training task through the target node. The technical problem of low deep learning training task scheduling efficiency in the prior art is solved.

The invention relates to a dynamic page loading optimization method and device based on user behaviors and a network environment. Comprising the following steps: collecting user behavior data in real time to obtain a user behavior analysis result: dynamically generating a loading priority list according to the user behavior analysis result; wherein the loading priority list comprises key resources, high-priority resources, secondary resources and delay resources; determining a resource loading strategy based on the loading priority list; wherein the resource loading strategy comprises priority loading of key resources, necessary loading of high-priority resources, and delayed loading or batch loading of secondary resources and delayed resources; detecting the network state of the user to evaluate the performance of the current network; and dynamically adjusting a resource loading strategy based on the performance of the current network so as to load resources required by the page according to the dynamically adjusted resource loading strategy. According to the invention, the resource loading strategy can be dynamically adjusted according to the network state, and the user experience during resource loading is improved.

System (100) for detecting anomalous commands in network traffic using a Large Language Model (LLM), comprising: a. a traffic capture module configured to monitor and extract command-level data from incoming network traffic over one or more communication protocols; b. a preprocessing and tokenization module configured to normalize, filter and transform the extracted commands into a structured format suitable for analysis; c. a contextual embedding module comprising a pre-trained or fine-tuned LLM configured to generate semantic embeddings of the structured commands; d. an anomaly detection module configured to compare the generated embeddings with a baseline profile of legitimate command behavior and identify deviations using machine learning algorithms; e. a threat classification module configured to categorize the identified anomalies based on the type and severity of the threat; and f. a response and logging module configured to initiate automatic remedial actions and record the events for further analysis, g. the system operates in real time to detect, classify, and respond to anomalous commands in a networked environment.

The invention discloses a malicious DoH tunnel traffic detection method and device and a storage medium, and relates to the technical field of network security, and the method comprises the steps: obtaining a to-be-detected traffic, and extracting a plurality of to-be-detected traffic features of the to-be-detected traffic; inputting each to-be-detected traffic feature into a target malicious traffic detection model for detection, and obtaining a malicious traffic detection result output by the target malicious traffic detection model, the target malicious traffic detection model is obtained by training a malicious traffic feature data set after important feature screening, semantic enhancement and semantic processing. According to the method, redundant and irrelevant features are removed through important feature screening, key information is focused, malicious traffic semantics are highlighted through semantic enhancement, and environmental noise interference is inhibited; and the flow features are deeply modeled through semantic processing, so that the model detection accuracy is high, the model can adapt to complex and changeable network environments and flow modes, and the robustness and generalization ability of the model are improved.

Techniques are disclosed for providing high performant packets processing capabilities in a virtualized cloud environment that enhance the scalability and high availability of the packets processing infrastructure. In certain embodiments disclosed herein, the VNICs functionality performed by network virtualization devices (NVDs) is offloaded from the NVDs to a fleet of computers, referred to as VNIC-as-a-Service System (or VNICaaS system). VNICaaS system is configured to provide Virtual Network Interface Cards (VNICs)-related functionality or service for multiple compute instances belonging to multiple tenants or customers of the CSPI. The VNICaaS system is capable of hosting multiple VNICs to process and transmit traffic in a distributed virtualized cloud networks environment. A single VNIC executed by the VNICaaS system can be used to process packets received from multiple compute instances.

The invention discloses an LSTM electric power Internet of Things ring main unit optimization method, system, device and medium, and belongs to the technical field of electric power Internet of Things management and data analysis, and the method comprises the steps: collecting communication state and network environment data in real time, and generating a multi-dimensional feature matrix through dynamic standard score standardization and derivative feature calculation; dynamically adjusting an LSTM forgetting gate weight based on the bandwidth fluctuation index, and outputting an equipment performance score; constructing a dynamic scoring algorithm and triggering a differentiated maintenance instruction in linkage with a multi-threshold scene strategy; a closed-loop feedback mechanism is adopted to screen high-deviation data, new scene training is enhanced through a dynamic loss function, and minute-level hot updating of the model is achieved; the method solves the problems of static threshold misjudgment, poor environment adaptability of the prediction model and single decision-making mechanism, remarkably improves the feature quality, the equipment evaluation precision and the resource allocation rationality, and strengthens the intelligent operation and maintenance capability of the system.

The invention discloses an Internet of Things equipment automatic identification method and device based on a cellular network environment and a storage medium, and the method comprises the following steps: S1, collecting IP flow information of Internet of Things equipment, exporting record data, and storing the record data to a flow data storage module; s2, extracting equipment traffic features, generating feature vectors, and storing the feature vectors in a feature database; s3, training an equipment identification model by using an improved structure sparse variational automatic encoder in combination with a space-time diagram convolutional network, and extracting an equipment traffic mode and behavior topological characteristics; s4, optimizing model training by adopting a category balance loss function; s5, collecting real-time flow data of to-be-identified equipment, converting the real-time flow data into feature vectors, inputting the feature vectors into the identification model, and calculating a matching result through cosine similarity; and S6, setting a similarity threshold value, outputting the type and the model of the equipment, or marking and storing unknown equipment. According to the method, the identification accuracy and the real-time performance are improved, and the identification capability of the shared IP equipment and the small sample equipment is enhanced.

The invention discloses a graph structure-based domain penetration attack path generation method, which belongs to the technical field of network security, and comprises the following steps of: constructing a multi-level relation graph comprising a host node, a service node and a user node through automatic detection by taking any host in a domain as a starting point; assigning a weight attribute to the atlas edge based on a vulnerability library and an attack pattern library; an improved heuristic graph search algorithm is adopted, all feasible attack paths and threat scores thereof are generated by integrating the path length, attack difficulty and permission improvement effect, and the problem that the threat scores of all the feasible attack paths are influenced in various scenes and dynamic change domain environments is solved. The technical problems of realizing comprehensive automatic penetration testing, accurately identifying potential attack paths and establishing a systematic threat assessment mechanism are solved, the automation, intelligence and high-efficiency level of domain penetration testing is remarkably improved, and the method has good adaptability, expansibility and practical value and is suitable for popularization and application. And attack path discovery and risk early warning work in a dynamic network environment with high security requirements can be effectively supported.

Solutions for integration and ongoing management of enterprise applications operating in a networked environment are disclosed. An AI transformer extracts, transforms, and manages integration point information from various sources, including documents, UMLs, and other system diagrams. Instead of relying on traditional documentation, the invention utilizes blockchain-based NFTs (non-fungible tokens) to store and manage integration point information. NFTs are created using smart contracts, which define the rules and conditions for the NFT. A metadata engine prepares metadata for generating the Integration Point NFTs. An NFT manager creates the integration point NFTs. A change analyzer determines the impact of changes to integration points on different applications and functionalities. A release compliance component is integrated with DevOps and is responsible for ensuring that integration point changes are incorporated into all impacted applications according to the release defined in the NFT.