34 results about "Web operations" patented technology

This invention provides a method for providing CNI support for a Docker container engine, relating to the field of container runtime technology. The method includes configuring a custom runtime component docker-cni in the Docker container engine; specifying the custom runtime component docker-cni as the runtime when creating a container and setting it to a network-free mode (none); injecting a pre-start hook and a stop hook into the container's configuration file during the container creation phase; the hooks are used to trigger the custom runtime component docker-cni to perform CNI network operations; when the container starts and triggers the pre-start hook, calling the CNI plugin's add network operation according to the command injected in the pre-start hook to complete the container network configuration; when the container stops and triggers the stop hook, calling the CNI plugin's delete network operation according to the command injected in the stop hook to clean up the container network configuration.

Methods, systems and devices for fractional cooperative multipoint network operation are described. One example method for wireless communication includes determining, by a network device, a cooperative multipoint (COMP) management status of wireless devices served by the network device, and providing, by the network device, wireless connectivity to the one or more wireless devices, wherein the network device jointly manages transmission resources for a first wireless device due to the COMP management status being a joint COMP status and the network device locally manages transmission resources for a second wireless device due to the COMP management status being a local COMP status.

A system configured for simulating communication loads over a network, the system having: a controller module; an application module, operationally coupled over the network to the controller module, wherein the system is configured to execute a load test over the network with the application module, by: transmitting mock telemetry messages, associated with mock devices, representing internet-of-things (IoT) devices, to the application module, wherein the mock telemetry messages are equivalent to production telemetry messages sent from the IoT devices; and issuing an alert when the system determines that a result of the load test is indicative of errors above a threshold associated with receipt of the mock telemetry messages by the application module.

The invention belongs to the crossing field of artificial intelligence and production manufacturing, and discloses a flexible job shop scheduling method based on a Mama model and a cross attention mechanism, which comprises the following steps: constructing a Markov decision process model; serializing scheduling state characteristics; extracting features of the Mama model and the cross attention backbone network; decision network operation-machine pair selection; and optimizing an arrangement transformation strategy. Aiming at the limitation of feature extraction in a traditional scheduling method based on a graph neural network, the invention provides a scheduling method taking a Mama encoder and a cross attention decoder as a trunk, efficient sequence feature modeling and feature interaction are realized, and then a high-quality solution of flexible job shop scheduling is obtained. According to the flexible job shop scheduling method, efficient sequence modeling and feature interaction can be realized.

Discussed herein are methods and systems for dynamically recalibrating machine learning model parameters. In one method, a server executes one or more prediction models to process network operations from various data feeds, in order to identify the likelihood of these operations being fraudulent or malicious. The server monitors performance data, such as the operation and execution metrics of network operations, and evaluates whether the performance values, like recall values, meet defined thresholds. If the performance data fails to meet these thresholds, the server employs a function-generation machine learning model to predict a threshold modification function. This modification function is then applied to adjust the relevant thresholds. Utilizing the modification function, the server dynamically revises one or more parameters of the prediction models to enhance their accuracy and efficacy.

Described herein are methods and systems to partially freeze an electronic account associated with a user. The server receives, via a graphical user interface a request to execute a network operation to connect to a webserver. When the server executes the network operation, the server transmits a request for access to the electronic account associated with an electronic application of the user. Once the webserver authenticates the request, the server can receive an indication that the request to access the electronic account has been blocked by the webserver. The server can revise the graphical user interface and authorize the user. Once the user is authorized, the server can cause the webserver to remove the blocked access and provide access to the electronic account. Furthermore, the server can cause the webserver to block future access to the electronic application.

Methods and systems are described herein for recommending network processing routes when conducting blockchain operations. For example, the system may receive a first user request to perform a first blockchain operation across one or more computer networks to access a first blockchain asset. The system may then determine a first processing characteristic for the first user request and a first plurality of locations of the first blockchain asset (e.g., across the one or more computer networks). The system may then determine a first plurality of network routes to a first subset of the first plurality of locations. The system may then filter the first plurality of network routes (e.g., based on the first processing characteristic) to generate a first subset of the plurality of network routes. Based on the first subset of the plurality of network routes, the system may generate a first recommendation for performing the blockchain operation.

This invention relates to the field of network communication technology, specifically to an intelligent access control method and system based on a zero-trust architecture between microservices, solving the technical problem of poor security protection in existing technologies. The method includes: acquiring the kernel network operation records of a container instance; the kernel network operation records include a set of context information corresponding to network operations triggered by the container instance at the operating system kernel layer; querying the corresponding resource status based on the container instance identifier in the context information set corresponding to the network operation, and calculating the effective quota in conjunction with the triggering time; the effective quota is used to characterize the current network resource budget that the container instance can consume; performing resource consumption matching analysis based on the context information set corresponding to the network operation to determine the actual resource consumption value; the actual resource consumption value is used to characterize the network resource quota required for the network operation; and performing access control decisions on the network operation based on the effective quota and the actual resource consumption value.

The application provides a method and system for implementing Internet of Things virtual simulation on a Web page, which comprises resource deployment: deploying a front-end display layer, a back-end logic layer and a data storage layer on a server; simulation configuration: loading the front-end display layer content to a Web page of a user terminal, and setting the properties of each simulation model in the Internet of Things system according to actual requirements; starting simulation: loading the corresponding simulation logic and resources according to the initialization information set by the user; initializing all devices and environment parameters, and entering the simulation state; model calculation: using a discrete time driving framework to simulate network operation, and ensuring that all network events occur at a specific time; picture presentation and dynamic editing. The method for implementing Internet of Things virtual simulation on a Web page combines front-end display, back-end logic processing and data storage through the design of a multi-layer architecture, realizes efficient and flexible Internet of Things system simulation, and is helpful to reduce the cost and time of Internet of Things system development and testing.

To create test scripts more efficiently for automated testing of web applications, we aim to improve the efficiency of the process. [Solution] A web operation automated execution agent 1 that automatically performs one or more operations on a website 3, which obtains the HTML 14 of the website 3, creates a script 15 to perform one or more operations using LLM2 based on the HTML 14, and performs operations on the website 3 based on the script 15.

A system configured for emulating a communication load on a network, the system having: a controller module; a controller module operatively coupled to the controller module over the network, where the system is configured to perform a load test over the network with the application module by transmitting an analog telemetry message associated with an analog device representing an Internet of Things (IoT) device to the application module, where the analog telemetry message is transmitted to the application module, the analog telemetry message is equivalent to a production telemetry message sent from the IoT device; and issuing an alarm when the system determines that a result of the load test indicates that an error is above a threshold associated with receiving the analog telemetry message by the application module.

Systems and methods for automatically updating validation rules used to detect and remediate network operation validation anomalies in conglomerate-application-based ecosystems are disclosed. The disclosed anomaly evaluation platform can dynamically monitor a set of document repositories containing specific guidelines for generating validation reports. In response to detecting updates in the guidelines, the anomaly evaluation platform retrieves a communication map defining parent-child relationships between validation rules and generates an updated communication map. The anomaly evaluation platform detects a transmission of a first network operation to a software application, which uses the first network operation to generate a second network operation. The anomaly evaluation platform provides the first network operation, the second network operation, and the updated communication map to an artificial intelligence model to generate a validation report that meets the specific criteria guidelines.

An unknown operational technology (OT) or Internet of Things (IoT) device is detected by capturing network transactions related to downstream network devices and identifying the unknown OT or IoT device from non-OT or non-IoT network devices on the enterprise network. A device type of the unknown OT or IoT device is determined using an AIOP detection model. The AIOP detection model is trained from unsupervised cluster learning from a history of externally captured network transactions of the plurality of known OT and IoT devices. The vulnerabilities of the unknown OT or IoT device are assessed by predicting services from correlating the device type to the externally captured network transactions.

Systems and methods are provided for provably secure network monitoring. A significant technical problem exists in Distributed Network Resources (DNRs) due to their increased reliance on digital communication and control, which presents a substantial risk from cyberattacks. A mathematically-backed secure network monitoring framework is provided that utilizes bastion platforms in the network to ensure end-to-end protection by detecting and preventing cyber threats while isolating, attributing, and securely communicating DNR traffic. Prior to deployment, software for the bastion platform undergoes a formal verification process that models the network mathematically and then evaluates that model to determine if a trusted network security mechanism (TNSM) holds true for the network. TNSM is a security mechanism that enforces permitted patterns of operations of the network without adversarial influence. In some implementations TNSM utilizes anti-spoofing, anti-flooding, secure kill-switch, secure patch delivery, and secure network flow mechanisms to secure the network.

Systems and methods for network management are disclosed herein, including collecting network information (2002) including at least a topology of the network (10), traffic flow characteristics, and network performance data; constructing a digital twin (410) of the network (10) based on the collected information, wherein the digital twin (410) includes nodes (12), links (14), and associated traffic flows; applying a machine learning model (416) to the digital twin (410) to predict QoS metrics for each traffic flow; and outputting the predicted QoS metrics for further analysis or network actions. The digital twin (410) can be used for various use cases, including, e.g., localizing faults in links (14) and / or nodes (12), optimizing QoS metrics over potential BGP routes via a local search algorithm (414), network planning to optimize network topologies of the network (10), what-if failure scenarios involving removal or disabling of links (14) and / or nodes (12), and the like.

Discussed herein are methods and systems for dynamically recalibrating machine learning model parameters. In one method, a server executes one or more prediction models to process network operations from various data feeds, in order to identify the likelihood of these operations being fraudulent or malicious. The server monitors performance data, such as the operation and execution metrics of network operations, and evaluates whether the performance values, like recall values, meet defined thresholds. If the performance data fails to meet these thresholds, the server employs a function-generation machine learning model to predict a threshold modification function. This modification function is then applied to adjust the relevant thresholds. Utilizing the modification function, the server dynamically revises one or more parameters of the prediction models to enhance their accuracy and efficacy.

The invention discloses a network fault simulation method and device, equipment and a storage medium. In the scheme, a network fault simulation configuration file is obtained, and the file predefines a mapping relation between a trigger file used for triggering execution of a fault scene and a network fault scene and a network operation sequence corresponding to each scene. And monitoring a preset monitoring path, and when it is monitored that the trigger file matched with the mapping relationship is created, determining a corresponding network fault scene. And calling a corresponding network operation sequence from the configuration file based on the scene and executing the network operation sequence. According to the technical scheme, the file system event is used as a drive, accurate linkage of network fault simulation and the service state is achieved, and the accuracy and the automation level of the test opportunity are improved. The mapping relation and the operation sequence are defined through the configuration file, a complex scene can be flexibly defined without modifying codes, the construction and maintenance cost is reduced, and therefore the automation level of network testing and the scene construction efficiency are improved.

A distributed generative modeling scheme employs a jointly trained neural network path (118) that implements a generative model (112) at a UE (110) rather than entirely at a remote application server (106). An encoder neural network (120) at a base station (108) between the application server and the UE operates to effectively encode input cue information (116) from the application server for efficient transmission to the UE. At the UE, a decoder neural network (122) is employed to effectively decode the encoded cue information to restore a representation of the original cue information (126). The decoded cue information is provided as input to a generative model at the UE for processing in some cases along with one or more additional inputs, such as local sensor data (144), to generate output content (128), which is then processed by one or more UX components (114) associated with the UE.

The present disclosure provides a security detection method, device and equipment for database data tampering and a medium, comprising: analyzing database network traffic and database local logs to obtain network operation information and log operation information; standardizing and enriching entities of the network operation information and the log operation information to obtain a database audit log; learning user behavior of each business entity to establish a user behavior baseline; learning access content of each business entity to establish a data content baseline; learning historical transactions of each business entity to establish a transaction pattern baseline; performing real-time operation risk assessment on real-time operation flow through the user behavior baseline and the data content baseline to obtain an operation risk score; performing transaction correlation analysis on the real-time operation flow through the transaction pattern baseline to obtain correlation analysis data; and generating a security risk level according to the operation risk score and the correlation analysis data. Thus, the security detection efficiency of database operation is effectively improved.

This invention provides an AI-based cross-system web automation operation method and system. The method includes: registering all web system functions within an enterprise to an AI system, inputting the web operation flow and key information extraction rules for each function; integrating AI capabilities into web applications as plug-ins; when a user selects target information, the AI ​​recognizes the user's intent, generates a shortcut function entry in the browser's right-click menu, and extracts key information; automatically executing corresponding cross-system web operations based on the automation capabilities of Playwright and Browser Use; providing auxiliary enhancement services through floating windows; and allowing users to drive automated web operations via natural language commands. The system comprises six modules, including a function registration module and an AI integration module, to implement the above method. This invention solves the problems of cumbersome web operations, difficult cross-system collaboration, and low automation levels in existing web applications. It eliminates the need for manual operation, improves operational efficiency, achieves intelligent collaboration among multiple systems, and provides convenient user access.

Systems and methods of traversing webpages using an artificial intelligence (AI) agent are disclosed. An example method includes, in response to a user request, received via a wearable device worn by a user, to perform a web-based action, invoking the AI agent. The method includes, causing the AI agent to traverse webpages based on the user request. The AI agent obtains, for each webpage of the webpages, information for performing the web-based action. The method includes generating, by the AI agent, an AI response based on the information, and presenting, at the wearable device, the AI response. The AI response includes recommendations for performing the web-based action. The method further includes presenting, at the wearable device, the AI response; and, in response to user selection, via the wearable device, of a recommendation included in the AI response, causing the AI agent to perform the web-based action based on the recommendation.

The invention discloses a Web operation video generation method and device, equipment, a medium and a program product, and relates to the technical field of video generation, and the method comprises the steps: generating an operation task queue based on a predefined configuration file after a host Web application is loaded; and executing the operation task instructions in the operation task queue in sequence, after any operation task instruction causing page view change is executed, starting a visual feedback buffer window with a preset duration, after the visual feedback buffer window is closed, suspending execution of subsequent operation task instructions, and controlling the media recording controller to enter a dynamic waiting state. During the dynamic waiting state, non-linear timeline control is performed on capture and writing of media stream data based on the monitored multi-dimensional state of the page view. And after all the operation task instructions are executed, packaging and exporting the media stream data recorded by the media recording controller, thereby reducing the post-processing cost of the web operation video.

A method of digital communication includes transmitting, by a first network function to a radio access network (RAN) device, a reader request; receiving, by the first network function, from the RAN device, a reader response in response to the reader request; transmitting, by the first network function, a service request based on the reader response; receiving, by the first network function, a service response for the service request; and performing further network operations based on the service response.

The invention discloses an automatic implementation method and system for batch operation of networks in a virtualization environment, and belongs to the technical field of communication service tests.The method comprises the steps that AI generates network parameters, natural language instruction cues containing operation intentions are processed, and a network configuration parameter data set is generated; the AI executes a background network operation script: performing instruction analysis on a natural language instruction cue word of a specific network task, generating a standardized operation instruction sequence, and scheduling and calling a Python network operation execution module for execution; python configuration data is read and integrated, and structured input data is provided for automatic execution; python network operation execution: receiving a scheduling instruction and configuration data, and driving a browser to complete actual webpage operation; the invention relates to a Chrome browser driver. According to the method, the communication service testing efficiency can be remarkably improved, and the operation accuracy is greatly improved by designing network parameters in advance.

One example method includes collecting real time data regarding operations in a network, processing the real time data to create structured data, extracting latent features from the structured data, using the latent features to generate potential workload offloading scenarios, identifying an optimal workload offloading scenario from the potential workload offloading scenarios, translating the optimal workload offloading scenario into a form understandable by a network device that is performing a workload, and transmitting an offloading command to the network device.

A method for offloading network operations is described. The method includes receiving, from a first application, an offload service capability request message, the offload service capability request message used to request information about capabilities of an offload service that meet a set of requirements for the offload service; transmitting a response to the application, the response including a set of offload service templates that are (1) selected based on application requirements and (2) possible templates that will be modified for performing operations of the application; evaluating network resources for program code of the application to select a set of network resources for offloading operations of the first application to the network resources; and installing program code generated based on the set of offload service templates on the set of network resources to cause the set of network resources to process packets from a second application addressed to the first application.

In one embodiment, a device in a network receives certificate data for an encrypted traffic flow associated with a client node in the network. The device determines one or more data features from the certificate data. The device determines one or more flow characteristics of the encrypted traffic flow. The device performs a classification of an application executed by the client node and associated with the encrypted traffic flow by using a machine learning-based classifier to assess the one or more data features from the certificate data and the one or more flow characteristics of the traffic flow. The device causes performance of a network action based on a result of the classification of the application.

In certain embodiments, a physical network access token at a network access terminal may be authenticated for modification of records at a remote server system. In some embodiments, a set of records and counterpart records having the same record identifiers and resource amounts may be stored independently on a physical token or user device and the remote server system. When a connection is established between an access terminal and the physical token (e.g., for authenticating a modification of record(s)), the access terminal may transmit input data to the token, which may use the input data with the records stored on the token to generate authentication data, which may be used by the remote server to authenticate a network action requested via the token.

A computer-implemented system for managing online, event-driven asset network events is disclosed. The system presents role-specific electronic dashboards to different parties via a web application. Prior to a scheduled event, a user selection and associated value are captured and stored within an immutable executable file containing asset attributes and computer-readable instructions. The file is stored in both a system database and on the user device to prevent unauthorized modification. Upon detecting commencement of the scheduled event, the system automatically executes the file, submitting the instructed value across connected platforms and confirming completion of the network operation in accordance with predefined transfer rules. Integrated synchronization and external data interfaces provide real-time updates, secure recordkeeping, and coordinated process management for all stakeholders.