138 results about "Netizen" patented technology

The invention discloses a network information security defense detection method and system, and relates to the technical field of network information security, and the system comprises a website security check module, a downloading security check module, an access security check module and a display execution module. In daily Internet surfing, net friends often encounter network security problems, for example, users may receive phishing mails disguised as banks or download malicious software in untrusted websites, resulting in data stealing. Identity embezzlement and data leakage increase privacy and financial risks, and man-in-the-middle attack may intercept sensitive information through an unencrypted Wi-Fi network. According to the method, security detection including domain name check, certificate verification, content scanning, integrity verification and the like is carried out on websites, downloading programs and external access equipment, so that security risks are found and processed in time; and meanwhile, by adopting a virtual machine isolation technology and behavior monitoring, the protection on malicious programs is enhanced, the security risk of external equipment is evaluated, and the security of a network environment and data is ensured.

Embodiments extract a key phrase from an online profile of a user of a professional social network (PSN) and identify a set of skills associated with the key phrase. A set of skill embeddings corresponding to the identified set of skills may be retrieved from a store of skill embeddings. The skill embeddings may be pre-created by a large language model (LLM). The retrieved skill embeddings may be aggregated to create a skill-centric digital representation of the user. Based on the skill-centric digital representation of the user, a subset of digital documents may be identified to present to the user via the PSN.

The invention relates to a transmission method and device based on network environment self-adaption and a storage medium, and the method comprises the steps: trying to establish direct connection with a cloud PC server if a user terminal is in an intranet and is in the same-level network with the cloud PC server; if the user terminal is in the intranet but is not in the same-level network with the cloud PC server, trying to establish connection with the cloud PC server through P2P, and if the user terminal does not successfully establish connection with the cloud PC server through the media Trans; if the user terminal is in the external network and the VPN connected with the user terminal and the cloud PC server are in the same level of network, connecting the VPN through dialing and directly connecting the IP address of the cloud PC server to realize direct connection of media streams; and if the user terminal is in an external network, but the VPN connected with the user terminal and the cloud PC server are not in the same level of network, the user terminal is connected with the VPN through dialing, the VPN tries to establish connection with the cloud PC server through P2P, and if the connection is not successful, the VPN tries to establish connection with the cloud PC server through Trans.

The invention provides a social network user viewpoint classification method and device, computing equipment and a storage medium. The method comprises the following steps: firstly, acquiring social network data including structured data and unstructured data; the unstructured data comprises a user personal description text, a user interaction text and a user interaction relationship; a social network edge text graph structure is constructed according to the obtained social network data, the structured data and the user personal description text serve as node attributes, and the user interaction text serves as edge connection attributes; reasoning a text topic of the whole network by utilizing a large language model according to text attributes on the connecting edges; combining the text topic, the text attribute of the node and the text attribute of the associated edge, and obtaining a node semantic vector by utilizing a large language model; extracting a node feature vector based on feature engineering, and splicing the node feature vector with the node semantic vector to obtain a node representation vector; and the node representation vectors are input into the graph attention network, user viewpoint classification is carried out, and the viewpoint classification accuracy is improved.

A video streaming user platform identification process for processing data packets of ISP level network traffic to automatically identify in real-time user platforms and content providers of video streams of the network traffic, the process being for execution at the ISP level by at least one processor of a video streaming user platform identification apparatus of a network service provider, the process including the steps of: receiving data packets of general network traffic of a plurality of network users of the network service provider; processing the received data packets to detect TCP / QUIC and TLS handshake packets of video streams of corresponding ones of the network users; processing the detected TCP / QUIC and TLS handshake packets of video streams to generate, for each of the video streams, a corresponding plurality of values of respective predetermined attributes; for each of the video streams, processing the corresponding attributes values with a trained classifier to classify the video stream into a corresponding one of a plurality of predetermined classes, each of the predetermined classes representing a corresponding combination of a corresponding content provider of the video stream, and a corresponding user platform being used by a corresponding one of the network users to stream the video stream from the corresponding content provider; and generating statistical data representing a statistical distribution of the classes of video streams being streamed via the network service provider; wherein the user platform includes at least one of: (i) the user's operating system; and (ii) the user's software application being used to stream the video stream.

The present disclosure discloses a system and a method for evaluating the risk associated with network assets. More particularly, the present disclosure provides the system and method for evaluating risk associated with network assets based on risk scoring. The disclosed methodology calculates a singular risk score for each network asset. Further, the singular risk score for each network asset is displayed on a web user interface (UI) page to facilitate the user to quickly and efficiently monitor the security status of the network assets without a need for extensive training or technical expertise.

The invention provides a token-based influence user selection method under a dynamic social network, which comprises the following steps of: 1, acquiring information of users in the social network and relationship information among the users, generating the intimacy degree among the users into a weight of an edge in the network, and constructing a whole social network model; 2, each user in the social network can generate a certain number of tokens according to own original influence; 3, distributing tokens to neighbor users according to a network node relation and a self-influence rule after each round of continuous generation through an iteration mechanism; 4, the number of tokens absorbed by the social network users represents the influence of the users; 5, selecting a proper z seed set through final convergence judgment; and 6, verifying the effect of the result seed set through an IC diffusion model. According to the method, the token is used as a medium, the problem of influence maximization of the social network is solved from a distributed perspective, a user selection model is provided, and the method is suitable for large-scale and dynamically changing network scenes and is low in cost.

Network subscriber devices simulate peer devices via virtual models displayed within a real-time capture of a physical environment, as an extended reality (XR) experience for users. Immersion of the XR experience is enhanced based on layering the virtual model of a simulated device in relation to digits of a user included within the real-time capture, thus appearing like the user is holding the simulated device. The digits of the user that are layered above the virtual model can be tracked such that the user can move these digits to make virtual inputs to the simulated device that can cause virtual display content shown with the virtual model to change. Device specification information is also shown within the XR experience. Via the XR experience, network subscribers can be better informed on devices available for use on a telecommunication network and can immediately acquire devices being simulated within the XR experience.

System and method for anti-phishing emails based on link domain name and user feedback The invention provides a system and a method for anti-phishing emails based on link domain name and user feedback. The system comprises an email receiving device, an email pre-processing device, an email property judging device, an outputting device and a feedback processing device. The method comprises the following steps: analyzing characteristics of the link domain name in the email, combining with a controlled network user feedback strategy, and identifying phishing emails and doubtful phishing emails. The Invention has advantages of high identification efficiency, low resource consumption and no error rate. The Invention can be configured to email servers, gateway servers and the like that require high real-time performance, protect the controlled network user against cheating by the phishing emails, resist the interference from the spiteful user in the controlled network, and can be widely applied to the application fields of network email filtering management, anti-phishing attack and the like.

The invention discloses a network user specific group identification method based on multi-relational graph characteristics. The method comprises the following steps: acquiring cluster behavior data of a plurality of network groups through a web crawler; constructing a labeling manual in a specific field, wherein the labeling manual comprises a large-scale data set of abnormal user and real user behavior data; determining abnormal user group behavior characteristics according to the suspicious abnormal group samples and existing normal user samples; researching a neighborhood node similarity measurement method, and constructing a user association graph based on a co-occurrence relationship; and establishing an abnormal group identification network from two aspects of content and behavior based on a relational graph convolutional neural network. The problem that an existing abnormal user identification method mainly depends on text features or simple social relations and is difficult to effectively utilize behavior association features of groups is solved, and the method can better aim at high-concealment specific groups.

The invention relates to a method for automatically generating and publishing all-media content, which belongs to the field of electric digital data processing, and comprises the following steps of: intelligently predicting the playing or opening type of a main image / audio / animation / webpage of a target residential user in a current time segment by adopting a content type prediction model; and automatically generating all-media content corresponding to the current time segment for the target residential user at a far-end all-media content server based on the intelligent prediction result. The invention further relates to an all-media content automatic generating and publishing system. According to the invention, the technical problem in the prior art that all-media content meeting watching requirements of users at different time periods cannot be automatically generated for each intelligent home network user is solved; the content type prediction model can be adopted to intelligently predict the playing or opening type of the main image / audio / animation / webpage of each intelligent home network user in the current time segment, and then the matched all-media content is generated, so that the technical problem is solved.

A machine learning-based proxy model may generate measurements of quality of user experience in a vehicle-based communication network in the absence of direct feedback from the users regarding the user experience. Once generated, the proxy model may be applied to observed operational parameters of the on-board network to quantify the user experience for any user in any given instance. User experience measurements (e.g., trends identified therein) may be utilized to identify and implement adjustments to hardware, firmware, software, and / or service procedures associated with implementation of the on-board network. These adjustments may be implemented between transits of the vehicle, or in some cases, during transit of the vehicle to improve the user experience over the duration of use of the vehicle-based communication network.

This invention discloses a network management method, device, electronic device, and storage medium, relating to the field of data processing technology. It includes training an initial classification model using a sample traffic feature dataset and corresponding classification targets. The model can fuse traffic features according to the classification targets and iterate model parameters using the model classification results and the actual classification results corresponding to the training data to obtain an actual classification model. This model then outputs the traffic classification result for any server network, enabling network resource allocation. This invention solves the technical problems in related technologies, such as the use of dynamic ports breaking the fixed mapping relationship between port numbers and applications, encryption techniques hiding payload content making it difficult to directly extract traffic features, affecting classification accuracy, and thus impacting network management effectiveness. It improves traffic classification accuracy in complex network environments, facilitating effective network management and ultimately enhancing the network user experience.

A device and method for employing a machine learning model using processor circuitry to intelligently predict user permissions within a network environment and output a restriction recommendation for modifying user permissions.

The invention belongs to an event analysis method, and provides a public opinion event propagation analysis method, a public opinion event propagation analysis system and a computer program product in order to solve the technical problem that a mathematical model is not fully used for exploring causes and a propagation mechanism in a network space in current research about moods of netizens. In different stages of the public opinion event, different models are adopted to analyze the propagation condition of the public opinion event, the progress of the public opinion event is divided according to whether bifurcation occurs, the network public opinion event is described in a mode of combining a chaos dynamics model and an infectious disease model, and the public opinion propagation process is analyzed in each stage. Therefore, reasons and essential reasons for changing network public opinion events into network violence events can be known fundamentally, and a strategy for controlling the trend of public opinions can be formulated based on the reasons. According to the method, the comprehensive model is improved, the public opinion process in which the network storm possibly occurs can be completely described, and in practical application, disposal suggestions can be given in combination with data analysis.

The invention relates to a method for providing a data analysis facility (DAF) with data relating to at least one user equipment, the method comprising: - a step of collecting, for a plurality of events in the network affecting at least one state of the user equipment, data relating to said at least one affected state; and - a step of providing the analysis facility (DAF) with, for at least one such state of the user equipment, at least one statistical datum obtained for the state by aggregating the data relating to the state collected during the collecting step, and a portion of said data corresponding to an integer X greater than or equal to 1 events among the most recent events of said plurality of events.

Aspects of the subject disclosure may include, for example, a device, including: a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations of: training a role-based agent to handle queries from users of a network; training a role-based foundation model to retrieve data relevant to a role of a user; receiving a query from the user; and providing the query to the role-based agent, wherein the role-based agent uses an associated role-based foundation model to process the query, collect relevant data from the network, and formulate an answer to the query. Other embodiments are disclosed.

One or more computing devices, systems, and / or methods for providing an artificial intelligence based communication system for network issue resolution are provided. Featured related to operation of a communication network are input into one or more models to generate a set of resolution recommendations related to a low signal detected with respect to the communication network. A resolution recommendation is selected and provided as solution content to users of the communication network and / or to agents troubleshooting issues for users. Post recommendation metrics are used to identify a user that has not implemented a resolution action described by the solution content. Accordingly, a follow-up notification is sent to the user as a reminder to perform the resolution action.

The application discloses a kind of method and device for determining network experience satisfaction, to solve the problem of inaccurate network experience satisfaction evaluation result.The scheme provided in the present application includes: obtaining the network experience satisfaction score and network use information of multiple network users;According to the network use information of multiple network users, clustering analysis is performed on multiple network users to obtain multiple groups of network users based on network service type classification, and the network service type represents the network function use preference of network users;Determine the network score weight of multiple network users according to the network service type to which multiple network users belong;According to the network score weight and network experience satisfaction score of multiple network users, determine the network experience satisfaction evaluation result.The scheme of the embodiment of the present application generates the network experience satisfaction evaluation result according to the use of network service by network users, effectively improves the accuracy of the generated evaluation result.

A system and method for training a security model in a testing system using machine learning is disclosed. For example, the method includes accessing information about security attacks from at least one security attack information source, recognizing types of security attacks being attempted using natural language processing to process the information about security attacks, attempting at least one of the recognized types of security attacks on a network, identifying at least one security vulnerability based on results of the attempting, training the security model based on the identified at least one security vulnerability using machine learning, providing the trained security model to a user of the network, and blocking a security attack using the trained security model.

In one embodiment, a method for detecting restricted cross-origin requests by a webpage is provided. The method includes: receiving, by a processor, webpage data associated with the webpage; determining, by the processor, a presence of cross-origin uniform resource locator (URL) data from the webpage data; in response to cross-origin URL data being present, generating, by the processor, an independent request for a resource directly to a server associated with the cross-origin URL; determining, by the processor, whether the resource was restricted by the server; and selectively generating, by the processor, mitigation data to mitigate presentation of the restricted resource associated with the cross-origin URL data.

In an example embodiment, machine learning is utilized to make recommendations for next actions by users of an online network. These next actions are called “next best actions.” The machine learning may be performed to train a multitask deep machine learning model to make recommendations based on a series of inputs, including, for example, contextual information that relies upon action sequences of the user and historical users, and user intent. The use of a multitask deep machine learning model allows for the model to generate action recommendations that are personalized, contextual, and coordinate across various different aspects of the online network, rather than being limited to only a single aspect. Likewise, the multi-task deep machine learning model can also be tailored to optimized different use-case specific objectives while at the same time being easy to scale and maintain.

Techniques are described for deploying a policy refiner application to analyze identity and access management policies created by users of a cloud provider network and recommend changes to those policies (e.g., to move the policies toward least-privilege permissions). A policy refiner application receives as input a policy to be analyzed and a log of events related to activities associated with one or more accounts of a cloud provider network. From the log of events, the policy refiner application can identify actions that were permitted based on specific instructions contained in the policy.Based on field values contained in the corresponding events, the policy refiner application generates an abstraction of the field values, where the abstraction of the field values can represent a more restrictive version of the field from a policy perspective. These abstractions can be presented to users as recommendations for modifying their policy to reduce the permissions granted by the policy.

A computer-implemented method for determining a periodic payment to users of a social network including. The method includes associating users of a cloud-based social network into a Merkle tree structure based on an order of membership referrals as to which user referred which to the cloud-based social network. The method includes quantifying an amount of digital engagement that a first user has with the cloud-based social network through an individual impact score. The method includes attributing all users that were referred to the cloud-based social network by the first user into the first user's sub-network. The method includes quantifying an amount of digital engagement for all users within the first user's sub-network has with the cloud-based social network through a sub-network impact score. The method includes making a recurring electronic payment to the first user on a periodic basis utilizing the individual impact score and the sub-network impact score.

A method and a device for controlling power Internet of Things flow, and a computer program product are provided. The method includes: determining a probability formula for reduction of network user flow, according to probability formulas of signal power over limit, poor quality of service, channel congestion and signal transmission delay occurring in each channel; determining a probability formula for cascading flow reduction of N network users; establishing a network user flow minimum control quantity function according to the probability formula for cascading flow reduction of N network users; determining a plurality of population particles according to a value range corresponding to the parameter of the network user flow minimum control quantity function; and determining the network user flow minimum control function as a fitness function, and optimizing population particles to obtain an optimal population particle. Power Internet of Things flow control is performed according to the optimal population particle.

The invention discloses a network management method and device, electronic equipment and a storage medium, and relates to the technical field of data processing, and the method comprises the steps: carrying out the training of an initial classification model through employing a sample traffic feature data set and a corresponding classification target, and enabling the model to carry out the traffic feature fusion according to the classification target, and performing iteration of model parameters by using a model classification result and an actual classification result corresponding to the training data to obtain an actual classification model, and further outputting a traffic classification result of any server network to realize network resource allocation, thereby solving the problem that a fixed mapping relationship between a port number and an application is broken due to the adoption of a dynamic port in related technologies, and the network resource allocation efficiency is improved. The method solves the technical problems that an encryption technology hides load content, traffic features are difficult to directly extract, the classification accuracy is influenced, and the network management effect is influenced, and improves the traffic classification accuracy in a complex network environment, so that effective network management is facilitated, and the network use experience of network users is improved.

Systems and methods for connecting people via a social networking system are provided. The systems and methods relate to meeting and connecting with people in real time both on-line and in-person. The users of the network are physically present in an establishment, where they can meet-up if the people mutually agree to do so. One embodiment involves creating a location-based connection between the users using geofences. Other methods use Quick Response (QR) codes and Near-Field Communication (NFC) tags to unlock the network and facilitate the meetings. The network can be used at various establishments including, for example, restaurants, bars, nightclubs, stores, hotels, casinos, sporting events, schools, libraries, coffee shops, and the like.

The application discloses a power grid safety emergency early warning method and system based on multi-source data fusion, relates to the technical field of power grid public opinion monitoring, and sets the public opinion influence grade and the public opinion tolerance of each power construction area, screens each public opinion transmission node of each power construction area, then monitors and evaluates the transmission influence of each public opinion transmission node, obtains the public opinion monitoring result of each power construction area, and finally executes a corresponding public opinion safety emergency early warning scheme, so that the key node can be accurately locked, resource waste caused by generalized monitoring can be avoided, the monitoring efficiency is improved, the emotional change track of the netizen who generates an interactive behavior in public opinion transmission is tracked according to the public opinion guiding effect, so that the actual situation of public opinion guidance can be judged, potential public opinion risks can be effectively identified, a targeted and efficient response strategy can be made when public opinion occurs, and the enterprise image can be effectively maintained.

Examples identify a user of a network as a possible contributor of digital content to a document, and identify different channels that are each capable of sending, to the user, an invitation for the user to contribute to the document. Respective channel usage data is determined, which includes historical data relating to use of the channel by the user to interact with content. Respective channel affinity scores are computed based on the respective channel usage data, where a channel affinity score includes an estimate of a likelihood of the user contributing to the document through the channel. Based on the respective channel affinity scores, an optimal channel is selected, and the invitation is sent to the user to contribute to the document through the optimal channel.

The invention discloses an application addressing method, device and system, relates to the technical field of communication, and aims to solve the problem that edge application discovery and edge network user plane change depend on and influence a session management function (SMF) network element, realize quick online of an edge application and simplify change of an edge network. The method comprises the following steps: a first function network element obtains edge application service EAS deployment information, and sends first information to an edge application service discovery function EASDF network element based on the EAS deployment information; and then, the EASDF network element receives a first request message from the terminal, and in order to respond to the first request message, the EASDF network element determines the EAS address of the first application service based on the first information. Furthermore, the EASDF network element sends the EAS address to the terminal, and correspondingly, the terminal receives the EAS address. The scheme of the invention can be widely applied to the fields of communication technology, artificial intelligence, Internet of Vehicles, smart home networking and the like.