109 results about "Netizen" patented technology

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.

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.

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 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 application provides a social network public opinion fast sensing and efficient tracing method and device. The method comprises the following steps: step one: obtaining a social network topology structure G, G=(V, E), wherein V represents a social network user node set, and E represents a social network user relationship set; step two: determining an optimal target observation user node set S according to the network topology structure G; and step three: determining whether an opinion event occurs and an initial propagation user node causing the opinion event according to information obtained by the target observation user node set S. Through the method provided by the application, the fast sensing and efficient tracing of opinion information can be realized under a small observation node set S.

The application discloses a method for verifying a Fabric complex endorsement strategy in cross-chain, wherein the Fabric blockchain first registers an MSP list, a chain code ID and an endorsement strategy description into a cross-chain network, transmits a generated cross-chain transaction to a relay chain, then the relay chain analyzes a transaction execution result and a set of endorsement node signatures, generates an endorsement strategy interpreter, and confirms that the transaction execution result is consistent with the chain code ID registered in the cross-chain network, and if not, the verification fails, and finally the endorsement strategy interpreter verifies the set of signatures. The application can verify a complex endorsement strategy customized by a user, is friendly to a user of the Fabric network, does not need to write a verification strategy by itself, and only needs to submit an endorsement strategy description, and can prevent a user from performing malicious attacks through different chain codes.

The application discloses a network security risk assessment method and system based on permission acquisition probability reasoning, and belongs to the technical field of communication network security. Firstly, the application accurately describes input network topology information, hardware and software deployment information, vulnerability information and other basic information in a formalized manner; then, network user permission information and permission dependency information are extracted, and a user permission conversion graph is constructed in combination with user permission importance and vulnerability exploitation difficulty; next, the existing user permissions are continuously utilized to reason about the permissions that the user can obtain next; finally, the probabilities of the attacker obtaining various user permissions are calculated, and network risks are quantitatively evaluated. According to the network basic information, the application can reasonably evaluate the current network security risks, and the network security indexes can effectively reflect the changes of the network security protection state.

The embodiment of the invention provides a network equipment background access control method, a program product, equipment and a medium, and relates to the technical field of network security. The network equipment background access control method is applied to target network equipment, and the target network equipment is provided with a network user interface Web UI. The method comprises the following steps: acquiring a background access control instruction input by a first user through the Web UI interface; wherein the background access control instruction is obtained according to a background operation command selected by a second user; and performing security check on the background access control instruction, and executing the background operation command under the condition that the background access control instruction passes the security check to obtain a command execution result. According to the embodiment of the invention, the technical effect of effectively ensuring the background access security of the network equipment can be realized.

The application belongs to the field of network security management, relates to data analysis technology, and is used for solving the problem that the prior art cannot linearly track from the perspective of an operation user and overall risk assessment of short-term behavior of the same network user, and particularly relates to a network adaptive management system based on dynamic network security, which comprises a behavior monitoring module, a security analysis module and a risk analysis module connected in sequence in communication, and the behavior monitoring module, the security analysis module and the risk analysis module are all connected in communication with a database; the application can finely and weightedly quantitatively evaluate the security of each combination element; the evaluation method fully considers the differentiated influence of different security attributes such as permission, privacy and network fluctuation on the security of network behavior, so that the finally calculated security coefficient of the combination element is more representative and accurate.

The present invention relates to the technical fields of artificial intelligence and social networks, and discloses a multi-channel social network circle recognition device and method based on enhanced network contrast constraints. The device includes an input module, a solution module, and an output module. The work of the input module is data preprocessing, which abstracts the multi-channel social network existing in the real world into a multi-layer network structure. The solution module includes a network generation model and a node representation model. Based on the node representation model, the low-dimensional representations of the learnable enhanced network and the original input network are calculated, and based on the two obtained low-dimensional representations, the loss function is solved, and the parameters of the network generation model and the node representation model are optimized in reverse, so that the device has higher accuracy and robustness. Finally, the obtained multi-layer network low-dimensional representation is imported into the output module, and the community division is obtained by using Kmeans. In essence, the obtained community division corresponds to a social circle formed by a group of network user accounts in the real world.

The invention discloses a power grid safety emergency early warning method and system based on multi-source data fusion, and relates to the technical field of power grid public opinion monitoring. Netizen inclusion degrees and public opinion influence levels of all power construction areas are set, and all public opinion propagation nodes of all the power construction areas are screened out; then, the propagation influence condition of each public opinion propagation node is monitored and evaluated, the public opinion monitoring result of each power construction area is obtained, and then a corresponding public opinion safety emergency early warning scheme is executed, so that key nodes can be accurately locked, resource waste of generalization monitoring is avoided, the monitoring efficiency is improved, and the public opinion safety emergency early warning effect is improved through the public opinion guiding effect. The emotion change track of the netizens generating interactive behaviors is tracked in public opinion propagation, so that the actual condition of public opinion guidance is judged, potential public opinion risks can be effectively recognized, a highly-targeted and efficient coping strategy is made when public opinions occur, and the corporate image is effectively maintained.