13 results about "Web presence" patented technology

A method includes generating, by a large language model (LLM), a multitude of search queries from a user input term obtained from a user application. The method further includes, executing, by the LLM, the multitude of search queries to obtain a multitude of verified search results regarding a multitude of candidates. A multitude of web presence schemas corresponding to the multitude of candidates from the multitude of verified search results is generated. The method further includes web-scraping a multitude of websites of a subset of candidates selected from the multitude of candidates to obtain a set of corresponding web-scraping payloads. The method further includes merging the corresponding web-scraping payloads with corresponding web presence schemas of the subset of candidates to obtain a multitude of updated web presence schemas. The method further includes presenting the multitude of updated web presence schemas of the subset of candidates in the user application.

Systems and methods for detecting the presence of a body (typically a human) in a network without fiducial elements while additionally using fiducial elements to assist the system and possibly reduce the computational loading on the system. The fiducial element may be used in many ways including, without limitation, assisting in ignoring some bodies present in the detection area and training the system using additional data.

A system and method for improved application path selection for cloud-based applications from a client device. In one embodiment, a method includes collecting metrics of a remote user's use of one or more cloud-based applications, categorizing the one or more cloud-based applications based at least in part on the collected metrics, determining one or more secure network paths based at least in part on the categorizing of the one or more cloud-based applications, establishing the one or more secure network paths between the remote user and one or more network points-of-presence, and routing data traffic for the one or more cloud-based applications through the one or more established secure network paths.

The application discloses a collaborative office network security management method and system based on artificial intelligence and a medium, belongs to the technical field of artificial intelligence network security, and is used for solving the technical problem that when the existing collaborative office network has network abnormal conditions or network security events, the emergency mechanism mainly depends on manual intervention, and the response efficiency is low when data leakage or system paralysis is caused. The method comprises the following steps: through an edge computing node and based on an artificial intelligence engine architecture, performing edge preprocessing on initial office network data about adversarial samples to obtain a network feature matrix based on the initial office network data; through the artificial intelligence engine architecture, performing double-channel network anomaly detection processing on the network feature matrix to obtain network anomaly detection result data; and according to the network anomaly detection result data, performing corresponding network risk response actions and automatically generating a network anomaly solution strategy.

Disclosed in the present invention is a network and log intelligent management method, comprising the following steps: deploying a network monitoring tool and a log analysis tool in a target network; defining a network monitoring index, monitoring the target network in real time by means of the network monitoring tool, and acquiring log data by means of a log monitoring tool; performing fusion analysis on network monitoring information and the log data, and on the basis of the analysis result, determining whether the target network has had an anomaly; and defining an alarm mechanism, and issuing an alarm when the target network has had an anomaly. By implementing monitoring, the present invention can discover anomalies in a system and a network quickly, thus reducing the processing time, improving the availability and stability of the system and the network, and by means of monitoring, performing configuration and optimization on network resources; the present invention can identify and prevent potential problems by means of data analysis, and can perform fusion analysis on network monitoring information and log data, so as to further enhance the anomaly detection capability; and by means of establishing a robust monitoring and emergency alarm mechanism, the present invention can ensure continuity and stability of services.

The invention discloses a network resource calling optimization method and device. The method comprises the following steps: acquiring an atlas change event set and network service demand information of a target network in a preset time period; updating the network time sequence atlas of the target network according to the atlas change event set; judging whether the target network is abnormal or not according to the updated network time sequence graph; and under the condition that the target network is abnormal, analyzing the updated network time sequence atlas and the network service demand information by using a pre-trained strategy decision model to obtain a corresponding target network resource scheduling strategy, and optimizing the network resource scheduling of the target network according to the target network resource scheduling strategy. According to the method and the device, the technical problem that the real-time service requirement of a user is difficult to meet due to poor accuracy and timeliness of a scheduling result when network resource scheduling is carried out based on a regularly updated network static graph in the prior art is solved.

The invention relates to the technical field of network security, and particularly provides a traffic fingerprint detection method and device, a storage medium and electronic equipment, the method is applied to network equipment using a Bypass mode in an abnormal state, and the method comprises the following steps: receiving a service traffic fingerprint; detecting the service flow fingerprint by using a flow fingerprint detection tool to obtain a detection result; wherein the detection result represents whether the business flow fingerprint is abnormal or not; and under the condition that the detection result represents that the service flow fingerprint is normal, sending the service flow fingerprint to target equipment. According to some embodiments of the invention, security protection can be performed on the network in the Bypass mode, and the risk that the network is attacked is avoided.

This invention discloses a video anomaly behavior recognition method based on knowledge distillation using a relaxed divergence function. This method allows for a more relaxed matching of the outputs of the teacher network and the student network, thereby improving the knowledge distillation effect. The specific implementation method is as follows: First, consecutive video images are randomly extracted from the entire dataset and preprocessed as training samples. Then, these samples are fed into the proposed teacher network for training to obtain the optimal parameters. Next, the optimal parameters of the teacher network are used to distill and train the student network. Due to the significant capacity difference between the teacher network and the student network, the proposed relaxed divergence function is used to match the output confidence of the two networks. Finally, soft loss and hard loss are used as a joint loss function to backpropagate the error of the student network, thereby optimizing the parameters of the student network to achieve optimal results.

The invention discloses a collaborative office network security management method and system based on artificial intelligence, and a medium, belongs to the technical field of artificial intelligence network security, and is used for solving the problem that when an existing collaborative office network has a network abnormal condition or a network security event, an emergency mechanism mainly depends on manual intervention, and the network security is influenced. And the response efficiency is low when data leakage or system paralysis is easily caused. The method comprises the steps of performing edge preprocessing of related adversarial samples on initial office network data through an edge computing node based on an artificial intelligence engine architecture to obtain a network feature matrix based on the initial office network data; performing dual-channel network anomaly detection processing on the network feature matrix through an artificial intelligence engine architecture to obtain network anomaly detection result data; and executing a corresponding network risk response action according to the network anomaly detection result data, and automatically generating a network anomaly solution strategy.

The application discloses a network situation assessment method, device, equipment, storage medium and program product. The scheme specifically comprises the following steps: acquiring mirror data in a trapping network, the mirror data comprising attack behavior data of a target IP address and a trapping point identifier, and the mirror data being stored in the form of a data linked list; clustering the mirror data for each trapping point corresponding to the same trapping point identifier in a head node of the data linked list, to obtain attack behavior data categories included in the mirror data of each trapping point; calculating a risk value of the trapping point by using a vulnerability score of a network node corresponding to the trapping point and the data linked list, for each trapping point corresponding to the same trapping point identifier in the head node of the data linked list; calculating a network situation value based on the risk values of all the trapping points; and determining that there is a security problem in the network when the risk value of the trapping point is greater than a preset trapping point threshold value or the network situation value is greater than a preset situation threshold value. In this way, the network situation can be accurately evaluated.

The present invention proposes a CDN-based file resource management system and method. The file resource management method includes: configuring the CDN network according to resource management requirements set by the user; the CDN network screening the target edge node corresponding to the user based on the user's file resource upload parameters to determine the target edge node corresponding to the user; the CDN network sending and caching the real-time uploaded content information of the file resource to the target edge node within the CDN network, and the target edge node sharing the data with other edge nodes of the CDN network; real-time monitoring of the operating parameters of the CDN network and issuing an abnormality alarm when an abnormality occurs in the CDN network. The system includes modules corresponding to the steps of the method.

Systems and methods for detecting the presence of a body (typically a human) in a network without fiducial elements while additionally using fiducial elements to assist the system and possibly reduce the computational loading on the system. The fiducial element may be used in many ways including, without limitation, assisting in ignoring some bodies present in the detection area and training the system using additional data.

Systems and methods for beacon-based network management are disclosed. A first device is configured to transmit a broadcast message indicating a presence of a first network. The broadcast message can include credentials for a second network. A second device is configured to receive the broadcast message, retrieve the credentials for the second network, and access, via provision of the credentials to a third device, the second network.