16 results about "Web page categorization" patented technology

A network security system that classifies webpages and uses the classification of the webpages to enforce relevant security policies is disclosed. To classify a webpage, a screenshot of at least a portion of the webpage is captured. An embedding engine generates a subject image embedding of the screenshot. The subject image embedding is classified by an image classifier that includes an index of training image embeddings each having a label of their classification and an associated approximate nearest neighbors model trained to identify the label of the closest training image embedding to the subject image embedding and a score representing their similarity. The webpage is classified based at least in part on the score and the label from the image classifier. The network security system applies security policies to requests from client devices that identify the webpage as its destination based at least in part on the webpage classification.

A network security system that classifies webpages and uses the classification of the webpages to enforce relevant security policies is disclosed. To classify a webpage, a screenshot of at least a portion of the webpage is captured. An embedding engine generates a subject image embedding of the screenshot. The subject image embedding is classified by an image classifier that includes an index of training image embeddings each having a label of their classification and an associated approximate nearest neighbors model trained to identify the label of the closest training image embedding to the subject image embedding and a score representing their similarity. The webpage is classified based at least in part on the score and the label from the image classifier. The network security system applies security policies to requests from client devices that identify the webpage as its destination based at least in part on the webpage classification.

The invention discloses a webpage classification method and system based on a Delaunay triangulation double-graph neural network, and belongs to the technical field of webpage classification. The method comprises the steps that original graphs and node features of webpage classification are acquired; the original graph comprises a plurality of nodes and directed edges among the nodes, the nodes are webpages, the directed edges are hyperlinks among the webpages, and the node features are webpage contents; constructing a shadow graph by adopting a Delaunay triangulation method; respectively carrying out graph convolution operation on the original graph and the shadow graph to obtain local structure information and global relation information of nodes; and the local structure information and the global relation information are fused to generate final node representation, and all nodes are classified by adopting a neural network classifier based on the final node representation. According to the method, the graph expression capability and the information spreading efficiency are effectively enhanced and the webpage classification effect is improved on the premise of not destroying the original graph structure semantics.

Improved bot detection systems and methods are disclosed. A page fingerprinting algorithm can be used to categorize web pages. The categorization of web pages enables improved insights in profiling the way a human interacts with a website as opposed to a bot. Learning the patterns of humans and bots for a given web page category (or navigation across categories), a heuristic ruleset and / or machine learning system can differentiate between humans and bots. In this way, a human website visitor can be distinguished from a bot in an improved manner. The teachings hereof include systems and methods for deriving page fingerprints from makeup language files, for categorizing pages based on their structure and associated fingerprints, as well as heuristics and machine learning techniques to characterize website visitor behavior and detect bots, based on such web page categorization and fingerprinting.

The invention provides a webpage classification method and device and electronic equipment, and relates to the technical field of artificial intelligence, the method comprises the steps that attribute features of a uniform resource locator (URL) of a to-be-classified webpage are acquired, and the attribute features comprise at least one of component features, length features, orthogonal features and sequence features; the attribute features are input into a pre-trained first model, character features of the URL are obtained based on output of the first model, and the character features comprise at least two of character pattern features, character string sequence dependency features and content association features; performing feature fusion on the character features to obtain fused features; and classifying the to-be-classified web pages by utilizing the fusion features. According to the embodiment of the invention, the attribute features of the URL of the webpage are obtained, and the character features of the URL are extracted by using the first model, so that the comprehensiveness of capturing the complex features of the URL can be improved, the generalization ability and accuracy of a classifier can be enhanced, and the classification accuracy can be improved.

The present disclosure relates to a webpage classification method and device, medium and electronic equipment, to solve the problem that the webpage cannot be classified according to the webpage production mode in the related art. The method comprises: obtaining page information of a target webpage; matching the page information of the target webpage with a preset classification rule to obtain a target classification result of the target webpage, wherein the preset classification rule comprises a classification rule based on a webpage production mode, and the webpage production mode comprises at least one of a platform template production mode, a website building production mode and an independent station production mode.

Systems, apparatus, articles of manufacture, and methods are disclosed to classify data via tiered machine learning analysis. An example apparatus includes interface circuitry to access a latent space representation (LSR) of a first sample of a webpage, machine-readable instructions, and at least one processor circuit to be programmed by the machine-readable instructions. For example, the at least one processor circuit is to initiate a first artificial intelligence (AI) model to classify the webpage as benign or potentially malicious based on the LSR. Additionally, the at least one processor circuit is to, after the first AI model classifies the webpage as potentially malicious, initiate a second AI model to classify the webpage as benign or malicious based on a second sample of the webpage, the first AI model being less precise than the second AI model.

The invention relates to the technical field of natural language processing, in particular to a webpage English text similarity calculation method, which comprises the following steps of: obtaining a feedback weight of each directory keyword according to each directory keyword and all feedback keywords in each target English webpage browsing page; and according to all the unit texts, obtaining the commonly-used property of each unit text, obtaining the credibility of each type of feedback keywords according to the commonly-used property of each unit text, and obtaining the final word right of each directory keyword according to the feedback weight of each directory keyword and the credibility of each type of feedback keywords. Through the optimized word right extraction method, the clustering result of the English webpage browsing page contains key information of the English webpage core attribute, and the English webpage classification efficiency and the application value are greatly improved.

The invention discloses a webpage classification display method and device and electronic equipment, and belongs to the technical field of electronic equipment. The method comprises the following steps: acquiring first environment information under the condition of displaying a first webpage or opening the first webpage; according to the first environment information and the type of the first webpage, a first operation is executed on the first webpage in a target application window, and the first operation comprises at least one of the following items: opening the first webpage; displaying the first webpage; zooming out and displaying the first webpage; hiding and displaying the first webpage; under the condition that the first environment information indicates that the electronic equipment is in the preset environment, the type of the target application window is matched with the type of the first webpage.

The application discloses a page classification method, device, equipment, storage medium and product, and belongs to the computer technical field.The method comprises the following steps: obtaining a to-be-processed page; performing text classification processing on text information to obtain a text classification result; performing page jump information detection processing on the to-be-processed page to obtain a detection result; in the case that the text classification result indicates that the text information is a target text type and the detection result indicates that the to-be-processed page comprises page jump information, determining that a page type corresponding to the to-be-processed page is a target page type.The technical scheme provided by the embodiment of the application can obtain the text classification result and the detection result by performing the text classification processing and the page jump information detection processing on the to-be-processed page, and if the text classification result indicates that the text information is the target text type and the detection result indicates that the to-be-processed page comprises the page jump information, it is determined that the to-be-processed page is the target page type, thereby effectively improving the accuracy of webpage classification.

A system comprising one or more processors and one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, cause the one or more processors to perform operations: classifying one or more webpages of a website into one or more classifications using interaction data, a content score, and a link equity score, each for the one or more webpages and removing, based on the one or more classifications, the one or more webpages from the website and from a sitemap of the website. Other embodiments are disclosed herein.

The present disclosure provides a webpage classification method and device, electronic equipment and storage medium, relates to the technical field of data processing, in particular to the field of artificial intelligence and intelligent recommendation. The specific implementation scheme is: respectively extracting features of a uniform resource locator (URL) of a webpage to be classified and a hypertext markup language (HTML) corresponding to the URL, obtaining a first feature vector and a second feature vector corresponding to the webpage to be classified; preprocessing the first feature vector and the second feature vector to obtain a target feature vector; and determining the type of the webpage to be classified based on the target feature vector.

The invention relates to the technical field of computers, and provides a webpage tag generation method and device, equipment, a storage medium and a program product. The method comprises the following steps: acquiring internet behavior data of a plurality of users; the internet behavior data comprises to-be-classified webpage links and access entity interaction information; based on the access entity interaction information of each user, user grouping is carried out through a clustering algorithm, and a user group is generated; determining a group tag of the user group; and determining a webpage label of the to-be-classified webpage link based on the group label. According to the mode, the users in the same user group generally have similar access behavior modes, that is, the users in the same user group are more likely to access webpages of the same type, and the group label can reflect the classification of the webpages accessed by the user group, so that the user group can access the webpages in the scene of webpage content deficiency, and the user group can access the webpages in the scene of webpage content deficiency. The webpage label of the to-be-classified webpage link can be determined according to the group label, accurate classification of the webpage and accurate generation of the webpage label are realized, and the application scene is wider.

Improved bot detection systems and methods are disclosed. A page fingerprinting algorithm can be used to categorize web pages. The categorization of web pages enables improved insights in profiling the way a human interacts with a website as opposed to a bot. Learning the patterns of humans and bots for a given web page category (or navigation across categories), a heuristic ruleset and / or machine learning system can differentiate between humans and bots. In this way, a human website visitor can be distinguished from a bot in an improved manner. The teachings hereof include systems and methods for deriving page fingerprints from makeup language files, for categorizing pages based on their structure and associated fingerprints, as well as heuristics and machine learning techniques to characterize website visitor behavior and detect bots, based on such web page categorization and fingerprinting.

The application discloses a malicious webpage classification model construction method and system and a storage medium, wherein the method determines the category of a webpage through HTML content, thereby providing an accurate basis for subsequent classification; the average TF-IDF value of the text in the HTML label is calculated through a TF-IDF algorithm, thereby avoiding the interference of noise and irrelevant information; the HTML label and the text are combined, and the text is processed using a word segmentation algorithm, thereby providing a basis for subsequent representation and processing; the word vector is encoded and processed through a word embedding technology combined with an encoder, thereby realizing the deep representation and understanding of the text; finally, a malicious webpage classification model is constructed according to the text classification result, and the existing data of manually labeled webpage categories are used for training, thereby improving the accuracy of the malicious webpage classification model. The model constructed by the application does not need to be used in cooperation with other models, thereby reducing the required computing resources and reducing the complexity of model deployment.