58 results about "Web API" patented technology

The invention provides a Web API recommendation method based on a multi-task deep neural network. The Web API recommendation method comprises the following steps: S1, cleaning a Mashup description document and a Web API description document in a data set; s2, mapping character information in the cleaned data set into digital codes; s3, generating sample data based on digital coding; s4, constructing a Web API recommendation model based on the multi-task deep neural network; s5, inputting the sample data into the Web API recommendation model for pre-training to obtain a pre-trained Web API recommendation model; s6, performing fine adjustment on the pre-trained Web API recommendation model by using a Mashup and API matching score prediction task; s7, obtaining a matching score list of the Mashup demand and each Web API (Application Program Interface); and S8, sorting the matching score lists of the Mashup requirements and the Web APIs in a descending order according to the matching scores, and selecting the first K Web APIs to form a final recommendation list. According to the method, the problem that no-interaction or low-interaction Web APIs in historical data are difficult to recommend accurately is solved, and indexes such as the recall rate and the accuracy rate of recommendation are improved.

The invention provides a Web AP I asset automatic mining system and method based on an IAST technology, and the system comprises a flow agent module which is used for capturing original flow data between a Web client and a server through a flow bypass agent; the flow cleaning module is used for cleaning and filtering the original flow data and screening out target flow data related to the Web AP I request; the framework identification module is used for identifying the framework type of the Web AP I request according to the data characteristics of the target flow data; the information extraction module is used for extracting asset information in the Web AP I request according to the frame type of the Web AP I request, and the asset information comprises an authentication parameter, a resource identifier and an authority identifier; and the asset library module is used for storing the asset information after duplicate removal and providing a management interface.

An authorization server, method, and non-transitory computer readable medium for generating and managing at least one access token associated with a client. The authorization server may include a memory configured to store computer readable instructions; and processing circuitry configured to execute the computer readable instructions to cause the authorization server to, compute an encryption key based on information associated with a user session, embed the encryption key into the at least one access token, map, within a database, the at least one access token to an access token handle associated with the client, return the access token handle to the client, and selectively provide the access token to at least one web Application Programming Interface (API) in response to receipt of the access token handle from the at least one web API.

A generative artificial intelligence (AI) pipeline has been created that employs aspects of natural language processing (NLP) to detect intents of web API calls and then summarizes the behavior expressed by the collective of intents. The pipeline uses a lightweight language model for intent classification of URLs corresponding to API calls in a time interval. The pipeline associates the intent classifications with metadata corresponding to the URLs and feeds this into another lightweight language model that summarizes the intent classifications and metadata. The natural language summarization describes exhibited behavior that can be understood by a wider audience than security experts. The capability to detect intents of API calls occurring in network traffic increases visibility and control of user behavior, particularly in Software-as-a-Service (Saas) environments. Furthermore, the enhanced visibility of user behavior with the created pipeline recognizes new and previously unseen API calls from live network traffic at enterprise scale.

The invention relates to the technical field of computers, and particularly discloses a Web API (Application Program Interface) recommendation method based on graph diffusion reconstruction and graph contrast learning, which comprises the following steps of: constructing a Mashup-API calling relation graph based on historical calling information; performing data expansion based on graph diffusion on the Mashup-API calling relation graph so as to generate a diffusion matrix of the Mashup-API calling relation; reconstructing the diffusion matrix by using adaptive graph reconstruction so as to generate a comparison view; a Mashup-API calling relation graph and a comparison view are obtained, the trained Light GCN is used as a basic graph encoder to process the Mashup-API calling relation graph and the comparison view, and a final feature representation vector of the node is generated; and sorting the APIs meeting the Mashup requirements based on an inner product between the feature representation vectors of the Mashup nodes and the feature representation vectors of the API nodes so as to generate a recommendation list. According to the method, the Mashup-API calling relation graph is expanded by adopting graph diffusion and an adaptive graph reconstruction model, so that the problem of sparse historical interaction data between the Mashup and the API is solved, and meanwhile, the recommendation accuracy is improved.

The invention discloses a multi-dimensional information traceability product quality control method, which comprises the following steps: acquiring basic data, and processing and analyzing the basic data; according to preset parameters, inputting the unique number of the product, and performing security processing on the basic data; setting a hyperlink capable of jumping to a detection analysis result page of the corresponding product; respectively accessing a plurality of access systems through the authenticated Web API interface; according to the method, the original data are decoded by utilizing the matched decryption algorithm, the original data are recovered, unified management is carried out through the data integration platform, multi-system fusion and safe and efficient whole-process tracing can be realized through the method, and the accuracy and the tracing efficiency are improved.

The invention discloses a GB / T28181 monitoring video stream efficient transmission and decoding method based on HTTP3 (Hyper Text Transfer Protocol 3) and electronic equipment. According to the method, the problems of high delay and high resource consumption of a traditional GB / T 28181 video monitoring Web end playing scheme based on WebSocket are solved by utilizing a new generation Web API (Application Program Interface) of WebTransport and WebCodec. According to the invention, through the UDP transmission capability of WebTransport based on HTTP / 3, the direct transmission of the RTP data packet from the server to the Web client is realized, and protocol conversion does not need to be carried out; the WebCodes is adopted to realize efficient hardware acceleration decoding of the client; and meanwhile, a multiplexing real-time monitoring and self-adaptive video quality control mechanism is provided. Compared with the traditional scheme, the method has the advantages that the end-to-end delay is remarkably reduced, the resource consumption of the server is reduced, the concurrency capability is improved, the video parameters can be dynamically adjusted according to the network condition, and the method is suitable for video monitoring application in various network environments.

The embodiment of the invention provides a Web API defect analysis method and device based on network packet capture, equipment and a medium. The method is applied to the technical field of data analysis, and comprises the following steps: acquiring business data of a URI specified by a client business module, an http session of the URI specified by a client HTTP Client library, a network data packet of the URI specified by a server Web Server and business data of the URI specified by a server business module in a preset time period, analyzing the http session and the network data packet to obtain corresponding business data, and sending the business data to the server business module. And finally, comparing each piece of service data, and determining whether the service request sent by the client is consistent with the service request received by the server, so that Web API defect investigation can be realized without changing the service code of the server, URI can be specified for analysis, server software does not need to be changed, the operation performance of the server software is not influenced, the storage pressure is reduced, and the service efficiency is improved. The time cost is saved; and the fault risk in the production environment is reduced.

The invention relates to a webpage API (Application Program Interface) depth discovery method, which comprises the following steps of: injecting a self-defined JS (JavaScript) code into a chromium browser which is started in a headless mode, triggering a webpage event and realizing request interception and hijack monitoring, and gradually triggering and completely traversing a functional process aiming at DOM0 and DOM2 events in the webpage code, meanwhile, a specific execution result is returned through a special DOM event of a HOOK part, the webpage is locked to avoid jumping in the triggering process, the DOM event on the webpage is triggered as completely as possible with the assistance of a field automatic filling function, and meanwhile, URL requests of webpage event functions are recorded and summarized. Compared with the prior art, the method has higher API discovery capability; according to the model, a browser event triggering and function HOOK technology method is adopted, the problems that a vulnerability scanner discovers and captures webpage URL paths and APIs are missing and incomplete are well solved, and the capacity of discovering the APIs and the vulnerabilities in the field of automatic vulnerability discovering is improved.

Approaches disclosed herein relate to dynamically determining and assigning user interface (UI) interaction modes across multiple multimodal devices based on real-time detection of input methods and screen sizes. Input methods are detected using web APIs. A screen size is determined using CSS pixel measurements and monitored for changes. CSS media queries are used to detect the effective resolution of the device's display. By applying breakpoints defined in CSS, the screen size can be categorized into predefined groups. The device state may be continuously monitored in real-time to identify any modifications, such as connecting or disconnecting input devices. Events triggered by these changes may be captured using the web APIs and event listeners. Such a system can listen for events like connecting or disconnecting devices to dynamically reassign the interaction mode. Such reassignment can be guided by an algorithm that prioritizes input methods based on the device's screen size and context.

The invention belongs to the technical field of recommendation, and particularly relates to a Web API recommendation method based on deep learning enhancement, in particular to a Web API recommendation enhancement multi-task fusion model (EMTFM-BERT) based on BERT and oriented to mixed applications. According to the method, multi-model fusion and multi-task learning are combined with a BERT pre-training model and a fine tuning technology thereof. The proposed solution is divided into three key stages: 1, constructing a recommendation model; secondly, training the model by using the usage data of the Web API and the hybrid application collected from the service provider; and thirdly, processing natural language input submitted by a developer or a user. Based on these inputs, the system generates a set of recommended Web APIs and proposes relevant tags or categories that meet specified requirements.

A device (120) comprising the following: at least one memory (122) in which computer-readable instructions are stored which, when executed by at least one processor (124) coupled to the at least one memory (122), cause the at least one processor (124) to: to receive data from a web service client (150), wherein the data includes at least one operational request associated with a vendor-neutral application programming interface (API) procedure, wherein the at least one operational request includes at least one request to configure a tenant VPC in a Smart NIC (132), and wherein the vendor-neutral API procedure includes a RESTful data model (Representational State Transfer Web API); which modify at least one operational requirement to match a Fabric data model; Decomposing the modified at least one operation requirement into a multitude of operations that act on data associated with a vendor-specific API method, where the vendor-specific API method differs from the vendor-neutral API method; and Performing the multitude of operations that act on the data using the vendor-specific API method for passing to the vendor firmware (160).

The invention provides an API calling sequence identification method and device, equipment and a storage medium, and relates to the technical field of computers. The method comprises the following steps: acquiring API flow of a network API interface; session IDs existing in the API traffic are identified, the API traffic is grouped according to the session IDs, a plurality of API traffic groups are obtained, and one session ID corresponds to one API traffic group; for each API flow group, extracting an effective API calling sequence; and performing sequence pattern processing based on the effective API calling sequence of each API flow group to obtain an identification result of the API calling sequence. According to the embodiment of the invention, complete sorting of the API calling sequence in the WEB application is realized, enterprise personnel can be helped to quickly understand potential business logic in a business system, security operation personnel can be helped to quickly analyze all behaviors of accessing the WEB API interface, and all hidden dangers of the WEB API interface are reduced.

The present invention designs a request parameter conversion method for Web API calls, which involves the fields of Web API intelligent matching and Web API recommendation. By dividing request parameters into seven types and utilizing the dependencies between request parameters, a new Web API that meets development needs can be recommended to developers more accurately based on the request data and specified return data provided by the developer. The method can also help developers solve the request parameter conversion problem when automatically replacing an invalid Web API with a new one, thereby achieving the goal that when a Web API becomes invalid, a new Web API can be used to automatically replace the invalid Web API without reading the development documentation of the new Web API and providing specified request parameters, thereby simplifying the software development process while ensuring the same system effect.

An authorization server, method, and non-transitory computer readable medium for generating and managing at least one access token associated with a client. The authorization server may include a memory configured to store computer readable instructions; and processing circuitry configured to execute the computer readable instructions to cause the authorization server to map, within a database, at least one access token to an access token handle associated with a client, return the access token handle to the client, and selectively provide the access token to at least one web Application Programming Interface (API) in response to receipt of the access token handle from the at least one web API.

The application discloses a Web service representation method based on multi-view multi-level contrast learning, and comprises the following steps: acquiring a programmable Web dataset; acquiring Mashup text description and Web API text description based on the programmable Web dataset; inputting the Mashup text description and the Web API text description into a Sentence-BERT model to acquire Mashup text representation and Web API text representation; inputting the programmable Web dataset into a Web service interaction network model to acquire Mashup structure representation and Web API structure representation; respectively performing global contrast learning on corresponding text representation and structure representation to acquire corresponding global feature information; and adding the corresponding global feature information to acquire a Web representation vector. The method simultaneously considers feature information of Web services under multiple views, and extracts features in combination with multi-level contrast learning.

The invention discloses a cold start service recommendation method based on a multi-strategy pre-training model. The cold start service recommendation method comprises the following steps: step 1, constructing a hypergraph based on labels, extracting cold start service features, and constructing service embedding; step 2, obtaining enhanced service embedding based on hypergraph contrast learning of singular value decomposition; step 3, obtaining service embedding based on content optimization based on comparative learning of the description text; and 4, carrying out fine adjustment on the cold start recommendation model through a downstream recommendation task. The cold start service recommendation method based on the multi-strategy pre-training model is designed by analyzing and optimizing data features of Web APIs without historical interaction records and taking a hypergraph neural network as a technical support, and aims to solve the problem of recommendation of APIs without any historical interaction information and optimize the recommendation performance of services.

A cybersecurity appliance monitoring application traffic to a web application programming interface (API) dynamically updates tree structures for the web API using the application traffic. An API tree generator generates batches of API trees from paths indicated in the application traffic. An API tree merger / pruner updates the generated batches of API trees with various merging, pruning, compacting, and malicious detection operations on the generated batches of API trees. The cybersecurity appliance implements the updated API trees with an API agent that filters the application traffic prior to processing by the web API.

The invention discloses a method for detecting interface calling time consumption and a related device, and relates to the field of server operation and maintenance. The Spring interceptor of the server side responds to the received Web interface calling request sent by the user side, and the request time of the Web interface calling request is stored as the interface calling starting time; when it is detected that the processing of the Web interface calling request is ended, storing the processing ending time as interface calling ending time; on the basis of the interface calling starting time and the interface calling ending time, interface calling time consumption corresponding to the Web interface calling requests is determined, so that the time consumption condition of each Web interface calling request is detected in real time, and a maintenance basis is provided for operation and maintenance personnel.

According to an embodiment, a generation device includes an analysis unit, a test scenario generation unit, and a test code generation unit. The analysis unit is configured to analyze a definition file that defines a structure of web application programming interface (Web API) and generate structure analysis data of the Web API. The test scenario generation unit is configured to generate test scenario data of the Web API from a test setting file and the structure analysis data. The test setting file including a test type of the Web API and a test item depending on the test type. The test code generation unit is configured to generate test code for testing the Web API based on the test scenario data.

The invention provides an automatic OpenAPI specification generation method and system based on LLM, and belongs to the field of artificial intelligence, and the method comprises the steps: S1, designing a multi-agent workflow, and recognizing a server-side technology stack of a Web application program to be analyzed by using the LLM, thereby generating a cue word template; s2, based on the cue word template, acquiring corresponding code semantics by using LLM; designing a corresponding multi-agent workflow, and extracting an API endpoint of the Web application program to be analyzed; s3, based on the cue word template, acquiring corresponding OpenAPI knowledge by using LLM; and designing a corresponding multi-agent workflow, generating an API endpoint request parameter and a response specification, and performing correction and integration to obtain a complete OpenAPI specification. According to the method, under the condition that the API is triggered without depending on the client of the Web application program, the Web API is found in an active mode, and the OpenAPI specification is generated.

The present invention discloses a method, system and medium for judging the performance of API interfaces based on link tracing. The method includes: each agent accesses the corresponding distributed service without code intrusion, actively scans all Web API interfaces in the distributed service when the distributed service is started, obtains the full amount of static data of the Web API interfaces and reports it to the center; obtains the HTTP requests of external services, realizes link tracing through each agent, and obtains the link tracing data of each HTTP request reported by the agent to the center at regular intervals; calculates the average performance indicators of each Web API interface by using the interface-based aggregation method; the center calculates the interface behavior characteristic data according to the link tracing data; the center uses a comprehensive judgment model to conduct a comprehensive evaluation based on the full amount of static data, average performance indicators and interface behavior characteristic data, and performs timely response processing on the interfaces corresponding to the key attention scores.

The invention relates to the field of network security, and provides an automatic Web API authentication type vulnerability scanning method and system, a system to be tested is accessed in a bypass mode, an authentication mechanism, an authentication parameter item and an authority identification item used by a Web API of the system to be tested are analyzed, preposed information required by authentication vulnerability scanning is collected, and an API asset library is constructed; based on an attack scheduling strategy, adopting an authentication item variation and identifier variation technology, adaptively constructing authentication item attack requests and identifier variation attack requests in different scenes from an attacker perspective, and initiating a vulnerability scanning test; and according to the original request response, the authentication item attack request response and the identifier variation attack request response of the Web API, determining a vulnerability scanning result based on a response feature identification technology and a similarity detection technology, and generating a vulnerability scanning report. According to the method, the automation level and efficiency of Web API authentication type vulnerability scanning can be remarkably improved, and the Web API security assessment period is shortened.

An authorization server, method, and non-transitory computer readable medium for generating and managing at least one access token associated with a client. The authorization server may include a memory configured to store computer readable instructions; and processing circuitry configured to execute the computer readable instructions to cause the authorization server to, compute an encryption key based on information associated with a user session, embed the encryption key into the at least one access token, map, within a database, the at least one access token to an access token handle associated with the client, return the access token handle to the client, and selectively provide the access token to at least one web Application Programming Interface (API) in response to receipt of the access token handle from the at least one web API.

Methods, systems, and computer-readable storage media for receiving a request of an API call from a client, the request including a query, determining a score for the request, the score representative of a burden that the request would place on technical resources, if the request were to be executed, in response to the score, providing a response including a set of actions without executing the request, orchestrating a set of API sub-calls based on the set of actions, each API sub-call returning a sub-call response, merging the sub-call responses to the set of API sub-calls to provide a final result, and returning the final result in response to the API call to the client.

This invention provides a Web API recommendation method based on functional semantics and structural interaction, belonging to the field of Web API recommendation technology in the Web environment. It solves the problem of excessively short descriptions of existing Mashup service requirements. This Web API recommendation method based on functional semantics and structural interaction includes the following steps: Step S1: Collect description data from existing Mashup applications and their related API services, and extract textual description information of the Mashup applications and API services; Step S2: Preprocess the textual description information of the Mashup applications and API services; Step S3: Construct a Mashup-API bipartite graph based on the collaborative relationships between API services used in the Mashup application; Step S4: Construct functional semantic components and extract functional semantic features from the textual description information; Step S5: Construct structural interaction components and extract potential implicit correlations in sparse interactions; Step S6: Integrate multi-model training and integrate different types of feature descriptions. This invention has advantages such as reducing the impact of data sparsity on recommendation results.

A method for detecting Web API vulnerability attacks includes the following steps: Step S1, traffic extraction and parsing; Step S2, constructing user behavior sequences; Step S3, linguisticizing service interaction sequences; Step S4, inputting HTTP transactions and user behavior sequences into an intelligent detection engine, and performing traffic semantic detection and behavioral intent detection in parallel; Step S5, obtaining vulnerability attack alarm information; The detection system includes: a traffic extraction and parsing module; a user behavior sequence construction module for constructing user behavior sequences; a service interaction sequence linguisticization module; an intelligent detection engine module for identifying vulnerability attacks based on the HTTP transactions; a behavioral intent detection unit for reasoning and vulnerability attack identification; and an alarm output module; The electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program executing the steps of the detection method; It has the characteristic of accurate detection.

The present invention belongs to the field of recommendation. Specifically, according to the requirements input by users, it recommends web API services that meet the users' needs, improving the accuracy and personalization of recommendations. It mainly includes four steps: A. Mashup clustering; B. User link prediction algorithm; C. Collaborative filtering algorithm based on link prediction; D. Popularity prediction of web API. E. Web API recommendation model. The present invention combines link prediction and collaborative filtering, improving the accuracy of recommendations while also enhancing the personalization of recommendations, making the results of recommendations more in line with the actual needs of users.

The invention discloses a method, system, medium and equipment for constructing a large amount of test data based on Web API, and the method comprises the following steps: S1, designing a demand specification; s2, a test case; s3, judging whether preset data of the test case is greater than a preset value or not; s4, if it is judged that the preset data of the test case text is not larger than the preset value, ending; if it is judged that the preset data of the test case text is larger than the preset value, the test data size is determined, then the relevant api interface is matched, the test device is switched to run the api interface, and a large amount of needed test data is obtained through running of the api interface. The method has the advantages that for the test case needing to construct a large amount of test data, the large amount of test data can be rapidly constructed by designing a demand specification and analyzing a service scene, starting from the test case, combining a related api interface and test equipment, inputting parameters, outputting results and using the test equipment to run the api interface, the test efficiency can be effectively improved, and the test efficiency is improved. And the accuracy of a test result can be further ensured.

The embodiment of the invention relates to the technical field of information, and discloses an interface security test method and device, a storage medium and a product, the interface security test method comprises the steps that a plurality of vulnerability detection plug-ins are constructed, the vulnerability detection plug-ins correspond to different interface security test categories, and the vulnerability detection plug-ins comprise basic principles of vulnerability types; obtaining interface information of the target interface and an interface associated with the target interface through the first intelligent agent; constructing a vulnerability scanning process through the first agent according to the interface information and the vulnerability arrangement information; performing vulnerability scanning on the target interface through the second agent according to the vulnerability scanning process to obtain a scanning result; therefore, by means of the endogenous ability of multiple agents, the workflow of a human penetration test engineer is simulated, through multi-step Web API calling, fine and meticulous vulnerability verification conforming to human thinking is achieved, the accuracy rate far higher than that of a traditional vulnerability scanner is achieved, and multiple vulnerability types difficult to cover by the traditional scanner can be covered.