1312 results about "Abstract syntax tree" patented technology

The invention provides a large language model-driven code review and optimization method, which belongs to the field of computer science, and comprises the following steps of: firstly, performing intensive training on a model through rich code samples and accurate manual annotation to ensure that the model has accurate code understanding and analysis capability; and then, performing deep analysis on an input source code by utilizing the trained large language model, and constructing an abstract syntax tree (AST) to capture deep semantic information of the code. And finally, the model automatically analyzes the codes, identifies potential defects, non-standard coding practices and optimization possibilities, and generates specific optimization suggestions, including code repair schemes and guidance following optimal practices, according to identification results, so that efficient support is provided for developers, and the quality of software products is ensured.

The invention relates to the technical field of code security analysis, and discloses a control flow analysis and retrieval enhancement-based large model code security review method, which comprises the following steps of: analyzing an incremental code to generate an abstract syntax tree and data flow analysis information; retrieving local knowledge base association business rules and historical vulnerabilities based on grammatical features, and generating a context enhancement prompt; calling a large language model to jointly analyze codes and contexts, identifying vulnerabilities and outputting a structured report; and combining with a historical false alarm data optimization result and then integrating to a development assembly line. Multi-source information is fused through an RAG technology to enhance semantic understanding, and control flow node tracking and cross-version semantic association are combined, so that the problems of a traditional tool business logic vulnerability detection blind area, incremental code analysis failure and high false alarm rate are solved, synchronous improvement of security examination accuracy and efficiency is realized, and the method is adaptive to an agile development scene.

The invention relates to the technical field of software development, and particularly provides a self-adaptive code annotation updating method and system, a terminal and a storage medium, and the method comprises the following steps: confirming that codes are updated, extracting an updated code set, and combining and constructing an abstract syntax tree for the codes; performing semantic analysis on the abstract syntax tree by using a deep learning model to generate a semantic vector, and constructing a program dependency graph based on the semantic vector; analyzing a dependency relationship among code elements in the program dependency graph by utilizing a graph neural network; and generating a code annotation based on the semantic vector and the dependency relationship between the code elements by using a natural language generation model. According to the method, the real-time performance and the accuracy of the code document are ensured, the readability and the maintainability of the code are greatly improved, and the maintenance cost is reduced.

The invention provides a method for generating an SQL (Structured Query Language) by a natural language based on bidirectional mapping and semantic parsing, which relates to the technical field of database query and comprises the following steps of: extracting natural language query elements and packaging the natural language query elements into structured data, and establishing a mapping relationship from a query field to a service attribute and a physical data table by adopting a bidirectional Hash index technology; and automatically identifying multi-table association keys, performing semantic extension and compliance verification, generating an abstract syntax tree, performing processing according to user permission, and finally converting the abstract syntax tree into an SQL statement conforming to a target database syntax specification. According to the method, the accuracy and the efficiency of converting the natural language into the SQL are improved, and the flexibility and the safety of the system are enhanced.

The present disclosure relates to a system and method for vulnerability localization based on deep learning, which comprises, at a minimum, a processor configured to: analyze a code file under detection to obtain a first abstract syntax tree devoid of semantic information; build upon the first abstract syntax tree by incorporating data-flow edges and / or control-flow edges, thereby forming a second abstract syntax tree with semantic-flow enhancement; split the second abstract syntax tree to obtain a plurality of second abstract syntax sub-trees; and input these second abstract syntax sub-trees into a pre-established vulnerability detection and localization model. Compared with existing code vulnerability detection methods, the present disclosure employs a semantically-enhanced abstract syntax tree and finely-grained segmentation thereof, enabling both the efficient detection and accurate localization of code vulnerabilities, characterized by swift detection rates, low false positive rates, and commendable interpretability of the detection results.

The invention discloses a method for automatically generating a dynamic language test case based on a large language model, which comprises the following steps of: firstly, in a preprocessing stage, arranging related information of a to-be-tested program into prompts, analyzing a parameter type of the to-be-tested program by the large language model according to the prompts, and generating code information required by a test through an abstract syntax tree; in the test case generation stage, a dynamic multi-objective optimization algorithm is adopted, test cases are continuously crossed and mutated through branch coverage feedback, in the partial evolution process, test data which cannot be generated by conventional variation are generated by utilizing a large language model, and finally, the screened excellent test cases are organized into a complete test suite. In a program repair phase, the test suite is run to detect potential errors, the errors are analyzed and iteratively repaired using a large language model, and an executable and error-free test suite is generated. According to the method, the test coverage rate of the dynamic language is remarkably improved, and the achievement of a coverage target is accelerated.

The invention discloses an auxiliary code review method and system based on a large language model, and the method comprises the steps: analyzing a submitted code file based on a static analysis technology and an abstract syntax tree AST extraction tool, and extracting key elements; reviewing the code through a pre-constructed company internal and external knowledge base by relying on a knowledge graph technology to obtain a reviewing result; performing context analysis on the code by adopting a large language model to obtain an analysis result; integrating the review result and the analysis result, and generating a review report through NLP; a containerization technology is adopted to be integrated with plug-ins of mainstream development tools, and private deployment is supported. According to the method, the efficiency, accuracy and safety of code review can be greatly improved, the workload of manual review is reduced, and automation and intelligentization of the code review process are realized.

The invention provides a cross-frame code migration method and system, electronic equipment and a storage medium, and the code migration method comprises the steps: carrying out the frame-level conversion of a first frame code, and generating a target engineering skeleton adaptive to a second frame protocol; analyzing the construction configuration and the entry file, determining an entry point and a compiling rule of code migration, analyzing the first frame code through an abstract syntax tree, and generating a target code range list needing to be migrated; and according to the target code range list and the dependency item list, applying a preset conversion rule to the first framework code, and generating a second framework code adaptive to a second framework protocol. Through automatic framework-level conversion and dynamic rule adaptation, rapid migration from the first framework code to the second framework code is realized, a multi-terminal code library is unified, the manpower development cost is reduced, synchronous operation of business iteration and migration is supported, and the development efficiency is improved.

The invention discloses an intelligent data acquisition and conversion method based on a visual rule model, which comprises the following steps: S1, converting a configured rule chain into a data body in a JSON format by a visual rule modeling layer, analyzing the data body into an AST abstract syntax tree, and performing field mapping on the AST abstract syntax tree, the mapping fields are converted into executable codes through an intelligent conversion engine, and an execution plan is generated; s2, the intelligent acquisition layer pulls data from the heterogeneous data source, cleans the data and then allocates the data to an intelligent conversion engine for parallel processing; and S3, the intelligent conversion engine monitors the data consanguinity map and the field-level conversion path in real time, and sends a signal to the visual rule modeling layer or the intelligent acquisition layer according to the abnormal condition type so as to realize rule chain reconfiguration or data reacquisition. The method has the advantages that non-coding docking and real-time supervision of heterogeneous system data are realized, and the problems of dynamic adaptation, semantic understanding and data blood relationship interpretability in financial fund management are solved.

The invention discloses a log quality detection method and system, electronic equipment and a storage medium. The method comprises the following steps: acquiring a multi-modal input source; performing reverse anchoring operation in a dynamic analysis stage of the running log to establish direct mapping between the running log and the source code; compiling the abstract syntax tree based on the source code, establishing a grammar dependence graph according to the abstract syntax tree, and traversing the abstract syntax tree to construct a structured data set; based on the running log, obtaining a quality detection result through structured analysis, and generating a semantic detection result by using a large language model; performing cooperative operation on the structured data set and the grammar dependency graph to generate a code log coverage score, and performing arrangement according to a quality detection result and a semantic detection result to obtain an abnormal mode feature; and performing feedback adjustment based on the code log coverage score and the abnormal mode feature in combination with a preset correction rule. The method can efficiently and accurately realize log quality detection, and can be widely applied to the technical field of data processing.

The invention relates to the technical field of vulnerability detection, and provides a fine-grained vulnerability detection method and system based on semantic perception sparse attention, and the method comprises the steps: analyzing a to-be-detected source code, and generating an abstract syntax tree, a control flow graph and a program dependency graph; carrying out weighted fusion on graph representations of the abstract syntax tree, the control flow graph and the program dependency graph, and integrating into a code attribute graph; for the code attribute graph, processing the code attribute graph by adopting a semantic perception sparse attention method combined with sparse attention, a learnable Token pruning method and Top-k interaction frequency to obtain a Token attention score; and based on the obtained Token attention scores, accumulating the attention score of each Token in each row of the code, and detecting and determining a statement where the vulnerability code is located. According to the detection method disclosed by the invention, the capability of modeling code semantic information and capturing a structured dependency relationship can be effectively improved, and meanwhile, the calculation complexity is remarkably reduced.

The invention discloses a PHP taint type vulnerability detection method based on a heterogeneous graph neural network, and belongs to the field of software security. The method comprises the following steps: performing annotation removal, variable naming standardization and character string standardization processing on a PHP source code through a code preprocessing module to generate a standardized code; based on a vulnerability sub-attribute graph extraction module, reversely tracking vulnerability sinks to a taint source, extracting a simplified vulnerability sub-attribute graph, and removing redundant nodes and edges; fusing BERT semantic features and node type features through a graph node embedding module to generate an initial embedding vector, and constructing a heterogeneous graph comprising an abstract syntax tree edge, a program flow graph edge and a control dependence graph edge; a heterogeneous graph neural network vulnerability detection module is adopted to perform independent feature aggregation on multiple types of edges, dynamic weighted fusion is performed in combination with an attention mechanism, and key nodes are screened through Top-k graph pooling; and finally, inputting the graph-level features into a classifier to realize vulnerability detection.

The invention provides a distributed storage method based on source code semantic partitioning, and particularly relates to the technical field of cloud data distributed storage. The method comprises the steps of performing semantic partitioning on a source code, and segmenting the source code into a plurality of semantic blocks according to dimensions such as functional semantics, an abstract syntax tree structure, author information and version information; generating metadata containing information such as grammar type tags, file paths, line number ranges, author identifiers, version identifiers and access popularity for each semantic block; constructing a weighted directed acyclic graph (DAG) based on the semantic chunks and the dependency relationship thereof; superposing a metadata layer in the DAG structure, and recording information such as function call dependency, inter-block reference relationship and version evolution chain; blocks with relatively high access frequency and close semantics are aggregated into super blocks, the traversal depth is reduced, and meanwhile, hot data and cold data are differentiated for hierarchical storage by adopting a cold and hot data management strategy; and evaluating a parent block aggregation degree through a BDS algorithm, determining a block sorting priority, and optimizing super block boundary division. Compared with the prior art, the method has the advantages that the semantic retrieval efficiency, the incremental updating capability and the distributed query performance of the source code storage system are improved.

The invention discloses a multi-mode fusion product document and source code association retrieval method based on a knowledge graph, and relates to the technical field of software engineering and artificial intelligence. The method comprises the steps that source codes are preprocessed, and code structure information and business semantics are mapped in combination with a predefined business term dictionary; performing controlled induction on a code file and a product document by utilizing a large model, extracting business terms, logic intentions and a subject relationship, fusing with original codes, and establishing a vector retrieval index system; further analyzing a code structure by using an abstract syntax tree, and extracting an entity and a calling relationship; semantic enhancement and relation normalization are performed in combination with the large model, entities and relations are stored in a graph database, and a knowledge graph is formed; and performing parallel processing on user query based on a full-text retrieval index, a vector retrieval index system and a knowledge graph, and finally generating a product concept. According to the method, the retrieval speed, the semantic depth and the logical reasoning ability can be considered at the same time, and the retrieval accuracy is improved.

The invention relates to the technical field of artificial intelligence and natural language processing, in particular to a cue word compiling and generating method and system for a large language model.The method comprises the steps that in response to a compiling calling request, a PDL protocol is received to serve as a compiling input text; performing lexical and grammatical analysis on the compiled input text, and constructing a corresponding abstract syntax tree AST as an intermediate representation according to a context-independent method; semantic verification and structure correction are carried out on the AST, and when semantics are effective, structure optimization is carried out to generate an optimized AST; and traversing the optimized AST, matching a cue word template in a cue word generation rule base, mapping semantic nodes into structured text fragments, splicing the structured text fragments to form a final cue word text, and outputting a compilation result consistent with PDL protocol semantics. By introducing a PDL-oriented compiling mechanism, automatic analysis, structure optimization and high-quality cue word generation of an input protocol are realized before cue words of a large language model are generated, and the development efficiency and the output quality of the cue words are improved.

The invention is applicable to the technical field of vulnerability detection, and provides a power system software security vulnerability dynamic and static combined detection method, which comprises the following steps: acquiring static source code data of power system software and dynamic behavior log data during operation; generating a hierarchical abstract syntax tree and structured time series data; performing multi-dimensional feature extraction on the static source code data and the dynamic behavior log data based on a vulnerability knowledge graph of the power system; generating a domain constraint confrontation sample based on the power protocol features and the abnormal features during operation, and inputting the domain constraint confrontation sample into the constructed hybrid detection model for confrontation training to obtain a trained hybrid detection model; and performing automatic detection on the power system software by utilizing the trained hybrid detection model, and outputting a detection result containing a static code defect position and a dynamic attack path. The full-life-cycle accurate detection of the software vulnerability of the power system is realized, and the network security protection capability of the power system is improved.

The invention provides a semantic-based migration and consistency verification method, system and device and a medium, and relates to the technical field of databases. The method comprises the following steps: performing metadata topology scanning analysis by obtaining metadata, including generating an abstract syntax tree and constructing a semantic graph; according to a metadata topology scanning analysis result, performing data mapping and conversion, including loading a YAML rule and generating corresponding type mapping and constraint conversion; data migration is carried out through primary key fragmentation parallel migration, batch writing optimization and real-time double-writing verification; through structure-data-business three-layer verification, simulation business SQL comparison and automatic difference repair, the integrity of migrated data and business compliance are ensured, the semantic gap problem in heterogeneous database migration is solved, high-precision and high-efficiency database migration is realized, the migration efficiency is improved, and the data migration efficiency is improved. The method is suitable for scenes with strict requirements on data consistency and migration efficiency, such as financial, government affair and enterprise-level data centers.

The invention discloses a method for realizing SQL (Structured Query Language) grammar conversion of different databases based on middleware and the middleware, belongs to the technical field of databases, and aims to solve the technical problem of how to realize SQL grammar conversion of different databases through the middleware. The following operations are executed through the middleware to realize the grammar conversion of the SQL between the source database and the target data constructing an abstract syntax tree AST by taking various SQL elements as nodes; operating nodes in the AST on the basis of requirements of a target database; based on the data type in the target database, performing data type mapping by applying a data type conversion rule, and generating an SQL statement adapted to the target database; performing security check on the generated SQL statement; caching the query result and the generated SQL statement based on a caching mechanism; and recording the conversion process and query processing of the SQL statement.

The invention provides a security code automatic generation method and system based on a full information theory and mechanism ambiguity, and the method comprises the steps: S1, carrying out the feature extraction of a grammar verification layer of an input demand, extracting the grammar features of a code through the topological features of an abstract syntax tree, and carrying out the formalized rule verification of the extracted grammar features; s2, constructing a semantic association model of a cross-code snippet through a semantic analysis layer, and forming a propagation path for tracking a data flow and a control flow; s3, in the pragmatic optimization layer, combining pragmatic constraints of the business scene, and generating a security policy through a dynamic utility function; and step S4, optimizing parameters and security policies of the dynamic utility function through knowledge federal evolution and a closed-loop feedback mechanism. According to the method, a deep collaboration mechanism of grammar, semantic and pragmatic three-dimensional information and a dynamic strategy generation model are reconstructed, security logic is converted from external attributes to endogenous cognitive competence, and the security, interpretability and adaptability of automatic code generation are improved.

A system for translating source code in a first programming language to a target language is provided. The system is configured to receive source code for converting to target code; determine an abstract syntax tree from the source code; determine program specifications from the source code; determine a dependency graph from the source code; determine a plurality of chunks based at least in part on the abstract syntax tree, the program specifications, and the dependency graph; determine a plurality of converted chunks based at least in part on the plurality of chunks and a deep learning model, the deep learning model converting the plurality of chunks from the language of the source code to the language of the target code; post-process the plurality of converted chunks to obtain intermediate code; and provide the intermediate code as the target code.

The invention discloses a cross-chain smart contract vulnerability detection method and system based on multi-feature fusion learning. The method comprises the following steps: collecting a cross-chain smart contract vulnerability data set for cleaning and labeling; feature extraction is carried out from the source code and the byte code, an abstract syntax tree (AST) is extracted from the cleaned source code, a basic control flow graph (CFG) is extracted from the byte code, and a cross-chain control flow graph (xCFG) is constructed; carrying out feature representation on AST and xCFG, generating a graph vector through a graph neural network (GNN), generating a semantic vector through CodeBert, and fusing the semantic vector into a feature fusion vector; performing model training and detection, taking the generated vectors as training data and test data, obtaining a cross-chain smart contract vulnerability detection model by adopting Transform-FC model training data, and finally evaluating model performance through accuracy, recall rate, precision rate and F1 value. According to the method, the structural features and semantic features of the codes can be effectively fused, potential vulnerability information in the codes can be fully mined, the recognition capability of the model for cross-chain vulnerabilities can be enhanced, and the accuracy and reliability of the cross-chain vulnerability detection model can be improved, so that the security of a block chain system can be more efficiently guaranteed.

The invention discloses a code translation method and system, electronic equipment and a computer readable storage medium, which are applied to a code translation model, and the code translation method comprises the following steps: generating an abstract syntax tree corresponding to a source code; segmenting the source code according to the code structure of the abstract syntax tree to obtain a plurality of code blocks; matching a corresponding code mapping relationship in a code mapping library; translating the plurality of code blocks into a plurality of target code blocks of a target code language according to a code mapping relationship; and compiling the plurality of target code blocks into target code in a target code language. According to the code translation model, the abstract syntax tree corresponding to the source code is firstly generated, then the whole source code is segmented into the multiple code blocks based on the abstract syntax tree, therefore, the whole task is segmented, then code translation is conducted through the code mapping relation in the code mapping library, the transfer speed and efficiency are improved, and the code translation efficiency is improved. And finally, compiling the plurality of translated target code blocks into a whole section of target code to ensure the integrity and accuracy of compiling.

The invention relates to the field of intelligent contract vulnerability detection, in particular to an intelligent contract vulnerability detection method based on code graph fusion analysis and a graph matching network. The method comprises the steps of constructing an intelligent contract source code data set; through static analysis, extracting three different code feature information including a control flow diagram, a data flow diagram and an abstract syntax tree from the source code of the smart contract; carrying out information integration on the three extracted code features, and constructing a code graph covering wider features; on the basis of the generated code graph, a GraphCodeBerT model is used for pre-training; vectorizing a code graph which is output by the pre-training model and contains structural and semantic features through a graph embedding network; and learning the feature representation of the contract pair by using a cross-graph attention mechanism of a graph matching network to realize specific vulnerability detection of the smart contract source code. According to the invention, potential vulnerabilities can be accurately identified.

The invention discloses an intelligent contract vulnerability detection method based on a heterogeneous graph attention network. According to the method, firstly, the source code of the intelligent contract is preprocessed, the SCIR of the code of the intelligent contract is constructed, the complexity of the code of the contract is reduced, and vulnerability features are enriched; and constructing an intelligent contract code attribute graph SCPG based on SCIR, integrating various code graph structures such as an abstract syntax tree and a control flow graph, and comprehensively describing syntax and semantic features of the contract. And then constructing an intelligent contract code heterogeneous graph SCHG on the basis of the SCPG, optimizing code graph structure representation, and realizing high-quality modeling of node features. And finally, detecting the vulnerability of the smart contract by using a customized multi-layer heterogeneous graph attention network model MHGAN. The intelligent contract vulnerability detection method based on deep learning makes up for the defects of an existing intelligent contract vulnerability detection method based on deep learning, effectively improves the accuracy of intelligent contract vulnerability detection, and is excellent in the interpretability of the detection result.

The invention discloses a code reconstruction method, system and device based on function recognition and a medium, and the method specifically comprises the steps: scanning a project file topological structure, and constructing a knowledge graph based on a code import relation and a technology stack feature; performing context enhancement by analyzing the code function description and combining with the knowledge graph to generate a code modification instruction set; based on the code modification instruction set, in combination with data stream sensitivity marking and performance portrait analysis, evaluating the target code to obtain an evaluation result; based on an evaluation result, matching a historical optimal practice mode through a graph neural network, and generating an optimization scheme which retains an original design style; by comparing abstract syntax tree differences between original codes and verified optimization schemes, only functional logic nodes are replaced, and legal code style features are reserved. According to the method, the efficiency and quality of code reconstruction and optimization are improved, and a more convenient, efficient and intelligent code processing tool is provided for software developers.

The invention belongs to the field of artificial intelligence, and relates to a code conversion method and device, computer equipment and a storage medium, and the method comprises the steps: obtaining a source code and a target language; analyzing the source code to generate an abstract syntax tree; analyzing the abstract syntax tree, and extracting semantic information; according to the semantic information and a grammar rule of a target language, converting the code into the target language to obtain a preliminary conversion code; and further optimizing the preliminarily converted code to generate a final translated code. The automation degree is high, and the workload of manual conversion is greatly reduced; the conversion process is standard and unified, and the consistency and accuracy of code conversion are improved; by analyzing the AST, the source code can be understood more deeply, so that the translation code with higher quality is generated; extension and maintenance are easy, and support for new languages or characteristics can be added according to needs; the optimization step ensures that the converted code is not only correct, but also efficient and high in readability; and cross-language programming and code reuse are promoted.

The invention discloses an intelligent Bug management method and platform based on an AI large model. The method comprises the steps that structured error codes, unstructured log texts and development communication records are collected; standardized Bug features are obtained after preprocessing; semantic matching is performed based on a CodeBERT large model to generate a historical similar Bug case set, and a problem code segment is analyzed and positioned in combination with an AST abstract syntax tree; according to the project technology stack features, the historical similar Bug case set and the problem code segment, generating a repair scheme including code modification suggestions, automatically generated repair code snippets and implementation risk assessment; constructing and optimizing a triple knowledge graph; and finally, associating the standardized Bug features, the problem code segment, the repair scheme and the knowledge graph to obtain a Bug diagnosis result, and outputting the Bug diagnosis result. According to the method, intelligent positioning and accurate repairing of the Bug are achieved, the average Bug solving time is remarkably shortened, and meanwhile automatic precipitation of development experience is achieved through the continuously optimized knowledge graph.

Disclosed in the present invention are a method and system for building a compilable customized module. The method comprises: customizing a module on the basis of a process, defining a modeling language syntax, and generating a modeling language file corresponding to the customized module; establishing a text matching rule and a grammar matching rule, using flex to generate a source program file of a lexical analyzer from the text matching rule, and using bison to generate a source program file of a syntax analyzer from the grammar matching rule; performing lexical analysis and generating an abstract syntax tree; traversing the syntax tree, generating a translation C++ file for a modeling language on the basis of a syntax-based translation configuration file, and compiling the translation C++ file; and importing the built customized module and an executable binary file into a simulation platform, and performing flow simulations for different scenarios including process simulation, optimization and parameter tuning. The present invention can realize the rapid commissioning of a process and can seamlessly switch a customized model to various application scenarios.

The invention provides a private intelligent code collaborative optimization method and system based on graph retrieval, and relates to the technical field of software engineering intelligent auxiliary tools, and the method comprises the steps: obtaining a source code file and operation log data of a private project; the method comprises the following steps: performing syntax tree analysis on a source code file through a Tree-siter analyzer to generate a corresponding abstract syntax tree; constructing a code knowledge graph by analyzing a function call relationship, an inheritance relationship and a module dependency relationship; monitoring the change of a project file in the code knowledge graph through a code change event processor, and updating the code knowledge graph in real time when the file is changed; establishing an association relationship between the running log data and a corresponding function node in the code knowledge graph; the method comprises the following steps: analyzing a natural language problem of a developer into a Cypher query statement through an intelligent Agent; obtaining a query result from the graph database; and formatting the query result to generate a final result.

The invention provides a file uploading attack interception method based on semantic entropy enhancement, and aims at overcoming the defects of an existing file uploading security protection technology in the face of complex attacks. The method specifically comprises the steps that S1, file format analysis and content extraction are conducted, hidden scripts are mined through nested content recognition, and intermediate representation is generated through grammar cleaning and coding specifications; s2, constructing an abstract syntax tree and semantic entropy calculation, tracking a pollution chain, analyzing a high-risk function, identifying a high-entropy character string, modeling and controlling flow complexity, and generating a semantic entropy vector; s3, dynamic scoring and decision making are carried out, and accurate judgment is carried out in combination with white list perception, feature comparison, multi-modal model scoring, adaptive threshold and sandbox observation; and S4, carrying out real-time interception and feature synchronization, blocking malicious file landing, generating an attack log and synchronizing an attack fingerprint. The method takes the semantic entropy vector as a core, breaks through the limitation of static features, remarkably improves the recognition rate of complex attacks, reduces missed judgment, and guarantees the safety of Web applications.