245 results about "Call graph" patented technology

The invention provides an SCA and SAST combined vulnerability reachable path detection method and device. The method comprises the steps of obtaining a to-be-detected item; extracting an entry function based on the to-be-detected item; when the to-be-detected item is a source code item or a product item, generating a function call graph based on call graph generation logic corresponding to the source code item or call graph generation logic corresponding to the product item; identifying a vulnerability function in the dependent component of the item to be detected through a software component analysis tool based on preset software bill of material information; taking the vulnerability function as a starting point, performing depth-first search traversal in the function call graph, and detecting whether an entry function is traversed or not; and if yes, determining a path from the entry function to the vulnerability function as a vulnerability reachable path. According to the method and the device, the reachable path of the vulnerability can be automatically detected, so that whether the security vulnerability is triggered or not is determined, invalid checking of the SCA tool in the use process can be avoided, and the reliability of the SCA tool is improved.

The invention belongs to the technical field of software engineering, and particularly relates to a project dependency library vulnerability step-by-step utilization method based on LLM enhancement. The method comprises the following steps: carrying out lightweight dependency tree and call graph reachability analysis on a project, and identifying all reachability call chains from the project to a vulnerability method; decomposing the fuzz test on the whole call chain into a directional fuzz test on each step of method call in the call chain; in combination with static analysis and prompt engineering technologies, seed generation is assisted by semantic understanding of LLM on a current test function and utilization information generated by a previous test. And performing directional fuzzy testing by utilizing the generated seeds, analyzing execution paths of the seeds through an execution feedback mechanism, guiding selection and variation of the seeds, and finally generating effective input capable of successfully utilizing the project dependent library vulnerabilities. According to the method, developers can be helped to quickly position and timely repair vulnerabilities, so that risks and potential loss of projects caused by dependency on the vulnerabilities are reduced.

The invention discloses a vulnerability hidden danger intelligent detection method based on a large model, and the method comprises the steps: firstly carrying out the global static analysis of a source code set, constructing a complete call graph and a complete data flow graph of a program, and forming a structured code knowledge graph; and then, aiming at the identified candidate vulnerability slices, based on the maps, carrying out accurate context retrieval and enhancement, converting key information such as a call chain and a data traceability path which are strongly related to the vulnerability slices into natural language description which can be understood by a large language model, and injecting the natural language description into cue words, so that missing global context information is provided for the model. And the defect of complex code analysis capability is overcome. In this way, the problem that an attention mechanism loses efficacy in remote code association is solved, and the accuracy and reliability of vulnerability detection are remarkably improved.

The invention provides a malicious code gene information recessive association mining method based on meta-learning, which comprises the following steps of: generating a function call graph (FCG) to obtain an attribute function call graph; training the feature extraction model based on the sequence-to-sequence mechanism by adopting meta-learning, and obtaining a key sub-graph of the function call graph from the generated attribute function call graph through the trained feature extraction model based on the sequence-to-sequence mechanism; generating a control flow graph (CFG), and obtaining an attribute control flow graph; using the optimized gene feature extraction model based on the graph structure for the generated attribute control flow graph to obtain a key sub-graph of the control flow graph; and a fine-tuning corpus is obtained through the key sub-graph of the function call graph and the key sub-graph of the control flow graph, and hidden association mining of malicious code gene information is carried out by using the fine-tuned large language model. According to the method, the recessive association of the malicious code gene information can be accurately mined, and the malicious code analysis precision and efficiency are remarkably improved.

The invention relates to an interactive visualization method, device and equipment for fuzzy test bottleneck breakthrough, and belongs to the field of network security. According to the method, all compiling modules in a tested program are combined into a complete intermediate code, and a global view of the tested program is obtained; generating a unique label for each function by using a hash algorithm; constructing a call graph and a control flow graph, and obtaining function call instruction information by scanning a basic block of each function; carrying out pile insertion on the basic blocks; performing a fuzzy test on the tested program after compiling and instrumentation, and obtaining runtime data in real time; according to the fuzzy test runtime data and the static analysis data, data-driven visualization is carried out, and a call graph, a control flow graph and real-time execution statistical information are displayed in real time; a test engineer is assisted through an interactive chart, and fuzzy test bottleneck constraints are positioned. According to the method, a test engineer can be helped to better understand and intervene the fuzzy test process, and the fuzzy test code coverage rate and the defect detection number are improved.

The invention belongs to the technical field of computers, and particularly relates to a software fault repairing method and system fused with intelligent analysis, which comprises the steps of collecting a multi-level running log and performing structured preprocessing, constructing a dynamic calling graph through a time sequence encoder and a graph neural network, inferring a fault root cause in combination with a Bayesian causal inference model, and repairing a fault fault according to the fault root cause. And matching the repair strategy to generate an atomization instruction sequence, and deploying the atomization instruction sequence to a production system after sandbox environment verification. The system comprises a log acquisition module, a feature coding module, a graph construction module, a causal reasoning module, a strategy matching module, an instruction generation module, a sandbox verification module, a deployment feedback module and the like. Through end-to-end intelligent analysis and a closed loop verification mechanism, the fault positioning precision and the repair safety are remarkably improved, system self-evolution is supported, and operation and maintenance are promoted to be transformed from passive response to active autonomy.

The embodiment of the invention provides a multi-round dialogue security defense method and device, electronic equipment, a storage medium and a program product, and relates to the technical field of data security. According to the method, dialogue semantic graphs capable of representing multi-round dialogue semantic evolution, intention circulation and entity association are constructed by obtaining multi-round dialogue content and a tool calling sequence; the method comprises the following steps of: respectively inputting user input information into two types of maps according to a tool calling graph representing a tool calling time sequence relationship and an interaction relationship to extract double risk feature vectors based on multi-round contexts and tool calling logic, and carrying out collaborative risk judgment in combination with a preset judgment rule; according to the invention, rapid integration of multi-round context information and accurate identification of attack risks are realized, so that a protection strategy can adapt to dynamic interaction requirements of an intelligent dialogue system in time, and the technical problem of low defense efficiency in the prior art is effectively solved.

According to the incremental defect detection method and system based on the function abstract, a function abstract design step, an SEG-based path condition storage method and a virtual node-based side effect abstract technology are adopted, a complete function call graph is constructed and stored as the function abstract, and the analysis precision and expandability are effectively improved; in the code change detection step, aiming at the differential detection requirement of the program, efficient identification of program version change is realized through an IR structured comparison method; an increment influence analysis step: based on the difference information, identifying an influence range brought by the change, and ensuring high efficiency and accuracy of increment analysis; and an incremental analysis step: recovering a path condition based on the behavior of the function abstract, recovering the complete program of the influenced program, designing a defect detection algorithm based on an SMT solver and the persistent function abstract, and realizing automatic detection and verification of potential defects of the program.

The invention belongs to the technical field of information security, and relates to an Android malicious software detection method based on API semantic enhancement, and the method comprises the steps: obtaining a to-be-detected Android application; performing static analysis on the to-be-detected Android application, and constructing a function call graph of the to-be-detected Android application; optimizing the function call graph of the Android application; extracting a complete API context sequence of a corresponding function from the optimized function call graph by adopting a recursive algorithm; adopting Skpi-Gram to map the complete API context sequence into feature vectors, and adopting a clustering algorithm to divide the feature vectors with close distances into the same class; inputting the clustered feature vectors into a trained graph convolutional neural network model to obtain a detection result of the to-be-detected Android application; according to the method, the extracted function call graph nodes are optimized by adopting the graph convolutional neural network, so that information loss caused by feature structure conversion is avoided, and the efficiency of model training and the accuracy of a detection result are improved.

The invention is applicable to the technical field of code development, and provides a code modification method based on a CURD scene, which comprises the following steps: acquiring a business demand of code modification, and acquiring a data model required by code modification according to the business demand; based on the data model, obtaining a code file related to a CURD scene during code modification; analyzing a calling relationship among functions related to the CURD scene in the code file, and constructing a function calling graph; on the basis of the service requirements and the data model, service code snippets are generated, the service code snippets are combined and modified to the code file, code modification of the service requirements can be processed, and development efficiency and code quality are further improved.

The invention discloses a knowledge graph platform supporting data integration analysis and visualization of an electric power system, and the platform comprises an electric power data preprocessing module which is used for extracting key information from documents and records of the electric power system through a natural language processing technology; the knowledge extraction module is used for extracting entities, relationships and attributes from the data source, constructing a triple and storing the triple into a knowledge graph; the data integration module is used for calculating the similarity among the attributes, the entities and the sub-graphs by using a model according to a preset requirement, identifying the ontologies needing to be aligned, and completing the integration of the entities; the graph construction module is used for constructing a knowledge graph in a dragging manner or from a public ontology library; the graph representation module is used for integrating a natural language processing function, providing a query service for a user in a natural language form, analyzing and querying through a natural language understanding technology, identifying a key entity and a user intention, and calling a graph algorithm to perform data retrieval and analysis; the user interface module is used for providing a user interface and providing user access and platform management, statistics and integration functions; the operation efficiency and the management level of the power system are improved.

The invention provides an automatic test generation platform and method for an operating system kernel derived function. The method comprises the following steps: (1) intelligent symbol extraction and analysis; (2) generating an LLM-driven test code; (3) performing automatic compiling and virtualization testing; and (4) intelligent error diagnosis and repair. According to the method, the test coverage degree is greatly improved, through the multi-level symbol context extraction technology, single function information is extracted, the dependency relationship of related functions in the same element is analyzed, a function call graph and data flow analysis are constructed, rich context information is provided for LLM, and the problem that traditional manual test case writing is incomplete in coverage is solved.

The invention provides a micro-service isolation operation method, medium and system for a productivity middle platform, belongs to the technical field of electric digital data processing, and provides the micro-service isolation operation method for the productivity middle platform, which realizes accurate scheduling of micro-service resources through multi-dimensional dynamic analysis. The method comprises the following steps: firstly, acquiring micro-service operation indexes including a processor occupancy rate, a memory occupancy rate, network throughput and the like, and generating a micro-service priority index; carrying out decomposition operation on the operation indexes, and constructing a reference and dynamic resource matrix; further calculating instantaneous and persistent competition indexes, and establishing a micro-service calling atlas; determining a resource allocation scheme by constructing a resource allocation equation set; determining a resource use threshold value by adopting an optimized dichotomy algorithm; and finally, the load of the resource unit is monitored in real time, when the load exceeds a threshold value, the migration cost is calculated, and the micro-service isolation operation is executed, so that the technical problem that a micro-service resource allocation and isolation mechanism cannot be accurately and dynamically adjusted in the prior art is solved.

An apparatus and method for improving ray tracing efficiency. For example, one embodiment of an apparatus comprises: An apparatus comprising: a binary instrumentation engine to perform binary instrumentation of ray tracing shaders and to trace execution of the ray tracing shaders to generate execution metrics; call graph construction logic to construct a shader call graph based on the execution metrics; shader source mapping logic to map the shader call graph to shader source code to generate a source code map; efficiency analysis logic to determine inefficiencies in ray tracing shader execution based on the source code map; and optimization logic to identify optimization actions based on the inefficiencies.

The invention relates to a homology analysis method and device for binary code files and computer equipment. The method comprises the following steps: obtaining and identifying a function call graph corresponding to each binary code file through a security monitoring program; identifying isomorphic sub-graphs among the binary code files through a hybrid search strategy, and performing symbol program slicing processing on respective write function nodes in each isomorphic sub-graph to obtain symbol program slicing codes corresponding to the respective write function nodes of each isomorphic sub-graph; performing escape processing on each symbol program slice code corresponding to the isomorphic sub-graph to obtain each piece of mathematical semantic information corresponding to the isomorphic sub-graph, and performing similarity calculation to obtain a code similarity value between the binary code files; therefore, the homologous binary code file is identified. By adopting the method, the homology analysis accuracy of the highly confused binary code file can be improved.

The invention relates to the field of interface component control, in particular to an interface component control method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring a calling relationship of all interface components, and constructing a component calling graph based on the calling relationship; constructing a main function of the interface component according to the component call graph; when the obtained control command is a starting instruction of a to-be-started interface component, starting of the to-be-started interface component is controlled according to the starting command and a main function of the to-be-started interface component; when the obtained control instruction is a closing instruction of a to-be-closed interface component, closing of the to-be-closed interface component is controlled according to the closing instruction, the component calling graph and a main function of the to-be-closed interface component. According to the method, the multi-level interface component can be accurately controlled, and the display observability and logic control accuracy of the interface component are improved.

The invention discloses a security detection method and system for an interactive application in a power system, a medium and a processor, and relates to the technical field of network security, the method comprises the following steps: acquiring firmware of the interactive application in the power system, and unpacking to extract a target code file function; performing static stain analysis on the extracted target code file, and constructing a pollution propagation call graph; on the basis of a graph search algorithm, traversing the pollution propagation call graph, and detecting whether potential security vulnerabilities exist or not; and classifying and evaluating the detected security vulnerabilities, and outputting a detection result. According to the method, pollution propagation analysis among different programming languages is realized, and the accuracy and range of vulnerability detection are improved.

The invention provides a code review method and system based on a multi-dimensional knowledge graph and electronic equipment. The method comprises the following steps: constructing a business knowledge graph, a method calling graph and a code review knowledge graph; code data are obtained, a target code in the code data is determined based on the code review request, and the target code comprises code change content; generating a large model cue word in combination with the business knowledge graph, the method calling graph and the code review knowledge graph; and according to the big model cue word, calling an AI big model to perform joint review on the code change content, and generating a review report which at least comprises a repair suggestion. According to the method, multi-dimensional knowledge of business, code calling and code review is integrated, a more comprehensive and closely associated knowledge system is formed, the generated cue word can accurately guide the AI large model to review code change in combination with multi-field knowledge, blindness of the large model during review is avoided, and review efficiency and accuracy are improved.

The invention discloses a software supply chain vulnerability verification code generation method based on guiding fuzzy testing. The method comprises the steps that s1, a tripartite library dependency graph with a binary program as a starting node is generated; s2, performing static component analysis on the target application software component to form a component dependency graph; analyzing the component dependency graph layer by layer, and constructing an overall function call graph; s3, matching all vulnerability functions existing in the three-party component based on CVE information, calculating vulnerability function layer distance according to an overall function call graph, and performing directed utility calculation on a prepositioned function and a function priority for fuzzy test energy distribution; s4, performing key node identification and vulnerability distance calculation on a tripartite library critical function in the overall function call graph; and s5: transmitting binary static analysis results of s1 to s4 to a fuzzy test tool for directional fuzzy test. The method has the advantages of being simple in principle, easy to implement, higher in testing capacity, better in applicability and the like.

The invention relates to the technical field of system testing, and provides a test case filtering method and related equipment based on an accessibility prediction model, and the method comprises the steps: generating a function call graph and a plurality of control flow graphs of a target system, and carrying out the integration to obtain an interprocess control flow graph; calculating the reachability representation of the test case based on the interprocess control flow diagram; performing feature extraction on the test cases to obtain case features, and constructing an association diagram of the test cases; carrying out reachability prediction by utilizing a reachability prediction model based on the use case characteristics and the association graph to obtain a prediction reachability representation; optimizing the reachability prediction model by using all the prediction reachability representations and all the reachability representations, and performing reachability prediction on the target test case by using the optimized reachability prediction model to obtain a final reachability representation; and filtering the target test case according to the final accessibility representation to obtain a filtering result. According to the method, the universality of test case filtering can be improved.

The invention provides a redundant code identification method and system based on a data flow diagram. The problem that multiple types of redundant codes cannot be comprehensively identified in the prior art is solved. Identifying a waste interface set through set operation processing of an interface access log, a total station scanning result and code warehouse information; performing dynamic call detection and data flow analysis based on a static analysis tool and the abstract syntax tree, and identifying an internal useless code set; identifying and combining a waste script set and a waste consumer set to form a waste component set through state check and log analysis of consumer operation states, script execution records and task scheduling configuration; constructing a whole system call graph to perform reverse reachability analysis, and identifying a cascade waste code set; and finally, generating a complete redundant code report containing statistical data, a detailed list and risk assessment based on the various sets.

The invention provides a code generation method and device based on a program call graph, electronic equipment and a storage medium, and relates to the technical field of intelligent software development. The method comprises the steps that a received input current code is responded, a code file related to the current code is selected and called from a plurality of code files by calling a relation graph, and the calling relation graph exists in a computer program; and generating subsequent code contents of the current code according to at least one code file in the code files and the current code file of the current code. By calling the relation graph, it can be ensured that the called code file has high correlation with the current code, and the calling accuracy is improved.

An impact analysis device acquires a list of method information related to a difference between a new version of a library and an old version of the library, based on a source code of the new version of the library and a source code of the old version of the library; extracts, from the list, a first difference related to a method existing in a first call graph of software that uses the old version of the library; extracts, from the list, a second difference related to a method added to a second call graph of the software that uses the new version of the library; extracts a set of method information of a caller of a transition added to the second call graph in comparison with the first call graph; and outputs a union of the first difference, the second difference, and the set, thereby enhancing support for determining a likelihood that a library update could affect operations of software.

The invention discloses an open source component analysis method based on a static program, and relates to the technical field of software analysis. Project source codes are input and analyzed, a semantic calling graph is obtained, and hierarchical management is conducted on the semantic calling graph; carrying out topological sorting on the layered semantic calling atlas to obtain a component check sequence; and comparing the semantic call maps corresponding to the new version and the old version of the to-be-upgraded component, performing software detection operation on the to-be-upgraded component, and outputting a conflict report. According to the method, a layered semantic calling graph is constructed and contains rich context information, so that analysis can go deep into the level of whether behaviors and semantics of the method are changed or not, topological sorting is carried out on component dependency relationships, a component checking sequence is provided, the problem that a conflict is repaired in reality is solved, and the method is more practical. By means of the method, it is guaranteed that before any component is checked, all the components on which the component depends are analyzed and confirmed, and orderliness and certainty of the analysis process are guaranteed.

A computer-implemented method for trace-driven call-graph-aware proactive coordinated autoscaling of component microservices in an application includes generating performance-resource elasticity models of endpoints of the component microservices of the application. Workload levels of the endpoint of the component microservices is predicted based on user traffic observed at a front end service. A trace-level performance of the application is predicted for different microservice replica scaling based on the performance-resource elasticity models at end points, the ends points on the trace call graph and the predicted workload levels. A microservice replica scaling is recommended for each of the component microservices to meet predefined trace-level user service level objectives.

The invention discloses a malicious code detection method and device for a third-party tool, a medium and a product. The method comprises the following steps: acquiring code data of a third-party tool; static analysis is carried out on the code data of the third-party tool to obtain static features, and the static features comprise at least one of cross-language call graph features, multi-version control flow graph difference features and local entropy values; the code data of the third-party tool is dynamically analyzed, dynamic characteristics are obtained, and the dynamic characteristics comprise at least one of an API calling sequence, memory operation information and a taint propagation path; based on a pre-trained detection model, the static features and the dynamic features are detected, a detection result of the code data of the third-party tool is obtained, and the detection result is used for indicating whether the code data of the third-party tool belongs to malicious codes or not. According to the technical scheme, collaborative analysis can be carried out on the code data through the static features and the dynamic features, and the detection accuracy of malicious codes is improved.

The invention discloses an intelligent detection method for network security vulnerabilities, and relates to the technical field of network security vulnerability detection, and the method comprises the following steps: analyzing source codes to construct an abstract syntax tree, a control flow graph and a call graph, identifying sensitive operation points based on a rule base, and screening hotspot functions in combination with code indexes and the call graph; extracting a candidate path from a program entry to a sensitive operation point, encoding to generate a path context snapshot, and scoring through a pre-training model; selecting a high-risk path to execute sparse symbolic execution, simplifying constraints by using variable interval information, and dynamically feeding back and adjusting scores; poC input verification is generated for the path triggering the vulnerability constraint, a report containing the path constraint, the triggering condition and the PoC is output, program analysis and machine learning are combined, the vulnerability detection efficiency and precision are improved, and the method is suitable for code security analysis of scenes such as an embedded system.

The invention discloses a method and a device for generating a software vulnerability analysis report, and relates to the field of information security. The method comprises the following steps: acquiring auditing information of software vulnerabilities; a code feature matching rule for the software vulnerability is generated according to the auditing information through the large language model, and the code feature matching rule is used for matching a code calling mode related to the software vulnerability in the target software project; constructing a calling graph of the target software project; according to the code feature matching rule and the call graph, the correlation degree between each code snippet of the target software project and the software vulnerability is detected, and the correlation degree is used for evaluating the risk level of each code snippet of the target software project; and generating a software vulnerability analysis report of the target software project according to the association degree between each code snippet of the target software project and the software vulnerability. The technical problem that in the prior art, an open source software vulnerability detection tool cannot accurately evaluate the real influence of vulnerabilities in specific project contexts is solved.

The invention provides a binary code decompiling system based on large model multi-stage fine tuning. The method comprises two stages: a first stage: a data set acquisition and construction stage: acquiring a source code data set comprising source codes and binary file pairs from an open source project and code data set, and obtaining a high-quality decompilation data set through a data preprocessing process; the second stage is a decompilation stage based on large model fine adjustment and static analysis, decompilation is carried out after a decompilation data set is compiled, an assembly code obtained after decompilation is aligned with a source code in the decompilation data set to be used for fine adjustment of the open source large model, a function call graph analysis technology is cooperated, a program context is obtained, and the open source large model is subjected to static analysis; and obtaining a final decompilation large model. Therefore, the purpose of providing a high-quality decompilation data set and an efficient decompilation algorithm is achieved.

The invention belongs to the field of big data analysis and processing in a micro-service environment, and relates to a false positive detection method for micro-service call graph abnormity based on fusion of a gated graph neural network (GGNN) and a Deep SVDD. The method comprises the following steps: obtaining a micro-service calling graph through a data set formed by link data of a micro-service log, and preprocessing the micro-service calling graph to obtain a model training data set; constructing an abnormal false positive detection model based on the micro-service calling graph; training the model to obtain normal / abnormal learning parameters of a gated graph neural network (GGNN), and respectively obtaining normal / abnormal Deep SVDD hyperspheres; and inputting to-be-detected suspected abnormal micro-service link data into the model by using the trained false positive detection model, and calculating the abnormal measurement of the micro-service call graph to judge the normal / abnormal state of the overall graph structure of the current micro-service call graph. According to the method, the problems of false positive and high false alarm rate of a current micro-service detection algorithm in an actual production environment can be solved.