157 results about "Call graph" patented technology

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 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.

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.

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 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 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 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.

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 discloses a cross-platform binary code block hierarchical semantic similarity detection method and a related device, and belongs to the field of code vulnerability detection.The cross-platform binary code block hierarchical semantic similarity detection method comprises the following steps that structural information of a binary file is analyzed, a machine instruction is analyzed through a hybrid disassembling engine, a basic block is extracted from the structural information of the binary file, and the basic block is extracted from the binary file; constructing a control flow graph, a data dependency graph and a function call graph; extracting instruction level semantics, basic block level semantics and function level semantics from the instruction sequence, the basic block, the control flow graph, the data dependency graph and the function call graph; according to the instruction-level semantics, the basic-block-level semantics and the function-level semantics, constructing instruction-level features, basic-block-level features and function-level features; and carrying out hierarchical similarity detection on the instruction-level features, the basic block-level features and the function-level features. The method and the related device can solve the problems of low detection efficiency and poor accuracy when a traditional vulnerability detection method is used for detecting vulnerabilities.

The invention discloses a fault propagation path determination method and device, a storage medium and electronic equipment, and relates to the field of Internet of Things. The method comprises the following steps: acquiring a target data set; a service calling graph is constructed according to the target data set, then strong connectivity vectors in the service calling graph are determined, and the service calling graph is used for representing a topological structure and a dependency relationship of service interaction in the distributed system; the strong connectivity vector is used for representing a connectivity component of which the bidirectional calling frequency is greater than a preset frequency and / or the data interaction strength is greater than a preset strength in the service calling graph; performing neighborhood feature aggregation on the service call graph to obtain a time sequence feature matrix; and determining a fault propagation path of the distributed system according to the strong connectivity vector and the time sequence characteristic matrix. According to the method and the device, the technical problem of poor fault propagation path prediction accuracy caused by lack of quantification of dependency relationship and topological association between services in fault propagation prediction in the prior art is solved.

The invention discloses a risk function detection method and system based on patch submission and risk propagation analysis. According to the method, static code analysis and a large language model are combined, and functions with security risk propagation possibility in patches can be identified and intelligently classified. The method comprises the following steps of: screening candidate functions, and extracting functions which are deleted, remarkably modified or quoted and changed from patches as detection objects; pruning a function call graph, and constructing an approximate Steiner sub-graph based on a call relationship to capture a key propagation path; and risk function classification: driving a large language model to complete function risk judgment by combining CVE information, a patch context and a call graph through a prompt project. According to the method, a risk function detection scheme combining the language model and call path pruning is proposed for the first time, and the method can be widely applied to scenes such as software supply chain security assessment and vulnerability repair verification and has the advantages of high accuracy, high cross-language adaptability and the like.

The invention discloses a cross-language interaction malicious behavior detection method based on a high-order function, and relates to the technical field of computer security. The method comprises the following steps: receiving a source code of a certain program, modeling a cross-language interface in the program, and identifying and marking a cross-language calling boundary; different language source codes are converted into universal intermediate representations (IR), the IR is combined, and cross-language calling points are marked; constructing a global cross-language function call graph and a control flow graph, determining a function analysis sequence by inverse topological sorting to perform dependency analysis on each function, and abstracting dependency information in the processes into a function abstract in a high-order function form; and parameter instantiation is carried out when function calling is processed, so that the dependency relationship between the cross-language APIs is obtained. According to the method, efficient analysis and transmission of the cross-language dependency relationship are realized through the high-order function abstract and the dependency matrix, the high overhead of constructing a complex system dependency graph by a traditional method is avoided, and the analysis efficiency and the detection accuracy are improved.

The embodiment of the invention provides a vulnerability analysis method based on a large model and SAST, a program product and equipment. The vulnerability analysis method based on the large model and the SAST comprises the steps that according to vulnerability information of any vulnerability in a vulnerability information base, a life cycle diagram of the vulnerability is generated; based on the life cycle diagram, obtaining a code context where the vulnerability is located from a project code; analyzing the code context by adopting a large model to obtain a vulnerability cause and an execution function of the vulnerability; backtracking and searching an entry function of the vulnerability from the execution function in a global call graph; wherein the global call graph is generated by analyzing the project code by adopting an SAST tool; outputting an analysis result of the vulnerability; wherein the analysis result comprises the vulnerability cause, the execution function and the entry function. According to the embodiment of the invention, the vulnerability availability information of various vulnerabilities in the open source project can be stably and accurately provided.

The invention discloses an artificial intelligence-based code generation method, which comprises the following steps of: S1, performing function level analysis on a generation request input by a user, extracting a function name, a parameter, a return value, a calling relationship and a dependency relationship, and generating a function calling graph; s2, taking the generation request input in the step S1 as a query, performing retrieval in combination with the function call atlas and the external knowledge base generated in the step S1, and jointly inputting the retrieved content and the coding model to generate a structured code snippet; and S3, taking the generation request input in the S1 and the structured code snippets generated in the S2 as joint input, and generating codes meeting individuation and project specifications. Through a function graph and a retrieval mechanism, a generated result has structural consistency and semantic integrity, and the matching degree with context requirements is remarkably improved (the actual measurement Top-1 accuracy is improved by about 22%).

The application discloses a virus detection model training method and device and a storage medium, relates to the computer field, and is used for realizing accurate detection of a Mirai virus. The method comprises the following steps: acquiring a plurality of executable files, determining a first function call graph in the plurality of function call graphs, and determining at least one second function with a function symbol similarity to the first function greater than a first threshold value; determining a third function with the greatest function symbol similarity to the first function in the at least one second function; adding function attribute information of the third function to the first function call graph as attribute information of the first function, and determining a second function call graph; comparing function information in the second function call graph with function information in a virus database, and determining whether the executable file corresponding to the first function call graph is a virus file; and if yes, training a virus detection model based on the second function call graph. Embodiments of the application are applied to the process of virus detection.

The invention discloses a code abstract generation method and system based on hierarchical context awareness, and belongs to the field of natural language processing and software engineering. The method comprises the following steps: generating hierarchical annotations for a code warehouse based on a function call graph, firstly extracting a call relationship in the graph and eliminating a ring structure, and then classifying leaf nodes and generating annotations; a topological sequence from bottom to top is adopted in the core step, the importance score of a child node is calculated for each father node, child node annotations, class function descriptions and code bodies of the child nodes are fused, and a large language model is input to generate annotations till the whole graph is covered. The method is suitable for an automatic document generation scene of a complex software project, and through a technical path combining function call graph analysis, child node importance measurement and class function description, the accuracy and semantic consistency of code annotation are improved; the method can be widely applied to the fields of software maintenance, code understanding, intelligent development tools, automatic document generation of enterprise-level code warehouses and the like.

This invention relates to an automated method for detecting firmware update vulnerabilities in IoT devices, comprising the following steps: extracting the file system from the firmware and separating the front-end and back-end files; parsing the front-end and back-end files to obtain the back-end binary file to be analyzed; parsing the back-end binary file to be analyzed and constructing a call graph between the back-end binary files, a function call graph of the firmware update process, and a function control flow graph; performing vulnerability detection based on the constructed call graph between the back-end binary files, the function call graph of the firmware update process, and the function control flow graph, and outputting a vulnerability report. Compared with existing technologies, this invention can automatically, accurately, and efficiently detect firmware update vulnerabilities in IoT devices.

The application provides an operating system kernel export function automatic test generation platform and method, comprising the following steps: (1) intelligent symbol extraction and analysis; (2) LLM driven test code generation; (3) automatic compilation and virtualization test; (4) intelligent error diagnosis and repair. The application greatly improves test coverage. Through multi-level symbol context extraction technology, not only single function information is extracted, but also the dependency relationship of related functions in the same element is analyzed and a function call graph and data flow analysis are constructed, rich context information is provided for LLM, and the problem of incomplete coverage of traditional manual test case writing is solved.

The invention discloses a file code similarity analysis method, which is based on a directed graph isomorphism theory and comprises the following steps of: A, extracting characteristics of an open source code file, namely a Call Graph (Call Graph for short), and establishing a file sample library; b, extracting a function call relation graph of the file to be analyzed; c, executing the standardization work of the graph, and preprocessing the graph according to the related definition of the tree structure; d, extracting the maximum common subgraph of the function call relation graph of the file to be analyzed and the sample library file; and E, calculating a relationship between the maximum common subgraph and the function call relationship graph to obtain a similar result, and completing file code similarity analysis. According to the method, the similarity of the file codes is analyzed by depending on the function call relation graph, the internal characteristics and higher-level logic characteristics of the file codes are considered, and higher accuracy and efficiency are achieved. The method is suitable for occasions such as source code plagiarism detection.

The embodiment of the invention provides a test method and device for an operating system kernel based on a Rust language, and the method comprises the steps: obtaining a source code of the operating system kernel based on the Rust language, compiling the source code into an intermediate representation, and generating a function call graph among functions contained in the operating system kernel according to the intermediate representation; determining a target position corresponding to a preset potential anomaly in the intermediate representation and a target variable corresponding to the potential anomaly in the target position through a preset recognition rule; performing backward tracking analysis on the target variable according to the function call graph and the target position, and determining a system call interface associated with the target position in an operating system kernel and a call variable associated with the target variable in the system call interface; and according to the system call interface and the call variable, generating a test case for the operating system kernel.

The invention provides an interactive code coverage rate defect detection method, device and equipment and a medium, the interactive code coverage rate defect detection method comprises the following steps: executing static analysis on a target system by adopting a hook function to obtain a taint set and a call graph of an interactive code configured by the target system; determining a running state diagram of the target system according to the stain set and the calling diagram; obtaining a configuration coverage rate and call stack information of a target system to which the workload is applied through the hook function, and testing the target system according to the configuration coverage rate, the call stack information and the running state diagram to obtain a tested seed pool and variation configuration parameters; and executing an iterative test according to the seed pool and the variation configuration parameters to obtain a configuration defect detection result. The method has the beneficial effect that the configuration interaction code coverage rate and the overall code coverage rate are improved.

The invention relates to the field of security vulnerability early warning, and particularly discloses a software security vulnerability early warning method based on deep learning, which comprises the following steps: starting from a predefined sensitive sink, screening out a code path set directly related to a potential risk by constructing a global function call graph and carrying out call chain reverse tracing; and the analysis redundancy is reduced from the source. Furthermore, according to the scheme, simple sequence modeling is not carried out on the call chain, function nodes in the call chain are converted into deep semantic vectors, and the semantic influence of key nodes is amplified and the interference of irrelevant information is inhibited by dynamically evaluating the time sequence importance of each function node in the call chain and through adaptive weighting. And finally, deep learning is carried out on the key path subjected to information discrimination, and vulnerability classification prediction is carried out, so that the problem of information overload in a long call chain is effectively overcome, and the accuracy of vulnerability early warning and the capture capability of complex semantic association are improved.

The invention relates to the technical field of recognition for open set shelled software, in particular to a recognition method, system, equipment and medium for open set shelled software, which comprises the following steps of: extracting function call graph structure information from a binary executable file to be recognized through a disassembling tool and a user-defined plug-in thereof; generating a structured graph representation file; on the basis of the graph representation file, graph statistical features and file section structure features are extracted, and a spliced feature vector is formed; and based on a multi-model single-class identification strategy constructed for open set identification, carrying out classification identification on the binary executable file corresponding to the current feature vector. The method has the beneficial effects that the identification efficiency of the shelled software is integrally improved, the detection capability of the unknown type of shelled software is particularly enhanced, and powerful support is provided for dynamic malicious software analysis.