34 results about "Data dependency graph" patented technology

The invention relates to a cross-language software vulnerability detection method and device, and the method comprises the steps: carrying out the analysis of a Joern static analysis pair, carrying out the integration and semantic enhancement of an abstract syntax tree, a control flow graph and a data dependence graph, and obtaining a cross-warehouse heterogeneous code graph; obtaining cross-language intermediate representation based on a compiler framework; after the cross-language intermediate representation and the cross-warehouse heterogeneous code graph are modeled, weighted fusion is carried out through a gated cross attention mechanism, and a multi-modal data set is obtained; carrying out migration training on the multi-modal cross-language vulnerability detection model, and carrying out vulnerability detection on cross-language software to obtain a detection result; through multi-modal data fusion and modeling, in combination with cross-language intermediate representation and a cross-warehouse heterogeneous code graph, the defects of a traditional method in the aspects of cross-language generalization ability and context reasoning ability are effectively overcome; the method has the advantages that the generalization ability of cross-language vulnerability detection is improved, the false alarm rate and the missing report rate are reduced, and the comprehensive utilization effect of global structure information is enhanced.

The invention relates to the field of code vulnerability detection, in particular to a binary vulnerability retrieval and positioning method and system based on high-dimensional vulnerability characterization, and the method comprises the steps: firstly inputting a binary file, analyzing the structural information of the binary file, and analyzing a machine instruction; extracting binary file function information, generating a cross-platform basic block IR instruction sequence, constructing a control flow graph, and constructing a data dependency graph; splicing the program dependency graph feature vector and the basic block IR instruction sequence feature vector, and performing multi-modal feature fusion to generate a comprehensive high-dimensional vulnerability representation vector; and finally, inputting the high-dimensional vulnerability representation vector into an index for searching to obtain a plurality of nearest neighbor indexes and distances, converting the distances into similarity, filtering according to a similarity threshold to obtain a most similar vulnerability vector, and obtaining vulnerability information and vulnerability positions corresponding to the vulnerability vector. The problems that traditional binary vulnerability detection cross-platform and compiler optimization detection fails and the result reliability is low are solved.

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 relates to the field of code vulnerability detection, in particular to a vulnerability detection method and system based on code block features. The method comprises the steps that firstly, a binary file is analyzed, and a machine instruction is analyzed through a hybrid disassembling engine; secondly, performing static linear scanning on the extracted binary file instruction, constructing a control flow graph, dividing basic blocks, extracting an operation code sequence in the basic blocks, and constructing a cross-basic-block data dependency graph and a system calling sequence called by the basic blocks; secondly, using an LSH algorithm to realize rapid filtering, performing similarity comparison on extracted basic block features and vulnerability feature library features, realizing coarse-grained matching, and screening out a matching candidate set; feature fusion is carried out on the candidate set basic block operation code sequence, the basic block data dependency graph and the system calling sequence called by the basic block, GNN fine-grained matching is carried out on the fused features, and vulnerability detection is carried out. The problems that traditional binary vulnerability detection is poor in adaptability and low in result reliability are solved.

The invention provides a data persistence defect detection method and device, electronic equipment and a storage medium, and relates to the technical field of database detection.The method comprises the steps that context analysis is conducted on database source codes, and at least one fault injection point in the database source codes is determined; random faults are injected into the fault injection points; determining a theoretical recovery data graph corresponding to the data dependency graph on the basis of a database recovery log under the condition of random fault triggering during database operation; the data dependency graph is used for representing a metadata state of the database before the random fault is triggered; and based on the actual recovery data graph and the theoretical recovery data graph corresponding to the database, determining a data persistence defect detection result corresponding to the database. According to the method, the random fault can be accurately injected into the fault injection point obtained based on context analysis, so that the fault triggering is efficiently simulated when the database runs, and the data persistence defect corresponding to the database is accurately identified.

The invention relates to the field of intelligent analysis, and particularly discloses a binary code-oriented static taint analysis system and method, which introduces an on-demand micro-symbol execution mechanism on the basis of basic data flow analysis through binary lifting and control flow diagram construction, that is, aiming at memory ambiguous nodes in a data dependence diagram, a micro-symbol execution mechanism is introduced on the basis of the basic data flow analysis; refined alias analysis is achieved through reverse instruction slicing and constraint solution, then an enhanced data dependency graph is generated, and path-sensitive taint propagation iteration is conducted on the basis to generate a final taint state table. In this way, complex dynamic memory addressing can be effectively recognized, taint chain breakage and misinformation explosion caused by ambiguity ignoring or excessive approximation are avoided, and resource allocation of an analysis engine is remarkably optimized.

The application relates to the technical field of computer network information security, and discloses an intelligent analysis and judgment system for network security vulnerability detection, which comprises the following modules: a state acquisition and marking module, which extracts a microstate feature vector and assigns a binary stain label based on a propagation condition to generate a high-dimensional point cloud set with labels; a semantic topology reconstruction module, which extracts a control flow graph and a data dependency graph, and generates a non-Euclidean distance weight matrix in combination with Euclidean numerical differences; a relative homology calculation module, which divides a trusted subspace and a full-amount topology space according to the non-Euclidean distance weight matrix to generate relative topological invariant data; and a vulnerability analysis and judgment module, which constructs a high-dimensional continuous bar graph, generates a vulnerability triggering alarm signal when a Wasserstein topology distance exceeds a dynamic security threshold value, and generates a topology deformation gradient to be fed back to a fuzzy test engine. The application eliminates normal execution background interference, and realizes high-precision identification of deep vulnerabilities and intelligent guidance of a test direction.

The invention belongs to the field of kernel vulnerability analysis of operating systems, and particularly relates to a kernel memory vulnerability cause diagnosis method based on causal chain analysis. The method comprises the steps of restoring a data dependency graph and a function call tree in a vulnerability triggering process from instruction-level tracking data in the vulnerability triggering process, and restoring a kernel execution state and kernel behavior semantics in combination with function modeling; analyzing the kernel execution state, and identifying an abnormal execution behavior in the kernel memory vulnerability in combination with the abnormal model of the kernel memory vulnerability; based on causal chain analysis of dynamic programming, causes of vulnerabilities are analyzed and obtained in a series of kernel execution abnormal behaviors. According to the method, the kernel execution state and behavior semantics are restored, the kernel abnormal behavior state is identified, and the causal chain of the abnormal behavior is analyzed, so that the cause of the kernel memory vulnerability can be effectively diagnosed, and the subsequent vulnerability repair work is promoted.

This invention provides a multi-strategy caching and automatic data generation method and system based on a Node environment, relating to the field of data processing technology. The method includes receiving client requests, obtaining data generation templates, constructing a data dependency graph to analyze field dependencies, using a recursive generation strategy and depth-first search to determine the field generation order, performing data validity verification, creating a data generation thread pool based on the data dependency graph using an adaptive load balancing mechanism, and implementing asynchronous processing through a double-buffered queue. This improves data generation efficiency, ensures data logical integrity, and reduces system resource consumption.

This invention provides a method, system, device, and storage medium for smart contract vulnerability detection, belonging to the fields of blockchain security and artificial intelligence technology. It includes extracting an abstract syntax tree, control flow graph, and data dependency graph from the original smart contract code; obtaining a composite graph object based on the abstract syntax tree, control flow graph, and data dependency graph; processing the composite graph object based on node semantics and function call relationships to obtain a hierarchical heterogeneous graph; constructing a heterogeneous graph Transformer network; using a heterogeneous graph Transformer encoder to process the hierarchical heterogeneous graph for encoding node and edge information, obtaining node representation vectors that fuse semantic and structural information; extracting multi-scale features from the node representation vectors; fusing the extracted multi-scale features using learnable weights; and processing the fused multi-scale features using a base classifier cluster to obtain vulnerability type prediction results and confidence levels. This invention achieves full-process optimization from structural modeling and semantic fusion to decision reasoning, significantly improving the accuracy and interpretability of smart contract vulnerability detection.

The application discloses a code search method and system based on multi-structure graph fusion. An early fusion strategy is adopted to fuse three modal information, i.e. abstract syntax tree, data dependency graph and control flow graph, into a functional program graph as the functional input of the code. Since the edges of the multiple graphs are heterogeneous, a meta-path aggregation graph neural network is adopted to distinguish the control flow, data dependency and syntax tree relationship through different edge type processing mechanisms, extract the code features of the graph, and enhance the understanding of the code semantic structure. The meta-path selection is dynamically adjusted through a graph attention mechanism, further weights are allocated to different nodes, key information is highlighted, the timing information in the code segment is captured, the accuracy of the code representation is improved, and the search capability is enhanced.

The invention discloses a taint type vulnerability detection method and system oriented to a native PHP application, and belongs to the technical field of vulnerability detection. The method comprises the following steps: generating a control flow diagram of each function in a PHP program source code; the method comprises the following steps of: initializing a function call graph which does not contain any node for a PHP program source code; determining a value range of each variable in the PHP program based on the control flow graph and the initialized function call graph, and carrying out iterative updating on an initial value set analysis result and the initialized function call graph; fusing the final function call graph and the control flow graph to generate an inter-process control flow graph, and executing inter-process arrival constant value analysis based on the inter-process control flow graph and a final value set analysis result to generate a data dependency graph; and propagating the stain information along the data dependence graph, and combining the final value set analysis result to obtain a stain type vulnerability detection result of the PHP program. According to the method, the dynamic value can be accurately deduced, and potential taint type vulnerabilities in the Web application can be found as early as possible.

A platform operation performance evaluation method and system based on collaborative intelligent analysis, relating to the field of electronic digital data processing, is disclosed. The method includes: establishing a directed data dependency graph based on data relationships; arranging operation performance data in chronological order to form a performance fluctuation sequence; calculating the correlation importance of monitored objects in the directed data dependency graph; analyzing the performance fluctuation sequence; calculating the propagation consistency factor of performance data exceeding a preset performance threshold and the temporal tightness factor of related performance data in the performance fluctuation sequence; calculating the propagation weight value between any two monitored objects and constructing a performance fluctuation propagation sequence for the monitored objects based on the propagation weight value; calculating the abnormal impact degree of the monitored objects based on the performance fluctuation propagation sequence and the directed data dependency graph; and identifying monitored objects with an abnormal impact degree exceeding a preset impact degree threshold as target abnormal objects. Implementing this method can improve the accuracy of platform operation performance anomaly evaluation.

The invention relates to the technical field of data processing, and provides an interface case data processing method and device, electronic equipment and a readable storage medium. The method comprises the steps that clustering processing is carried out on to-be-processed flow data based on a preset sorting condition, an ordered interface calling chain is obtained, and the ordered interface calling chain comprises response data and request data; performing full-field value matching processing on the response data and the request data to obtain a data dependency graph; performing parameterization processing on the request data based on the ordered interface call chain and the data dependency graph to obtain target interface case data; and the target interface is verified based on the target interface case data, so that the automation degree of interface testing is improved, the fitting degree of the case data and a real business scene is improved, the verification accuracy, continuity and maintainability are enhanced, the interface verification accuracy and efficiency are improved, the interface case implementation efficiency is improved, the maintenance difficulty is reduced, and the user experience is improved. And the reusability of the interface case data is enhanced.

The application discloses a kind of data operation execution systems for enterprise level low code, specifically relates to low code development technical field, including atomic executor module, dependent parallel scheduling module and debugging sandbox module. Query, new, update, delete, rule check and custom function are uniformly encapsulated as standard atomic executor by atomic executor module, each executor configuration execution condition expression and trigger actual logic when condition is satisfied, while introducing adaptive timeout fuse mechanism to guarantee system stability. Dependent parallel scheduling module constructs data dependency graph between executors by regular matching, and based on topological sorting, non-dependent executors are divided into parallel groups. After priority sorting, they are executed in parallel through thread pool. The application solves the problems of low efficiency of low code platform data operation serial execution, dirty data in debugging process and opaque process, and realizes efficient parallel scheduling and safe and observable debugging experience.

The invention relates to an electronic control firmware performance optimization method and related equipment, and the method comprises the following steps: carrying out the static semantic analysis of an original firmware code, and obtaining a firmware code symbol table; performing data stream tracking on the original firmware code based on the firmware code symbol table to obtain a data dependency graph; performing control flow sensitive analysis on the original firmware code based on the data dependency graph to obtain a control flow sensitive instruction sequence; performing loop expansion optimization on the control flow sensitive instruction sequence through an instruction scheduling engine technology to obtain an expanded instruction sequence; and performing register allocation on the original firmware code based on the expanded instruction sequence so as to reduce memory access times, improve the execution efficiency of the original firmware code, and solve the problems that traditional firmware development focuses on function implementation, neglects efficient matching between a code execution path and hardware resources, and improves the execution efficiency of the original firmware code. And the technical problem that instruction execution is delayed in a complex application scene is solved.

The application provides a reconfigurable computing architecture mapping method based on graph convolution and reinforcement learning, comprising the following steps: acquiring a data dependency graph and a coarse-grained reconfigurable array, and calculating a minimum start interval of the data dependency graph; preprocessing the data dependency graph according to the minimum start interval; simplifying a hardware model resource graph according to the structure of the coarse-grained reconfigurable array; taking a node set of the hardware model resource graph as an action space; adopting a reinforcement learning model based on a graph convolution network to map the preprocessed data dependency graph, so as to obtain a mapping result of a data dependency graph node on the hardware model resource graph; calculating a reward value of the mapping result; and when it is judged according to the reward value that the mapping result meets a successful mapping condition, outputting the mapping result. The application optimizes the layout and wiring of a loop on a reconfigurable computing array by using graph convolution and reinforcement learning, improves the parallelism of loop execution, and thus increases the mapping quality and reduces the execution time of image denoising.

A technique for arranging matrix multiplications for concurrent execution in an integrated circuit device may include obtaining a representation of a data dependency graph of a neural network model. The data dependency graph may include having an accumulation group (AG) pack of accumulation groups (AGs), in which each of the AGs has one or more matrix multipartition instructions. A representation of a memory location base partition constraint graph of the AG pack can be generated, and an AG row group constraint graph can be generated based on the memory location base partition constraint graph. The AGs of the AG pack can then be assigned to tiles in an integrated circuit device based on the AG row group constraint graph.

The application provides a vulnerability detection method based on graph embedding and a bidirectional gated graph neural network. First, function-level source code containing vulnerabilities and source code not containing vulnerabilities extracted from source code are obtained, all the source code is converted into a program dependence graph including a control dependence graph and a data dependence graph by using a source code analysis tool, and then the improved node2vec method is used to convert the program dependence graph into a code graph embedding representation, which contains both the graph structure information of the source code and the text structure information of the source code, and to a certain extent, the ability of the feature to represent nonlinear information is improved, and finally, the code embedding after the pretreatment is trained by using a bidirectional gated graph neural network model. The training result is applied to a target program to detect and evaluate the code vulnerability of the target program.

The application relates to the field of smart contract security and discloses a smart contract vulnerability detection method based on an operation code program dependency graph, which aims to obtain effective smart contract vulnerability related semantic information from the bytecode level and improve the accuracy of automatic detection of smart contract vulnerabilities. The method comprises the following steps: collecting a smart contract bytecode dataset and marking operation codes that may trigger vulnerabilities; simulating the execution of a smart contract operation code sequence, constructing a smart contract operation code control flow graph and a data dependency graph; constructing a smart contract operation code program dependency graph; taking the operation codes that may trigger vulnerabilities as key nodes, extracting vulnerability related program dependency graph slices, combining the marked information to construct a slice dataset; encoding the program dependency graph slices; training a smart contract vulnerability detection model based on a graph neural network to extract slice features and realize smart contract vulnerability detection.

The invention discloses a code searching method and system based on multi-structure chart fusion. An early fusion strategy is adopted, and three kinds of modal information of the abstract syntax tree, the data dependency graph and the control flow graph are fused into a function program graph to serve as function input of codes. Due to heterogeneity of edges of multiple graphs, a meta-path aggregation graph neural network is adopted to distinguish control flow, data dependence and syntax tree relations through different edge type processing mechanisms, code features of the graphs are extracted, and understanding of code semantic structures is enhanced. And meta-path selection is dynamically adjusted through a graph attention mechanism, weights are further allocated to different nodes, key information is highlighted, meanwhile, time sequence information in code snippets is captured, the code representation accuracy is improved, and the search capability is enhanced.

The specification discloses a code detection method and device, a storage medium and an electronic device, wherein the method comprises: acquiring a code to be detected, generating a syntax tree, determining a data flow and a control flow of the code to be detected based on the syntax tree, generating a data dependency graph based on the data flow, the data dependency graph comprising a plurality of nodes and data dependency edges corresponding to each node, generating a control flow graph based on the control flow, embedding the data dependency edges into the control flow graph, generating a hybrid dependency graph, performing code risk detection based on the hybrid dependency graph, and generating a code risk detection report.

This application discloses an automatic design method, device, medium, and product for complex special-purpose instructions (SPDIS) in hardware, relating to SPDIS hardware design, SPDIS instruction design, or instruction set extension. The method includes: analyzing the program to collect dynamic information; converting basic blocks in the program into a data dependency graph based on the compiler's intermediate representation and extracting seeds; using a genetic algorithm to obtain candidate programs that meet input / output constraints based on the seeds; using a specially designed cost model to score the candidate programs as a selection criterion; utilizing maximum weighted independent set analysis to overcome the overlap problem of candidate programs; and fusing the subgraphs corresponding to the SPDIS instructions to generate dedicated functional units, thus completing the automatic design of complex SPDIS instructions for hardware. This application can explore the similarities between basic blocks, discover common and frequently executed code segments in the program as SPDIS instructions; and the search can maximize the coverage of the target program, significantly accelerating the program.

The application provides a relational database backup recovery method and system based on big data, relates to the technical field of database backup recovery, and comprises the following steps: obtaining full backup data and incremental backup data of a relational database to obtain merged backup data; based on the features and access modes of the merged backup data, a multi-dimensional index structure is constructed, and data dependency graphs, classification results and hot and cold layering information are integrated into the multi-dimensional index; target backup data is decompressed to obtain recovered backup data, and the recovered backup data is intelligently checked by using data integrity constraint rules; if the intelligent checking is passed, the recovered backup data is imported into the relational database to complete data recovery at a specified time point; if the intelligent checking is not passed, intelligent data repair and completion are performed, and then verification, checking and import are performed again until the intelligent checking is passed.

The invention discloses a detection method, equipment, a medium and a product. The method comprises the steps that a knowledge graph is constructed, the knowledge graph comprises knowledge nodes and knowledge edges, the knowledge nodes comprise at least one of vulnerability types, attack modes, functions and variables, and the knowledge edges are used for representing the incidence relation between the connected knowledge nodes; a behavior graph is generated according to a control flow graph and a data dependency graph corresponding to a to-be-detected code, the behavior graph comprises behavior nodes and behavior edges, the behavior nodes comprise execution units in the application code, and the behavior edges are used for representing the incidence relation between the connected behavior nodes. The behavior edge comprises at least one of a calling relation, a data transmission relation and a control flow relation; and determining a detection result corresponding to the to-be-detected code based on the behavior graph and the knowledge graph, through the technical scheme of the invention, the security of the application can be improved.

This invention discloses an accelerated EPS calculation method, apparatus, server, and storage medium, comprising: calculating the ratio of edges to nodes in a directed acyclic graph of a circuit verification task; comparing the ratio with a ratio threshold, and when the ratio is less than the ratio threshold, performing data-level parallel processing using a SIMD instruction set; when the ratio is greater than the ratio threshold, decomposing the directed acyclic graph of the circuit verification task to obtain decomposed subgraphs until a subgraph constraint condition is met; calculating the subgraph ratio of edges to nodes in each decomposed subgraph, generating a task-level data dependency graph for the decomposed subgraphs based on dependencies; and comparing the subgraph ratio with a ratio threshold, and when the ratio is less than the ratio threshold, performing data-level parallel processing using a SIMD instruction set; otherwise, generating a subtask queue matching the computational processing unit based on the decomposed subgraphs and the task-level data dependency graph.

The invention discloses a code detection method and device, a storage medium and electronic equipment, and the method comprises the steps: obtaining a to-be-detected code, generating a syntax tree, determining a data flow and a control flow of the to-be-detected code based on the syntax tree, and generating a data dependence graph based on the data flow, the data dependence graph comprises a plurality of nodes and data dependence edges corresponding to the nodes, generating a control flow graph based on the control flow, embedding the data dependency edge into the control flow graph, generating a mixed dependency graph, performing code risk detection based on the mixed dependency graph, and generating a code risk detection report.

The application discloses a heuristic method suitable for instruction rearrangement of a multi-transmitter processor, comprising the following steps: in a data preparation stage, obtaining runtime parameters of each instruction according to instruction set information and functional unit information, obtaining a data dependency graph according to data dependency relationships between instructions, processing the data dependency graph to obtain transmission priorities of the instructions, and then in a simulation running stage, ensuring that the instruction transmission process meets constraint conditions of a parallel transmission instruction number, an available functional unit number and a pipeline function limit. The application also provides a device suitable for instruction rearrangement of the multi-transmitter processor. The application solves the problem of instruction parallel optimization caused by the performance decline of an application program due to the migration of assembly codes between different generations of a domestic Shenwei many-core processor, improves the program transplantation efficiency, and solves the adaptability problem of an application assembly program library to a dual-transmission pipeline microstructure compared with the prior art.

The present application relates to the technical field of data security, in particular to a legal consultation data access control method, comprising the following steps: identifying legal documents, evidence lists and party entities in each piece of legal consultation data as data nodes. The present application structures the reference relationship between data by identifying and constructing legal data dependency graph for legal documents, evidence lists and party entities, enables dynamic removal by layer-by-layer transmission along the directed link and combining the blocking marker in the authority transmission process, thereby avoiding unauthorized diffusion. By splitting the legal consultation data content into independent vocabulary units and combining global and local feature calculation node content scarcity cost score, the data value and access decision are directly associated, and the information budget quota comparison is introduced at the same time of authority judgment, ensuring that the access behavior not only meets the authority condition, but also meets the information cost constraint.

The invention relates to the technical field of data security, in particular to a legal consultation data access control method, which comprises the following steps of: identifying legal documents, evidence lists and parties entities in each piece of legal consultation data as data nodes; according to the method, the legal document, the evidence list and the party entity are identified, and the legal data dependency graph is constructed, so that the reference relationship among the data is subjected to structured expression, layer-by-layer transmission along the directed link can be realized in the permission transmission process, and dynamic elimination is realized in combination with the blocking mark, so that unauthorized diffusion is avoided. The legal consultation data content is divided into independent vocabulary units, global and local features are combined to calculate node content scarcity degree cost scores, data values are directly associated with access decisions, information budget quota comparison is introduced during permission judgment, and it is ensured that access behaviors not only meet permission conditions, but also meet information cost constraints.